Upload 22 files

.gitattributes

@@ -0,0 +1,12 @@
+DATA_iplm_final_05022026_update1.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_iplm_final_05022026.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_FINAL_CLEAN_pusat_15012026.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL190226.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL200226.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL200226%20%2B%20TORAJA%20UTARA.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALLL.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20IPLM%20FINAL_CLEAN_pusat.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA%20IPLM%20FINAL_CLEAN.xlsx filter=lfs diff=lfs merge=lfs -text
+File_IPLM_090226_All.xlsx filter=lfs diff=lfs merge=lfs -text
+IPLM_clean_manual_131225.xlsx filter=lfs diff=lfs merge=lfs -text

DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:259136a817e48feb2317cc10cbc9437f2e8ea77f2cbdf97e9a8f474216c3c3a8
+size 33861015

DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL190226.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d7c3b1a749bd7d7fd8140d3f2537d9c4d092b026ecac7d841fb82706ab1bb8f
+size 34881155

DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL200226%20%2B%20TORAJA%20UTARA.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f4a6490c233059db656e186586b54102f98cebb6137db1aa6804a4b3cdb8ba7
+size 34895620

DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALL200226.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6b16755600b16048075f8bb71cb669b53d570f96edbd7c46cc0a52cbf594e59
+size 34881277

DATA%20CLEAN%20GABUNGAN%20SANGGAH-TIDAK%20SANGGAH%20-%20ALLL.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:469f473a88e97921278293b7319b3f137ab3abf9021fdd9e3fbdc3840f4782c2
+size 33987216

DATA%20IPLM%20FINAL_CLEAN.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:173f08fad648cce8455a9ad4659002be4e49cdce8fca3f436f6d3b1f91aa0ad5
+size 29900129

DATA%20IPLM%20FINAL_CLEAN_pusat.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:822fcc274a633a7feb180eeadf2a5e9ab02963de7b8ea0156bc6cb4562e4a571
+size 30828517

DATA_IPLM_FINAL_CLEAN_pusat_15012026.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7705e1ec01b0458ac576a45cc2a76479780030949a7da618b1f1204ac6c2153
+size 30899631

DATA_iplm_final_05022026.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22ddfe02aef021d53925917326e482f1e9d7bd34336acb82cb8318f2b1c43965
+size 34441568

DATA_iplm_final_05022026_update1.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c49f9e919a8485787242bc7a6c4307afb90a399d092444e8c4a066546b510cd2
+size 36026291

File_IPLM_090226_All.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:259136a817e48feb2317cc10cbc9437f2e8ea77f2cbdf97e9a8f474216c3c3a8
+size 33861015

IPLM_clean_manual_131225.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5560e8f700d983ca8b0b13d188ef06c2dab1cae364a24870226afd9c72b5c1db
+size 21587518

README.md

@@ -0,0 +1,13 @@
+---
+title: AI Iplm
+emoji: 🌍
+colorFrom: green
+colorTo: red
+sdk: gradio
+sdk_version: 6.0.2
+app_file: app.py
+pinned: false
+license: bsd
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,1705 @@
+# -*- coding: utf-8 -*-
+"""
+IPLM 2025 — Final (Target Sampel 33.88% per Jenis) — TANPA Kinerja Relatif / Percentile
+UPDATE (sesuai instruksi terbaru Anda) — TANPA mengubah pipeline lain:
+
+FOKUS PEMBENAHAN (LLM + WORD):
+1) Nilai Kepatuhan, Koleksi, Tenaga, Kinerja, Pelayanan, Pengelolaan:
+   - TIDAK dikalikan 100.
+   - Ditulis APA ADANYA dari kolom agregat aplikasi:
+     Rata2_dim_kepatuhan, Rata2_sub_koleksi, Rata2_sub_sdm, Rata2_dim_kinerja,
+     Rata2_sub_pelayanan, Rata2_sub_pengelolaan.
+2) Nilai IPLM ditulis apa adanya: Indeks_Final_Wilayah_0_100.
+3) LLM mengisi Interpretasi & Rekomendasi:
+   - Interpretasi: deskriptif, kondisi riil berbasis relasi angka (lebih besar/kecil, gap, dominan, konsistensi),
+     plus pemaknaan substantif dimensi (koleksi/sdm/pelayanan/pengelolaan) TANPA label normatif.
+   - Rekomendasi: operasional, 2–3 butir ringkas, menaut ke pola angka (gap/ketimpangan/kontribusi).
+4) Di bawah tabel Word: tambah deskripsi jumlah perpustakaan sumber data (dari tabel agregat wilayah × jenis / “gambar 2”):
+   sekolah=..., umum=..., khusus=..., total=...
+
+Catatan penting:
+- Semua perhitungan dan dashboard tetap.
+- Yang diubah hanya: (a) cara mengambil nilai untuk tabel Word (tanpa *100),
+  (b) prompt LLM untuk isi interpretasi/rekomendasi agar nyambung dengan angka,
+  (c) tambahan paragraf jumlah perpustakaan di bawah tabel Word.
+"""
+
+import os
+import re
+import time
+import json
+import math
+import tempfile
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from sklearn.preprocessing import PowerTransformer
+
+# python-docx (wajib kalau mau Word)
+DOCX_AVAILABLE = True
+try:
+    from docx import Document
+    from docx.shared import Pt
+    from docx.oxml import OxmlElement
+    from docx.oxml.ns import qn
+except Exception:
+    DOCX_AVAILABLE = False
+    Document = None
+
+# huggingface client (opsional)
+HF_AVAILABLE = True
+try:
+    from huggingface_hub import InferenceClient
+except Exception:
+    HF_AVAILABLE = False
+    InferenceClient = None
+
+
+# ============================================================
+# 1) KONFIGURASI
+# ============================================================
+
+DATA_FILE   = os.getenv("DATA_FILE",   "DATA CLEAN GABUNGAN SANGGAH-TIDAK SANGGAH - ALL200226 + TORAJA UTARA.xlsx")
+POP_KAB     = os.getenv("POP_KAB",     "Data_populasi_Kab_kota_fixed.xlsx")
+POP_PROV    = os.getenv("POP_PROV",    "Data_populasi_propinsi.xlsx")
+POP_KHUSUS  = os.getenv("POP_KHUSUS",  "Data_populasi_perp_khusus.xlsx")
+
+W_KEPATUHAN = float(os.getenv("W_KEPATUHAN", "0.30"))
+W_KINERJA   = float(os.getenv("W_KINERJA",   "0.70"))
+
+TARGET_RATIO = float(os.getenv("TARGET_RATIO", "0.3388"))
+
+USE_LLM = True
+LLM_MODEL_NAME = os.getenv("LLM_MODEL_NAME", "meta-llama/Meta-Llama-3-8B-Instruct")
+HF_TOKEN = (
+    os.getenv("HF_SECRET")
+    or os.getenv("HF_TOKEN")
+    or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+    or os.getenv("HF_API_TOKEN")
+)
+
+
+# ============================================================
+# 2) UTIL
+# ============================================================
+
+def _mtime(path_str: str):
+    p = Path(path_str)
+    return p.stat().st_mtime if p.exists() else None
+
+def _canon(s: str) -> str:
+    return re.sub(r"[^a-z0-9]+", "", str(s).lower())
+
+def _disp_text(x):
+    if pd.isna(x):
+        return None
+    t = str(x).strip().upper()
+    return " ".join(t.split())
+
+def pick_col(df, candidates):
+    if df is None or df.empty:
+        return None
+    for c in candidates:
+        if c in df.columns:
+            return c
+    can_map = {_canon(c): c for c in df.columns}
+    for c in candidates:
+        k = _canon(c)
+        if k in can_map:
+            return can_map[k]
+    return None
+
+def coerce_num(val):
+    if pd.isna(val):
+        return np.nan
+    t = str(val).strip()
+    if t == "" or t in {"-", "–", "—", "NA", "N/A", "null", "NULL"}:
+        return np.nan
+    t = t.replace("\u00a0", " ").replace("Rp", "").replace("%", "")
+    t = re.sub(r"[^0-9,.\-]", "", t)
+
+    if t.count(".") > 1 and t.count(",") == 1:
+        t = t.replace(".", "").replace(",", ".")
+    elif t.count(",") > 1 and t.count(".") == 1:
+        t = t.replace(",", "")
+    elif t.count(",") == 1 and t.count(".") == 0:
+        t = t.replace(",", ".")
+    else:
+        t = t.replace(",", "")
+
+    try:
+        return float(t)
+    except Exception:
+        return np.nan
+
+def minmax_norm(s: pd.Series) -> pd.Series:
+    x = pd.to_numeric(s, errors="coerce").astype(float)
+    mn, mx = x.min(skipna=True), x.max(skipna=True)
+    if pd.isna(mn) or pd.isna(mx) or mx == mn:
+        return pd.Series(0.0, index=s.index)
+    return (x - mn) / (mx - mn)
+
+def norm_kew(v):
+    if pd.isna(v):
+        return None
+    t = str(v).strip().upper()
+    if "KAB" in t or "KOTA" in t:
+        return "KAB/KOTA"
+    if "PROV" in t:
+        return "PROVINSI"
+    if "PUSAT" in t or "NASIONAL" in t:
+        return "PUSAT"
+    return t
+
+def norm_prov_disp(s):
+    if pd.isna(s):
+        return None
+    t = str(s).strip().upper()
+    t = t.replace("\u00a0", " ")
+    t = " ".join(t.split())
+    t = t.replace("PROPINSI", "PROVINSI")
+    while t.startswith("PROVINSI PROVINSI "):
+        t = t.replace("PROVINSI PROVINSI ", "PROVINSI ", 1)
+    if t.startswith("PROVINSI "):
+        name = t[len("PROVINSI "):].strip()
+    else:
+        name = t
+    name = " ".join(name.split())
+    if not name:
+        return None
+    return f"PROVINSI {name}"
+
+def norm_prov_label(s):
+    if pd.isna(s):
+        return None
+    t = str(s).strip().upper().replace("\u00a0", " ")
+    t = " ".join(t.split())
+    t = t.replace("PROPINSI", "PROVINSI")
+    t = t.replace("PROVINSI", "").strip()
+    return re.sub(r"[^A-Z0-9]+", "", t)
+
+def norm_kab_label(s):
+    if pd.isna(s):
+        return None
+    t = str(s).upper()
+    t = t.replace("KABUPATEN", "KAB")
+    t = t.replace("KAB.", "KAB")
+    t = t.replace("KOTA ADMINISTRASI", "KOTA")
+    t = t.replace("KOTA ADM.", "KOTA")
+    t = t.replace("KOTA.", "KOTA")
+    t = " ".join(t.split())
+    return re.sub(r"[^A-Z0-9]+", "", t)
+
+def safe_div(num, den):
+    if den is None or pd.isna(den) or float(den) <= 0:
+        return np.nan
+    return float(num) / float(den)
+
+def faktor_penyesuaian_total(n_total: float, target_total: float) -> float:
+    if target_total is None or pd.isna(target_total) or float(target_total) <= 0:
+        return 1.0
+    if n_total is None or pd.isna(n_total) or float(n_total) < 0:
+        n_total = 0.0
+    return float(min(float(n_total) / float(target_total), 1.0))
+
+
+# ============================================================
+# 3) INDIKATOR IPLM
+# ============================================================
+
+koleksi_cols = [
+    "JudulTercetak","EksemplarTercetak","JudulElektronik","EksemplarElektronik",
+    "TambahJudulTercetak","TambahEksemplarTercetak",
+    "TambahJudulElektronik","TambahEksemplarElektronik",
+    "KomitmenAnggaranKoleksi"
+]
+sdm_cols = [
+    "TenagaKualifikasiIlmuPerpustakaan",
+    "TenagaFungsionalProfesional",
+    "TenagaPKB",
+    "AnggaranTenaga"
+]
+pelayanan_cols = [
+    "PesertaBudayaBaca","PemustakaLuringDaring","PemustakaFasilitasTIK",
+    "PemanfaatanJudulTercetak","PemanfaatanEksemplarTercetak",
+    "PemanfaatanJudulElektronik","PemanfaatanEksemplarElektronik"
+]
+pengelolaan_cols = [
+    "KegiatanBudayaBaca","KegiatanKerjasama","VariasiLayanan","Kebijakan","AnggaranLayanan"
+]
+all_indicators = koleksi_cols + sdm_cols + pelayanan_cols + pengelolaan_cols
+
+alias_map_raw = {
+    "j_judul_koleksi_tercetak": "JudulTercetak",
+    "j_eksemplar_koleksi_tercetak": "EksemplarTercetak",
+    "j_judul_koleksi_digital": "JudulElektronik",
+    "j_eksemplar_koleksi_digital": "EksemplarElektronik",
+    "tambah_judul_koleksi_tercetak": "TambahJudulTercetak",
+    "tambah_eksemplar_koleksi_tercetak": "TambahEksemplarTercetak",
+    "tambah_judul_koleksi_digital": "TambahJudulElektronik",
+    "tambah_eksemplar_koleksi_digital": "TambahEksemplarElektronik",
+    "j_anggaran_koleksi": "KomitmenAnggaranKoleksi",
+    "j_tenaga_ilmu_perpus": "TenagaKualifikasiIlmuPerpustakaan",
+    "j_tenaga_nonilmu_perpus": "TenagaFungsionalProfesional",
+    "j_tenaga_pkb": "TenagaPKB",
+    "j_anggaran_diklat_perpus": "AnggaranTenaga",
+    "j_peserta_budaya_baca": "PesertaBudayaBaca",
+    "j_pemustaka_luring_daring": "PemustakaLuringDaring",
+    "j_pemustaka_fasilitas_tik": "PemustakaFasilitasTIK",
+    "j_judul_koleksi_tercetak_termanfaat": "PemanfaatanJudulTercetak",
+    "j_eksemplar_koleksi_tercetak_termanfaat": "PemanfaatanEksemplarTercetak",
+    "j_judul_koleksi_digital_termanfaat": "PemanfaatanJudulElektronik",
+    "j_eksemplar_koleksi_digital_termanfaat": "PemanfaatanEksemplarElektronik",
+    "j_kegiatan_budaya_baca_peningkatan_literasi": "KegiatanBudayaBaca",
+    "j_kerjasama_pengembangan_perpus": "KegiatanKerjasama",
+    "j_variasi_layanan": "VariasiLayanan",
+    "j_kebijakan_prosedur_pelayanan": "Kebijakan",
+    "j_anggaran_peningkatan_pelayanan": "AnggaranLayanan",
+}
+alias_map = {_canon(k): v for k, v in alias_map_raw.items()}
+
+
+# ============================================================
+# 4) PIPELINE NASIONAL (LEVEL ENTITAS)
+# ============================================================
+
+def _mean_norm_cols(row, cols):
+    vals = []
+    for c in cols:
+        k = f"norm_{c}"
+        if k in row.index:
+            v = row[k]
+            if pd.isna(v):
+                v = 0.0
+            vals.append(float(v))
+    return float(np.mean(vals)) if vals else 0.0
+
+def prepare_global(df_src: pd.DataFrame) -> pd.DataFrame:
+    if df_src is None or df_src.empty:
+        return df_src
+
+    df = df_src.copy()
+
+    rename_map = {}
+    for col in df.columns:
+        c = _canon(col)
+        if c in alias_map:
+            rename_map[col] = alias_map[c]
+        else:
+            for tgt in all_indicators:
+                if c == _canon(tgt):
+                    rename_map[col] = tgt
+                    break
+    if rename_map:
+        df = df.rename(columns=rename_map)
+
+    available = [c for c in all_indicators if c in df.columns]
+    for c in available:
+        df[c] = df[c].apply(coerce_num)
+
+    for c in available:
+        x = pd.to_numeric(df[c], errors="coerce").astype(float).values
+        mask = ~np.isnan(x)
+        transformed = np.full_like(x, np.nan, dtype=float)
+        if mask.sum() > 1:
+            pt = PowerTransformer(method="yeo-johnson", standardize=False)
+            transformed[mask] = pt.fit_transform(x[mask].reshape(-1, 1)).ravel()
+        else:
+            transformed[mask] = x[mask]
+        df[f"norm_{c}"] = minmax_norm(pd.Series(transformed, index=df.index))
+
+    df["sub_koleksi"]     = df.apply(lambda r: _mean_norm_cols(r, [c for c in koleksi_cols if c in available]), axis=1)
+    df["sub_sdm"]         = df.apply(lambda r: _mean_norm_cols(r, [c for c in sdm_cols if c in available]), axis=1)
+    df["sub_pelayanan"]   = df.apply(lambda r: _mean_norm_cols(r, [c for c in pelayanan_cols if c in available]), axis=1)
+    df["sub_pengelolaan"] = df.apply(lambda r: _mean_norm_cols(r, [c for c in pengelolaan_cols if c in available]), axis=1)
+
+    df["dim_kepatuhan"] = df[["sub_koleksi","sub_sdm"]].mean(axis=1)
+    df["dim_kinerja"]   = df[["sub_pelayanan","sub_pengelolaan"]].mean(axis=1)
+
+    df["Indeks_Dasar_0_100"] = 100 * (W_KEPATUHAN * df["dim_kepatuhan"] + W_KINERJA * df["dim_kinerja"])
+
+    for c in ["sub_koleksi","sub_sdm","sub_pelayanan","sub_pengelolaan","dim_kepatuhan","dim_kinerja","Indeks_Dasar_0_100"]:
+        df[c] = pd.to_numeric(df[c], errors="coerce").fillna(0.0)
+
+    return df
+
+
+# ============================================================
+# 5) CACHE LOADER (NO UPLOAD)
+# ============================================================
+
+_CACHE = {"key": None, "df_all": None, "df_raw": None, "pop_kab": None, "pop_prov": None, "pop_khusus": None, "meta": None, "info": None}
+
+def _parse_pop_khusus(path_xlsx: str) -> pd.DataFrame:
+    df = pd.read_excel(path_xlsx)
+    if df is None or df.empty:
+        return pd.DataFrame()
+
+    c_mix = pick_col(df, [
+        "Propinsi/Kab/kota", "Propinsi/Kab/Kota", "Provinsi/Kab/Kota",
+        "Provinsi/Kab/kota", "Provinsi/Kabupaten/Kota",
+        "Wilayah", "Nama Wilayah"
+    ])
+    if c_mix is None:
+        raise ValueError("POP_KHUSUS: kolom gabungan Provinsi/Kab/Kota tidak ditemukan.")
+
+    c_pop = pick_col(df, ["POP_KHUSUS", "Pop_Khusus", "pop_khusus"])
+    if c_pop is None:
+        raise ValueError("POP_KHUSUS: kolom 'POP_KHUSUS' tidak ditemukan.")
+
+    mix = df[c_mix].astype(str).fillna("").str.strip()
+    pop_series = df[c_pop].apply(coerce_num)
+
+    rows = []
+    current_prov = None
+
+    for m, pval in zip(mix.tolist(), pop_series.tolist()):
+        mm = _disp_text(m) or ""
+        if mm == "":
+            continue
+
+        if mm.startswith("PROVINSI "):
+            prov_name = mm.replace("PROVINSI", "").strip()
+            current_prov = prov_name
+            rows.append({
+                "LEVEL": "PROV",
+                "Provinsi_Label": f"PROVINSI {prov_name}",
+                "Kab_Kota_Label": None,
+                "Pop_Total_Jenis": pval,
+            })
+            continue
+
+        rows.append({
+            "LEVEL": "KAB",
+            "Provinsi_Label": f"PROVINSI {current_prov}" if current_prov else None,
+            "Kab_Kota_Label": mm,
+            "Pop_Total_Jenis": pval,
+        })
+
+    pop = pd.DataFrame(rows)
+    if pop.empty:
+        return pop
+
+    pop["Pop_Total_Jenis"] = pd.to_numeric(pop["Pop_Total_Jenis"], errors="coerce").fillna(0.0)
+    pop["prov_key"] = pop["Provinsi_Label"].apply(norm_prov_label)
+    pop["kab_key"] = pop["Kab_Kota_Label"].apply(norm_kab_label) if "Kab_Kota_Label" in pop.columns else None
+    return pop
+
+def load_default_files(force=False):
+    key = (
+        DATA_FILE, POP_KAB, POP_PROV, POP_KHUSUS,
+        _mtime(DATA_FILE), _mtime(POP_KAB), _mtime(POP_PROV), _mtime(POP_KHUSUS)
+    )
+
+    if (not force) and _CACHE["key"] == key and _CACHE["df_all"] is not None:
+        return _CACHE["df_all"], _CACHE["df_raw"], _CACHE["pop_kab"], _CACHE["pop_prov"], _CACHE["pop_khusus"], _CACHE["meta"], _CACHE["info"]
+
+    for p, label in [(DATA_FILE, "DM"), (POP_KAB, "POP_KAB"), (POP_PROV, "POP_PROV"), (POP_KHUSUS, "POP_KHUSUS")]:
+        if not Path(p).exists():
+            info = f"File tidak ditemukan ({label}): {p}"
+            _CACHE.update({"key": key, "df_all": None, "df_raw": None, "pop_kab": None, "pop_prov": None, "pop_khusus": None, "meta": {}, "info": info})
+            return None, None, None, None, None, {}, info
+
+    fp = Path(DATA_FILE)
+    xls = pd.ExcelFile(fp)
+    frames = [pd.read_excel(fp, sheet_name=s) for s in xls.sheet_names]
+    df_raw = pd.concat(frames, ignore_index=True, sort=False)
+
+    prov_col  = pick_col(df_raw, ["provinsi", "Provinsi", "PROVINSI"])
+    kab_col   = pick_col(df_raw, ["kab/kota", "Kab/Kota", "Kab_Kota", "KAB/KOTA", "kabupaten_kota", "Kabupaten/Kota", "kabupaten kota", "kota", "kab_kota"])
+    kew_col   = pick_col(df_raw, ["kewenangan", "jenis_kewenangan", "Kewenangan", "KEWENANGAN"])
+    jenis_col = pick_col(df_raw, ["jenis_perpustakaan", "Jenis Perpustakaan", "JENIS_PERPUSTAKAAN"])
+    nama_col  = pick_col(df_raw, ["nm_perpustakaan","nama_perpustakaan","Nama Perpustakaan","nm_instansi_lembaga","nm_perpus"])
+
+    missing = []
+    if prov_col is None:  missing.append("Provinsi")
+    if kab_col is None:   missing.append("Kab/Kota")
+    if kew_col is None:   missing.append("Kewenangan")
+    if jenis_col is None: missing.append("Jenis Perpustakaan")
+    if missing:
+        info = f"Kolom wajib tidak ditemukan di DM: {', '.join(missing)}"
+        _CACHE.update({"key": key, "df_all": None, "df_raw": None, "pop_kab": None, "pop_prov": None, "pop_khusus": None, "meta": {}, "info": info})
+        return None, None, None, None, None, {}, info
+
+    val_map_jenis = {
+        "PERPUSTAKAAN SEKOLAH": "sekolah", "SEKOLAH": "sekolah",
+        "PERPUSTAKAAN UMUM": "umum", "UMUM": "umum", "PERPUSTAKAAN DAERAH": "umum",
+        "PERPUSTAKAAN KHUSUS": "khusus", "KHUSUS": "khusus",
+    }
+
+    df_raw["KEW_NORM"]  = df_raw[kew_col].apply(norm_kew)
+    df_raw["_dataset"]  = df_raw[jenis_col].astype(str).str.strip().str.upper().map(val_map_jenis)
+    df_raw["PROV_DISP"] = df_raw[prov_col].apply(norm_prov_disp)
+    df_raw["KAB_DISP"]  = df_raw[kab_col].apply(_disp_text)
+    df_raw["prov_key"]  = df_raw["PROV_DISP"].apply(norm_prov_label)
+    df_raw["kab_key"]   = df_raw["KAB_DISP"].apply(norm_kab_label)
+
+    if nama_col and nama_col in df_raw.columns:
+        kcols = [prov_col, kab_col, kew_col, jenis_col, nama_col]
+    else:
+        kcols = [prov_col, kab_col, kew_col, jenis_col]
+
+    tmp = df_raw[kcols].astype(str).fillna("").apply(lambda s: s.str.strip(), axis=0)
+    df_raw["_row_key"] = tmp.apply(lambda r: "||".join(r.values.tolist()), axis=1).apply(_canon)
+    before = len(df_raw)
+    df_raw = df_raw.drop_duplicates(subset=["_row_key"], keep="first").copy()
+    after = len(df_raw)
+
+    # POP KAB
+    pk = pd.read_excel(POP_KAB)
+    c_kab  = pick_col(pk, ["KABUPATEN_KOTA","Kab/Kota","Kabupaten/Kota","KAB/KOTA","Kabupaten_Kota","kab_kota","kabupaten_kota"])
+    c_prov = pick_col(pk, ["PROVINSI","Provinsi","provinsi"])
+    if c_kab is None:
+        info = "POP_KAB: wajib ada kolom Kab/Kota."
+        _CACHE.update({"key": key, "df_all": None, "df_raw": None, "pop_kab": None, "pop_prov": None, "pop_khusus": None, "meta": {}, "info": info})
+        return None, None, None, None, None, {}, info
+
+    pop_kab = pk.copy()
+    pop_kab["Kab_Kota_Label"] = pk[c_kab].astype(str).str.strip()
+    pop_kab["Provinsi_Label"] = pk[c_prov].astype(str).str.strip() if c_prov else ""
+    pop_kab["kab_key"] = pop_kab["Kab_Kota_Label"].apply(norm_kab_label)
+    pop_kab = pop_kab.groupby("kab_key", as_index=False).first()
+
+    # POP PROV
+    pp = pd.read_excel(POP_PROV)
+    c_pr = pick_col(pp, ["Provinsi","PROVINSI","provinsi","Propinsi","PROPINSI","propinsi"])
+    if c_pr is None:
+        info = "POP_PROV: wajib ada kolom Provinsi."
+        _CACHE.update({"key": key, "df_all": None, "df_raw": None, "pop_kab": None, "pop_prov": None, "pop_khusus": None, "meta": {}, "info": info})
+        return None, None, None, None, None, {}, info
+
+    pop_prov = pp.copy()
+    pop_prov["Provinsi_Label"] = pp[c_pr].astype(str).str.strip()
+    pop_prov["prov_key"] = pop_prov["Provinsi_Label"].apply(norm_prov_label)
+    pop_prov = pop_prov.groupby("prov_key", as_index=False).first()
+
+    # POP KHUSUS
+    try:
+        pop_khusus = _parse_pop_khusus(POP_KHUSUS)
+    except Exception as e:
+        info = f"POP_KHUSUS gagal dibaca: {repr(e)}"
+        _CACHE.update({"key": key, "df_all": None, "df_raw": None, "pop_kab": None, "pop_prov": None, "pop_khusus": None, "meta": {}, "info": info})
+        return None, None, None, None, None, {}, info
+
+    df_all = prepare_global(df_raw)
+    meta = dict(prov_col=prov_col, kab_col=kab_col, kew_col=kew_col, jenis_col=jenis_col, nama_col=nama_col)
+
+    info = (
+        f"Mode NO UPLOAD (cache aktif)\n"
+        f"DM: {fp.name} | Baris: {before} -> dedup: {after}\n"
+        f"POP_KAB: {Path(POP_KAB).name} (n={len(pop_kab)})\n"
+        f"POP_PROV: {Path(POP_PROV).name} (n={len(pop_prov)})\n"
+        f"POP_KHUSUS: {Path(POP_KHUSUS).name} (n={len(pop_khusus)})\n"
+        f"TARGET sampel per jenis: {TARGET_RATIO*100:.2f}%\n"
+        f"mtime: DM={time.ctime(_mtime(DATA_FILE))} | Kab={time.ctime(_mtime(POP_KAB))} | Prov={time.ctime(_mtime(POP_PROV))} | Khusus={time.ctime(_mtime(POP_KHUSUS))}"
+    )
+
+    _CACHE.update({
+        "key": key,
+        "df_all": df_all,
+        "df_raw": df_raw,
+        "pop_kab": pop_kab,
+        "pop_prov": pop_prov,
+        "pop_khusus": pop_khusus,
+        "meta": meta,
+        "info": info
+    })
+    return df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info
+
+
+# ============================================================
+# 6) FAKTOR WILAYAH — PER JENIS (TARGET 33.88%)
+# ============================================================
+
+def build_faktor_wilayah_jenis(df_filtered, pop_kab, pop_prov, pop_khusus, kew_value):
+    if df_filtered is None or df_filtered.empty:
+        return pd.DataFrame()
+
+    kew_norm = str(kew_value or "").upper()
+    df = df_filtered.copy()
+    df = df[df["_dataset"].isin(["sekolah", "umum", "khusus"])].copy()
+    if df.empty:
+        return pd.DataFrame()
+
+    jenis_list = ["sekolah", "umum", "khusus"]
+
+    if "PROV" in kew_norm:
+        key_col, label_col, label_name, mode = "prov_key", "PROV_DISP", "Provinsi", "PROV"
+        base_pop = pop_prov.copy() if (pop_prov is not None and not pop_prov.empty) else pd.DataFrame()
+        if not base_pop.empty and "prov_key" not in base_pop.columns:
+            base_pop["prov_key"] = base_pop["Provinsi_Label"].apply(norm_prov_label)
+        base_pop = base_pop.set_index("prov_key") if (not base_pop.empty and "prov_key" in base_pop.columns) else pd.DataFrame().set_index(pd.Index([]))
+    else:
+        key_col, label_col, label_name, mode = "kab_key", "KAB_DISP", "Kab/Kota", "KAB"
+        base_pop = pop_kab.copy() if (pop_kab is not None and not pop_kab.empty) else pd.DataFrame()
+        if not base_pop.empty and "kab_key" not in base_pop.columns:
+            base_pop["kab_key"] = base_pop["Kab_Kota_Label"].apply(norm_kab_label)
+        base_pop = base_pop.set_index("kab_key") if (not base_pop.empty and "kab_key" in base_pop.columns) else pd.DataFrame().set_index(pd.Index([]))
+
+    base_keys = df[[key_col, label_col]].drop_duplicates().rename(columns={key_col: "group_key", label_col: label_name})
+    full = base_keys.assign(_tmp=1).merge(pd.DataFrame({"Jenis": jenis_list, "_tmp": 1}), on="_tmp").drop(columns="_tmp")
+
+    cnt = (
+        df.groupby([key_col, label_col, "_dataset"], dropna=False)
+          .size()
+          .reset_index(name="n_jenis")
+          .rename(columns={key_col: "group_key", label_col: label_name, "_dataset": "Jenis"})
+    )
+    cnt["Jenis"] = cnt["Jenis"].astype(str).str.lower().str.strip()
+
+    base_n = full.merge(cnt, on=["group_key", label_name, "Jenis"], how="left")
+    base_n["n_jenis"] = pd.to_numeric(base_n["n_jenis"], errors="coerce").fillna(0).astype(int)
+
+    base_n["target_total_33_88_jenis"] = 0.0
+    base_n["pop_total_jenis"] = 0.0
+
+    if not base_pop.empty:
+        if mode == "KAB":
+            pop_sekolah = pd.to_numeric(base_pop.get("jumlah_populasi_sekolah_base", base_pop.get("jumlah_populasi_sekolah", 0)), errors="coerce").fillna(0.0)
+            pop_umum    = pd.to_numeric(base_pop.get("jumlah_populasi_umum_base", base_pop.get("jumlah_populasi_umum", 0)), errors="coerce").fillna(0.0)
+            tgt_sekolah = pop_sekolah * float(TARGET_RATIO)
+            tgt_umum    = pop_umum * float(TARGET_RATIO)
+        else:
+            sma = pd.to_numeric(base_pop.get("sma ", base_pop.get("sma", 0)), errors="coerce").fillna(0.0)
+            smk = pd.to_numeric(base_pop.get("smk", 0), errors="coerce").fillna(0.0)
+            slb = pd.to_numeric(base_pop.get("slb", 0), errors="coerce").fillna(0.0)
+            pop_sekolah = sma + smk + slb
+            tgt_sekolah = pop_sekolah * float(TARGET_RATIO)
+            pop_umum = pd.to_numeric(base_pop.get("perpus_umum_prop", 0), errors="coerce").fillna(0.0)
+            tgt_umum = pop_umum * float(TARGET_RATIO)
+
+        m = base_n["Jenis"].eq("sekolah")
+        base_n.loc[m, "pop_total_jenis"] = base_n.loc[m, "group_key"].map(pop_sekolah).fillna(0.0).values
+        base_n.loc[m, "target_total_33_88_jenis"] = base_n.loc[m, "group_key"].map(tgt_sekolah).fillna(0.0).values
+
+        m = base_n["Jenis"].eq("umum")
+        base_n.loc[m, "pop_total_jenis"] = base_n.loc[m, "group_key"].map(pop_umum).fillna(0.0).values
+        base_n.loc[m, "target_total_33_88_jenis"] = base_n.loc[m, "group_key"].map(tgt_umum).fillna(0.0).values
+
+    if pop_khusus is not None and not pop_khusus.empty:
+        pk = pop_khusus.copy()
+        pk["Pop_Total_Jenis"] = pd.to_numeric(pk.get("Pop_Total_Jenis", 0), errors="coerce").fillna(0.0)
+
+        if mode == "PROV":
+            pk_prov = pk[pk["LEVEL"].astype(str).str.upper() == "PROV"].copy()
+            pk_map = pk_prov.groupby("prov_key", as_index=True).agg(pop=("Pop_Total_Jenis", "sum"))
+            pop_series = pk_map["pop"]
+        else:
+            pk_kab = pk[pk["LEVEL"].astype(str).str.upper() == "KAB"].copy()
+            pk_map = pk_kab.groupby("kab_key", as_index=True).agg(pop=("Pop_Total_Jenis", "sum"))
+            pop_series = pk_map["pop"]
+
+        tgt_series = pop_series * float(TARGET_RATIO)
+
+        m = base_n["Jenis"].eq("khusus")
+        base_n.loc[m, "pop_total_jenis"] = base_n.loc[m, "group_key"].map(pop_series).fillna(0.0).values
+        base_n.loc[m, "target_total_33_88_jenis"] = base_n.loc[m, "group_key"].map(tgt_series).fillna(0.0).values
+
+    base_n["target_total_33_88_jenis"] = pd.to_numeric(base_n["target_total_33_88_jenis"], errors="coerce").fillna(0.0)
+    base_n["pop_total_jenis"] = pd.to_numeric(base_n["pop_total_jenis"], errors="coerce").fillna(0.0)
+
+    m_need_pop = (base_n["pop_total_jenis"] <= 0) & (base_n["target_total_33_88_jenis"] > 0)
+    base_n.loc[m_need_pop, "pop_total_jenis"] = base_n.loc[m_need_pop, "target_total_33_88_jenis"] / float(TARGET_RATIO)
+
+    base_n["faktor_penyesuaian_jenis"] = [
+        faktor_penyesuaian_total(n, t)
+        for n, t in zip(
+            pd.to_numeric(base_n["n_jenis"], errors="coerce").fillna(0).astype(float),
+            pd.to_numeric(base_n["target_total_33_88_jenis"], errors="coerce").fillna(0).astype(float),
+        )
+    ]
+
+    base_n["coverage_jenis_%"] = [
+        (safe_div(n, p) * 100.0) if (p is not None and not pd.isna(p) and float(p) > 0) else 0.0
+        for n, p in zip(
+            pd.to_numeric(base_n["n_jenis"], errors="coerce").fillna(0).astype(float),
+            pd.to_numeric(base_n["pop_total_jenis"], errors="coerce").fillna(0).astype(float),
+        )
+    ]
+
+    base_n["gap_target33_88_jenis"] = [
+        max(float(t) - float(n), 0.0)
+        for n, t in zip(
+            pd.to_numeric(base_n["n_jenis"], errors="coerce").fillna(0).astype(float),
+            pd.to_numeric(base_n["target_total_33_88_jenis"], errors="coerce").fillna(0).astype(float),
+        )
+    ]
+
+    base_n["target_total_33_88_jenis"] = pd.to_numeric(base_n["target_total_33_88_jenis"], errors="coerce").fillna(0).round(0).astype(int)
+    base_n["pop_total_jenis"]          = pd.to_numeric(base_n["pop_total_jenis"], errors="coerce").fillna(0).round(0).astype(int)
+    base_n["coverage_jenis_%"]         = pd.to_numeric(base_n["coverage_jenis_%"], errors="coerce").fillna(0.0).round(2)
+    base_n["faktor_penyesuaian_jenis"] = pd.to_numeric(base_n["faktor_penyesuaian_jenis"], errors="coerce").fillna(1.0).round(3)
+    base_n["gap_target33_88_jenis"]    = pd.to_numeric(base_n["gap_target33_88_jenis"], errors="coerce").fillna(0).round(0).astype(int)
+
+    return base_n
+
+
+# ============================================================
+# 7) AGREGAT WILAYAH × JENIS
+# ============================================================
+
+def build_agg_wilayah_jenis(df_filtered, faktor_wilayah_jenis, kew_value):
+    if df_filtered is None or df_filtered.empty:
+        return pd.DataFrame()
+
+    kew_norm = str(kew_value or "").upper()
+    df = df_filtered.copy()
+
+    if "PROV" in kew_norm:
+        key_col, label_col, label_name = "prov_key", "PROV_DISP", "Provinsi"
+    else:
+        key_col, label_col, label_name = "kab_key", "KAB_DISP", "Kab/Kota"
+
+    df = df[df["_dataset"].isin(["sekolah", "umum", "khusus"])].copy()
+    if df.empty:
+        return pd.DataFrame()
+
+    jenis_list = ["sekolah", "umum", "khusus"]
+
+    base_keys = df[[key_col, label_col]].drop_duplicates().rename(columns={key_col: "group_key", label_col: label_name})
+    full = base_keys.assign(_tmp=1).merge(pd.DataFrame({"Jenis": jenis_list, "_tmp": 1}), on="_tmp").drop(columns="_tmp")
+
+    agg_real = df.groupby([key_col, label_col, "_dataset"], dropna=False).agg(
+        Jumlah=("Indeks_Dasar_0_100", "size"),
+        Rata2_sub_koleksi=("sub_koleksi", "mean"),
+        Rata2_sub_sdm=("sub_sdm", "mean"),
+        Rata2_sub_pelayanan=("sub_pelayanan", "mean"),
+        Rata2_sub_pengelolaan=("sub_pengelolaan", "mean"),
+        Rata2_dim_kepatuhan=("dim_kepatuhan", "mean"),
+        Rata2_dim_kinerja=("dim_kinerja", "mean"),
+        Indeks_Dasar_Agregat_0_100=("Indeks_Dasar_0_100", "mean"),
+    ).reset_index().rename(columns={key_col: "group_key", label_col: label_name, "_dataset": "Jenis"})
+
+    agg_real["Jenis"] = agg_real["Jenis"].astype(str).str.lower().str.strip()
+
+    agg = full.merge(agg_real, on=["group_key", label_name, "Jenis"], how="left")
+    for c in ["Jumlah","Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
+              "Rata2_dim_kepatuhan","Rata2_dim_kinerja","Indeks_Dasar_Agregat_0_100"]:
+        if c in agg.columns:
+            agg[c] = pd.to_numeric(agg[c], errors="coerce").fillna(0.0)
+    agg["Jumlah"] = agg["Jumlah"].round(0).astype(int)
+
+    if faktor_wilayah_jenis is None or faktor_wilayah_jenis.empty:
+        agg["faktor_penyesuaian_jenis"] = 1.0
+    else:
+        fw = faktor_wilayah_jenis.copy()
+        fw["Jenis"] = fw["Jenis"].astype(str).str.lower().str.strip()
+        keep = ["group_key", label_name, "Jenis",
+                "faktor_penyesuaian_jenis", "target_total_33_88_jenis", "pop_total_jenis",
+                "coverage_jenis_%", "gap_target33_88_jenis", "n_jenis"]
+        fw = fw[[c for c in keep if c in fw.columns]].copy()
+        agg = agg.merge(fw, on=["group_key", label_name, "Jenis"], how="left")
+        agg["faktor_penyesuaian_jenis"] = pd.to_numeric(agg["faktor_penyesuaian_jenis"], errors="coerce").fillna(1.0)
+
+    agg["Indeks_Final_Agregat_0_100"] = (
+        pd.to_numeric(agg["Indeks_Dasar_Agregat_0_100"], errors="coerce").fillna(0.0)
+        * pd.to_numeric(agg["faktor_penyesuaian_jenis"], errors="coerce").fillna(1.0)
+    )
+
+    for c in [
+        "Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
+        "Rata2_dim_kepatuhan","Rata2_dim_kinerja"
+    ]:
+        if c in agg.columns:
+            agg[c] = pd.to_numeric(agg[c], errors="coerce").fillna(0.0).round(3)
+    for c in ["Indeks_Dasar_Agregat_0_100","Indeks_Final_Agregat_0_100"]:
+        if c in agg.columns:
+            agg[c] = pd.to_numeric(agg[c], errors="coerce").fillna(0.0).round(2)
+
+    agg["faktor_penyesuaian_jenis"] = pd.to_numeric(agg["faktor_penyesuaian_jenis"], errors="coerce").fillna(1.0).round(3)
+    return agg
+
+
+# ============================================================
+# 8) AGREGAT WILAYAH (KESELURUHAN) — avg3 dari 3 jenis
+# ============================================================
+
+def build_agg_wilayah_total_from_jenis(agg_jenis, faktor_wilayah_jenis, kew_value):
+    if agg_jenis is None or agg_jenis.empty:
+        return pd.DataFrame()
+
+    kew_norm = str(kew_value or "").upper()
+    label_name = "Provinsi" if "PROV" in kew_norm else "Kab/Kota"
+    jenis_list = ["sekolah", "umum", "khusus"]
+
+    a = agg_jenis.copy()
+    a["Jenis"] = a["Jenis"].astype(str).str.lower().str.strip()
+
+    base_keys = a[["group_key", label_name]].drop_duplicates()
+    full = base_keys.assign(_tmp=1).merge(pd.DataFrame({"Jenis": jenis_list, "_tmp": 1}), on="_tmp").drop(columns="_tmp")
+
+    cols_present = [c for c in [
+        "Jumlah",
+        "Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
+        "Rata2_dim_kepatuhan","Rata2_dim_kinerja",
+        "Indeks_Dasar_Agregat_0_100",
+        "Indeks_Final_Agregat_0_100",
+    ] if c in a.columns]
+
+    full = full.merge(a[["group_key", label_name, "Jenis"] + cols_present],
+                      on=["group_key", label_name, "Jenis"], how="left")
+    for c in cols_present:
+        full[c] = pd.to_numeric(full[c], errors="coerce").fillna(0.0)
+
+    out = full.groupby(["group_key", label_name], as_index=False).agg(
+        n_total=("Jumlah", "sum"),
+        Rata2_sub_koleksi=("Rata2_sub_koleksi", "mean"),
+        Rata2_sub_sdm=("Rata2_sub_sdm", "mean"),
+        Rata2_sub_pelayanan=("Rata2_sub_pelayanan", "mean"),
+        Rata2_sub_pengelolaan=("Rata2_sub_pengelolaan", "mean"),
+        Rata2_dim_kepatuhan=("Rata2_dim_kepatuhan", "mean"),
+        Rata2_dim_kinerja=("Rata2_dim_kinerja", "mean"),
+        Indeks_Dasar_Agregat_0_100=("Indeks_Dasar_Agregat_0_100", "mean"),
+        Indeks_Final_Wilayah_0_100=("Indeks_Final_Agregat_0_100", "mean"),
+    )
+
+    for c in ["Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan","Rata2_dim_kepatuhan","Rata2_dim_kinerja"]:
+        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(3)
+    for c in ["Indeks_Dasar_Agregat_0_100","Indeks_Final_Wilayah_0_100"]:
+        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
+    out["n_total"] = pd.to_numeric(out["n_total"], errors="coerce").fillna(0).round(0).astype(int)
+
+    return out
+
+
+# ============================================================
+# 9) SUMMARY (PER JENIS) + KESELURUHAN
+# ============================================================
+
+def build_summary_per_jenis(agg_jenis, agg_total):
+    jenis_list = ["sekolah", "umum", "khusus"]
+
+    def _row_default(jenis):
+        return {
+            "Jenis": jenis,
+            "Jumlah_Wilayah": 0,
+            "Total_Perpus": 0,
+            "Pop_Total_Jenis": 0,
+            "Target33_88_Total_Jenis": 0,
+            "Terkumpul_Jenis": 0,
+            "Coverage_Target33_88_Jenis_%": 0.0,
+            "Indeks_Dasar_0_100": 0.0,
+            "Indeks_Final_Disesuaikan_0_100": 0.0,
+            "Penyesuaian_Poin": 0.0,
+        }
+
+    rows_by_jenis = {j: _row_default(j) for j in jenis_list}
+
+    if agg_jenis is not None and not agg_jenis.empty:
+        a = agg_jenis.copy()
+        a["Jenis"] = a["Jenis"].astype(str).str.lower().str.strip()
+        for c in ["Jumlah","Indeks_Dasar_Agregat_0_100","Indeks_Final_Agregat_0_100","pop_total_jenis","target_total_33_88_jenis"]:
+            if c in a.columns:
+                a[c] = pd.to_numeric(a[c], errors="coerce").fillna(0)
+
+        for jenis in jenis_list:
+            sub = a[a["Jenis"] == jenis].copy()
+            if sub.empty:
+                continue
+
+            jumlah_wilayah = int(sub.shape[0])
+            terkumpul = int(pd.to_numeric(sub.get("Jumlah", 0), errors="coerce").fillna(0).sum())
+            pop_total = int(pd.to_numeric(sub.get("pop_total_jenis", 0), errors="coerce").fillna(0).sum())
+            target3388 = int(pd.to_numeric(sub.get("target_total_33_88_jenis", 0), errors="coerce").fillna(0).sum())
+
+            coverage = (terkumpul / target3388 * 100.0) if target3388 > 0 else 0.0
+            dasar = float(pd.to_numeric(sub.get("Indeks_Dasar_Agregat_0_100", 0), errors="coerce").fillna(0).mean())
+            final = float(pd.to_numeric(sub.get("Indeks_Final_Agregat_0_100", 0), errors="coerce").fillna(0).mean())
+
+            rows_by_jenis[jenis] = {
+                "Jenis": jenis,
+                "Jumlah_Wilayah": jumlah_wilayah,
+                "Total_Perpus": terkumpul,
+                "Pop_Total_Jenis": pop_total,
+                "Target33_88_Total_Jenis": target3388,
+                "Terkumpul_Jenis": terkumpul,
+                "Coverage_Target33_88_Jenis_%": float(coverage),
+                "Indeks_Dasar_0_100": float(dasar),
+                "Indeks_Final_Disesuaikan_0_100": float(final),
+                "Penyesuaian_Poin": float(final - dasar),
+            }
+
+    rows = [rows_by_jenis[j] for j in jenis_list]
+
+    dasar_all = (rows_by_jenis["sekolah"]["Indeks_Dasar_0_100"]
+                 + rows_by_jenis["umum"]["Indeks_Dasar_0_100"]
+                 + rows_by_jenis["khusus"]["Indeks_Dasar_0_100"]) / 3.0
+
+    final_all = (rows_by_jenis["sekolah"]["Indeks_Final_Disesuaikan_0_100"]
+                 + rows_by_jenis["umum"]["Indeks_Final_Disesuaikan_0_100"]
+                 + rows_by_jenis["khusus"]["Indeks_Final_Disesuaikan_0_100"]) / 3.0
+
+    pop_all = int(rows_by_jenis["sekolah"]["Pop_Total_Jenis"]
+                  + rows_by_jenis["umum"]["Pop_Total_Jenis"]
+                  + rows_by_jenis["khusus"]["Pop_Total_Jenis"])
+
+    target_all = int(rows_by_jenis["sekolah"]["Target33_88_Total_Jenis"]
+                     + rows_by_jenis["umum"]["Target33_88_Total_Jenis"]
+                     + rows_by_jenis["khusus"]["Target33_88_Total_Jenis"])
+
+    terkumpul_all = int(rows_by_jenis["sekolah"]["Terkumpul_Jenis"]
+                        + rows_by_jenis["umum"]["Terkumpul_Jenis"]
+                        + rows_by_jenis["khusus"]["Terkumpul_Jenis"])
+
+    coverage_all = (terkumpul_all / target_all * 100.0) if target_all > 0 else 0.0
+
+    jumlah_wilayah_all = int(agg_total.shape[0]) if (agg_total is not None and not agg_total.empty) else int(
+        max(rows_by_jenis["sekolah"]["Jumlah_Wilayah"],
+            rows_by_jenis["umum"]["Jumlah_Wilayah"],
+            rows_by_jenis["khusus"]["Jumlah_Wilayah"])
+    )
+
+    rows.append({
+        "Jenis": "keseluruhan",
+        "Jumlah_Wilayah": jumlah_wilayah_all,
+        "Total_Perpus": terkumpul_all,
+        "Pop_Total_Jenis": pop_all,
+        "Target33_88_Total_Jenis": target_all,
+        "Terkumpul_Jenis": terkumpul_all,
+        "Coverage_Target33_88_Jenis_%": float(coverage_all),
+        "Indeks_Dasar_0_100": float(dasar_all),
+        "Indeks_Final_Disesuaikan_0_100": float(final_all),
+        "Penyesuaian_Poin": float(final_all - dasar_all),
+    })
+
+    out = pd.DataFrame(rows)
+    for c in ["Jumlah_Wilayah","Total_Perpus","Pop_Total_Jenis","Target33_88_Total_Jenis","Terkumpul_Jenis"]:
+        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0).round(0).astype(int)
+    for c in ["Coverage_Target33_88_Jenis_%","Indeks_Dasar_0_100","Indeks_Final_Disesuaikan_0_100","Penyesuaian_Poin"]:
+        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
+    return out
+
+
+# ============================================================
+# 10) DETAIL ENTITAS (Final menempel dari wilayah)
+# ============================================================
+
+def attach_final_to_detail(df_filtered, agg_total, meta, kew_value):
+    if df_filtered is None or df_filtered.empty:
+        return pd.DataFrame()
+
+    kew_norm = str(kew_value or "").upper()
+    df = df_filtered.copy()
+
+    if "PROV" in kew_norm:
+        key_col = "prov_key"
+        label_cols = ("PROV_DISP", "KAB_DISP")
+    else:
+        key_col = "kab_key"
+        label_cols = ("PROV_DISP", "KAB_DISP")
+
+    if agg_total is None or agg_total.empty:
+        df["Indeks_Final_0_100"] = df["Indeks_Dasar_0_100"]
+    else:
+        m = agg_total[["group_key", "Indeks_Final_Wilayah_0_100"]].copy()
+        df = df.merge(m, left_on=key_col, right_on="group_key", how="left")
+        df["Indeks_Final_0_100"] = df["Indeks_Final_Wilayah_0_100"].fillna(df["Indeks_Dasar_0_100"])
+        df = df.drop(columns=[c for c in ["group_key","Indeks_Final_Wilayah_0_100"] if c in df.columns])
+
+    base_cols = [label_cols[0], label_cols[1], "KEW_NORM", "_dataset"]
+    if meta.get("nama_col") and meta["nama_col"] in df.columns:
+        df["nm_perpustakaan"] = df[meta["nama_col"]].astype(str)
+        base_cols.insert(2, "nm_perpustakaan")
+
+    keep = base_cols + [
+        "sub_koleksi","sub_sdm","sub_pelayanan","sub_pengelolaan",
+        "dim_kepatuhan","dim_kinerja",
+        "Indeks_Dasar_0_100",
+        "Indeks_Final_0_100",
+    ]
+    keep = [c for c in keep if c in df.columns]
+
+    out = df[keep].copy()
+    out = out.rename(columns={label_cols[0]:"Provinsi", label_cols[1]:"Kab/Kota", "_dataset":"Jenis"})
+
+    for c in ["sub_koleksi","sub_sdm","sub_pelayanan","sub_pengelolaan","dim_kepatuhan","dim_kinerja"]:
+        if c in out.columns:
+            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(3)
+    for c in ["Indeks_Dasar_0_100","Indeks_Final_0_100"]:
+        if c in out.columns:
+            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
+    return out
+
+
+# ============================================================
+# 11) VERIF (kecukupan sampel)
+# ============================================================
+
+def build_verif_jenis(faktor_wilayah_jenis, kew_value):
+    if faktor_wilayah_jenis is None or faktor_wilayah_jenis.empty:
+        return pd.DataFrame()
+
+    kew_norm = str(kew_value or "").upper()
+    label_col = "Provinsi" if "PROV" in kew_norm else "Kab/Kota"
+
+    out = faktor_wilayah_jenis.copy()
+    keep = [c for c in [
+        label_col, "Jenis",
+        "pop_total_jenis", "target_total_33_88_jenis", "n_jenis",
+        "coverage_jenis_%", "faktor_penyesuaian_jenis", "gap_target33_88_jenis"
+    ] if c in out.columns]
+
+    out = out[keep].copy()
+
+    for c in ["pop_total_jenis", "target_total_33_88_jenis", "n_jenis", "gap_target33_88_jenis"]:
+        if c in out.columns:
+            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0).round(0).astype(int)
+    if "coverage_jenis_%" in out.columns:
+        out["coverage_jenis_%"] = pd.to_numeric(out["coverage_jenis_%"], errors="coerce").fillna(0.0).round(2)
+    if "faktor_penyesuaian_jenis" in out.columns:
+        out["faktor_penyesuaian_jenis"] = pd.to_numeric(out["faktor_penyesuaian_jenis"], errors="coerce").fillna(1.0).round(3)
+
+    return out
+
+
+# ============================================================
+# 12) BELL CURVE — Indeks Dasar per Entitas (per Jenis) + Hover
+# ============================================================
+
+def _make_bell_curve_entitas(dfp, title, xcol="Indeks_Dasar_0_100", label_col="nm_perpustakaan", hover_cols=None, min_points=2):
+    fig = go.Figure()
+    fig.update_layout(
+        title=title,
+        xaxis_title="Skor (0–100)",
+        yaxis_title="Kepadatan",
+        hovermode="closest",
+        margin=dict(l=40, r=20, t=60, b=40),
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0),
+    )
+
+    if dfp is None or dfp.empty or xcol not in dfp.columns:
+        fig.add_annotation(text="Tidak ada data untuk ditampilkan.", x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False)
+        fig.update_xaxes(range=[0, 100])
+        fig.update_yaxes(rangemode="tozero")
+        return fig
+
+    d = dfp.dropna(subset=[xcol]).copy()
+    if d.empty:
+        fig.add_annotation(text="Tidak ada data untuk ditampilkan.", x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False)
+        fig.update_xaxes(range=[0, 100])
+        fig.update_yaxes(rangemode="tozero")
+        return fig
+
+    x = pd.to_numeric(d[xcol], errors="coerce").astype(float)
+    d = d.loc[x.notna()].copy()
+    x = x.loc[x.notna()].values
+    if len(x) < 1:
+        fig.add_annotation(text="Tidak ada data untuk ditampilkan.", x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False)
+        fig.update_xaxes(range=[0, 100])
+        fig.update_yaxes(rangemode="tozero")
+        return fig
+
+    hover_cols = hover_cols or []
+    def _val(row, col):
+        if col not in row.index:
+            return ""
+        v = row[col]
+        return "" if pd.isna(v) else str(v)
+
+    hover_text = []
+    for _, row in d.iterrows():
+        lines = []
+        nm = _val(row, label_col) if (label_col and label_col in d.columns) else ""
+        if nm:
+            lines.append(f"<b>{nm}</b>")
+        lines.append(f"{xcol}: {float(pd.to_numeric(row[xcol], errors='coerce')):.2f}")
+        for hc in hover_cols:
+            vv = _val(row, hc)
+            if vv:
+                lines.append(f"{hc}: {vv}")
+        hover_text.append("<br>".join(lines))
+
+    if len(x) < min_points:
+        x_single = float(x[0])
+        fig.add_trace(go.Scatter(
+            x=[x_single], y=[0],
+            mode="markers", showlegend=False,
+            hovertext=[hover_text[0]] if hover_text else None,
+            hoverinfo="text"
+        ))
+        fig.add_vline(x=x_single, line_width=1, line_dash="dash", annotation_text=f"Nilai: {x_single:.1f}", annotation_position="top")
+        fig.update_xaxes(range=[0, 100])
+        fig.update_yaxes(rangemode="tozero")
+        return fig
+
+    mu = float(np.mean(x))
+    sigma = float(np.std(x, ddof=1)) if len(x) > 1 else 1.0
+    sigma = max(sigma, 1e-3)
+
+    xmin = max(0.0, float(np.min(x)) - 5.0)
+    xmax = min(100.0, float(np.max(x)) + 5.0)
+    xs = np.linspace(xmin, xmax, 250)
+    pdf = (1.0 / (sigma * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xs - mu) / sigma) ** 2)
+
+    fig.add_trace(go.Scatter(x=xs, y=pdf, mode="lines", name="Kurva Normal (fit)"))
+    fig.add_trace(go.Scatter(
+        x=x, y=np.zeros_like(x),
+        mode="markers", showlegend=False,
+        hovertext=hover_text if hover_text else None,
+        hoverinfo="text"
+    ))
+
+    q1, q2, q3 = np.percentile(x, [25, 50, 75])
+    for xv, lab in [(q1, "Q1"), (q2, "Q2 (Median)"), (q3, "Q3"), (mu, "Mean")]:
+        fig.add_vline(x=float(xv), line_width=1, line_dash="dash", annotation_text=f"{lab}: {xv:.1f}", annotation_position="top")
+
+    fig.update_xaxes(range=[0, 100])
+    fig.update_yaxes(rangemode="tozero")
+    return fig
+
+
+# ============================================================
+# 13) KPI DASHBOARD (2 kartu: final + dasar)
+# ============================================================
+
+def _safe_first(df, col, default=0.0, where=None):
+    if df is None or df.empty or col not in df.columns:
+        return default
+    sub = df
+    if where is not None:
+        sub = df.loc[where]
+    if sub is None or sub.empty:
+        return default
+    return float(pd.to_numeric(sub[col], errors="coerce").fillna(default).iloc[0])
+
+def build_kpi_markdown(summary_jenis):
+    if summary_jenis is None or summary_jenis.empty:
+        return ""
+    final_all = _safe_first(summary_jenis, "Indeks_Final_Disesuaikan_0_100", 0.0, where=summary_jenis["Jenis"].astype(str).str.lower().eq("keseluruhan"))
+    dasar_all = _safe_first(summary_jenis, "Indeks_Dasar_0_100", 0.0, where=summary_jenis["Jenis"].astype(str).str.lower().eq("keseluruhan"))
+
+    def fmt(x, nd=2):
+        return "NA" if pd.isna(x) else f"{x:.{nd}f}"
+
+    return f"""
+<div style="display:flex; gap:12px; flex-wrap:wrap;">
+  <div style="border:1px solid #333; border-radius:10px; padding:10px 12px; min-width:260px;">
+    <div style="opacity:0.8;">Indeks IPLM FINAL (Disesuaikan 33.88%)</div>
+    <div style="font-size:26px; font-weight:700;">{fmt(final_all,2)}</div>
+    <div style="opacity:0.7;">Skor absolut (untuk akuntabilitas)</div>
+  </div>
+
+  <div style="border:1px solid #333; border-radius:10px; padding:10px 12px; min-width:260px;">
+    <div style="opacity:0.8;">Indeks Dasar (Tanpa Penyesuaian)</div>
+    <div style="font-size:26px; font-weight:700;">{fmt(dasar_all,2)}</div>
+    <div style="opacity:0.7;">Sebelum faktor kecukupan sampel</div>
+  </div>
+</div>
+""".strip()
+
+
+# ============================================================
+# 14) LLM: Isi Interpretasi & Rekomendasi (TABEL) + WORD
+# ============================================================
+
+_HF_CLIENT = None
+
+def get_llm_client():
+    global _HF_CLIENT
+    if _HF_CLIENT is not None:
+        return _HF_CLIENT
+    if (not HF_AVAILABLE) or (InferenceClient is None):
+        _HF_CLIENT = None
+        return None
+    try:
+        _HF_CLIENT = InferenceClient(model=LLM_MODEL_NAME, token=HF_TOKEN) if HF_TOKEN else InferenceClient(model=LLM_MODEL_NAME)
+        return _HF_CLIENT
+    except Exception:
+        _HF_CLIENT = None
+        return None
+
+def _to_float(x, default=0.0):
+    try:
+        if x is None:
+            return float(default)
+        if isinstance(x, float) and math.isnan(x):
+            return float(default)
+        return float(x)
+    except Exception:
+        return float(default)
+
+def summarize_jumlah_perpus_dari_agg_jenis(agg_jenis_full, wilayah_label, kew_value):
+    """
+    Ambil jumlah perpustakaan sumber data dari tabel agregat wilayah × jenis (gambar 2).
+    Untuk filter 1 wilayah (kab/prov), agg_jenis_full biasanya 3 baris (sekolah/umum/khusus).
+    Untuk nasional/semua wilayah, ini akan menjumlahkan seluruh wilayah per jenis.
+    """
+    if agg_jenis_full is None or agg_jenis_full.empty:
+        return {"sekolah": 0, "umum": 0, "khusus": 0, "total": 0}
+
+    a = agg_jenis_full.copy()
+    if "Jenis" not in a.columns:
+        return {"sekolah": 0, "umum": 0, "khusus": 0, "total": 0}
+
+    a["Jenis"] = a["Jenis"].astype(str).str.lower().str.strip()
+    if "Jumlah" in a.columns:
+        a["Jumlah"] = pd.to_numeric(a["Jumlah"], errors="coerce").fillna(0).astype(int)
+    else:
+        a["Jumlah"] = 0
+
+    out = {}
+    for j in ["sekolah", "umum", "khusus"]:
+        out[j] = int(a.loc[a["Jenis"].eq(j), "Jumlah"].sum())
+    out["total"] = int(out["sekolah"] + out["umum"] + out["khusus"])
+    return out
+
+def build_interpretasi_table_values(agg_total, wilayah_label, target_ratio):
+    """
+    MENGAMBIL NILAI APA ADANYA (tanpa *100) dari hasil aplikasi (agg_total):
+    - Kepatuhan = Rata2_dim_kepatuhan
+    - Koleksi   = Rata2_sub_koleksi
+    - Tenaga    = Rata2_sub_sdm
+    - Kinerja   = Rata2_dim_kinerja
+    - Pelayanan = Rata2_sub_pelayanan
+    - Pengelolaan = Rata2_sub_pengelolaan
+    - Nilai IPLM = Indeks_Final_Wilayah_0_100
+
+    Jika agg_total > 1 baris (mis. nasional), diambil mean kolom-kolom tersebut.
+    """
+    if agg_total is None or agg_total.empty:
+        base = {
+            "kepatuhan": 0.0, "koleksi": 0.0, "tenaga": 0.0,
+            "kinerja": 0.0, "pelayanan": 0.0, "pengelolaan": 0.0,
+            "iplm": 0.0
+        }
+    else:
+        a = agg_total.copy()
+        cols_needed = [
+            "Rata2_dim_kepatuhan",
+            "Rata2_sub_koleksi",
+            "Rata2_sub_sdm",
+            "Rata2_dim_kinerja",
+            "Rata2_sub_pelayanan",
+            "Rata2_sub_pengelolaan",
+            "Indeks_Final_Wilayah_0_100",
+        ]
+        for c in cols_needed:
+            if c in a.columns:
+                a[c] = pd.to_numeric(a[c], errors="coerce").fillna(0.0)
+            else:
+                a[c] = 0.0
+
+        base = {
+            "kepatuhan": float(a["Rata2_dim_kepatuhan"].mean()),
+            "koleksi": float(a["Rata2_sub_koleksi"].mean()),
+            "tenaga": float(a["Rata2_sub_sdm"].mean()),
+            "kinerja": float(a["Rata2_dim_kinerja"].mean()),
+            "pelayanan": float(a["Rata2_sub_pelayanan"].mean()),
+            "pengelolaan": float(a["Rata2_sub_pengelolaan"].mean()),
+            "iplm": float(a["Indeks_Final_Wilayah_0_100"].mean()),
+        }
+
+    # pembulatan display (nilai tetap "apa adanya", hanya format)
+    # untuk sub/dim (0–1) biasanya 3 desimal; untuk IPLM (0–100) 2 desimal.
+    base_disp = {
+        "kepatuhan": round(_to_float(base["kepatuhan"]), 3),
+        "koleksi": round(_to_float(base["koleksi"]), 3),
+        "tenaga": round(_to_float(base["tenaga"]), 3),
+        "kinerja": round(_to_float(base["kinerja"]), 3),
+        "pelayanan": round(_to_float(base["pelayanan"]), 3),
+        "pengelolaan": round(_to_float(base["pengelolaan"]), 3),
+        "iplm": round(_to_float(base["iplm"]), 2),
+    }
+
+    rows = [
+        {"No":"1",   "Dimensi":"Kepatuhan", "Nilai":base_disp["kepatuhan"], "SumberKolom":"Rata2_dim_kepatuhan"},
+        {"No":"1.1", "Dimensi":"Variabel Koleksi", "Nilai":base_disp["koleksi"], "SumberKolom":"Rata2_sub_koleksi"},
+        {"No":"1.2", "Dimensi":"Variabel Tenaga Perpustakaan", "Nilai":base_disp["tenaga"], "SumberKolom":"Rata2_sub_sdm"},
+        {"No":"2",   "Dimensi":"Kinerja", "Nilai":base_disp["kinerja"], "SumberKolom":"Rata2_dim_kinerja"},
+        {"No":"2.1", "Dimensi":"Variabel Pelayanan", "Nilai":base_disp["pelayanan"], "SumberKolom":"Rata2_sub_pelayanan"},
+        {"No":"2.2", "Dimensi":"Variabel Penyelenggaraan/Pengelolaan", "Nilai":base_disp["pengelolaan"], "SumberKolom":"Rata2_sub_pengelolaan"},
+        {"No":"4",   "Dimensi":"Nilai IPLM", "Nilai":base_disp["iplm"], "SumberKolom":"Indeks_Final_Wilayah_0_100"},
+    ]
+
+    header = {
+        "judul": f"Interpretasi dan Rekomendasi IPLM — {wilayah_label}",
+        "target_sampel": f"{target_ratio*100:.2f}%"
+    }
+    return header, rows
+
+def llm_fill_interpretasi_rekomendasi(header, rows, wilayah_label, kew_label, jumlah_perpus_by_jenis):
+    """
+    LLM diminta mengisi kolom Interpretasi dan Rekomendasi dengan narasi yang NYAMBUNG ke angka:
+    - Interpretasi: jelaskan apa arti angka untuk kondisi operasional perpustakaan (koleksi/sdm/pelayanan/pengelolaan),
+      memakai relasi angka antardimensi (lebih besar/kecil, selisih, dominan, gap, konsistensi) TANPA label normatif.
+    - Rekomendasi: 2–3 aksi teknis per baris yang langsung meng-address pola angka (misal dimensi lebih kecil → prioritas aktivitas),
+      serta mengaitkan dengan volume data (jumlah perpustakaan per jenis) bila relevan.
+    Output wajib JSON.
+    """
+    client = get_llm_client()
+    if client is None or (not USE_LLM):
+        out = []
+        for r in rows:
+            out.append({k: r.get(k) for k in ["No","Dimensi","Nilai"]} | {"Interpretasi":"", "Rekomendasi":""})
+        return out, "LLM tidak digunakan / tidak tersedia."
+
+    payload = {
+        "wilayah": wilayah_label,
+        "kewenangan": kew_label,
+        "target_sampel_per_jenis": header["target_sampel"],
+        "jumlah_perpustakaan_sumber_data": jumlah_perpus_by_jenis,
+        "catatan_skala": (
+            "Baris Kepatuhan/Koleksi/Tenaga/Kinerja/Pelayanan/Pengelolaan memakai nilai agregat 'apa adanya' "
+            "(umumnya rentang 0–1 karena berasal dari sub/dim hasil normalisasi). "
+            "Baris 'Nilai IPLM' memakai Indeks_Final_Wilayah_0_100 (rentang 0–100)."
+        ),
+        "baris": rows
+    }
+
+    system = (
+        "Anda adalah analis kebijakan perpustakaan di Indonesia.\n"
+        "Tugas: isi kolom Interpretasi dan Rekomendasi untuk setiap baris tabel.\n"
+        "ATURAN WAJIB:\n"
+        "1) Jangan mengubah nilai angka. Jangan menghitung ulang skor.\n"
+        "2) Netral-deskriptif: dilarang memakai label normatif seperti baik/buruk, tinggi/sedang/rendah, memuaskan/kurang, optimal/tidak optimal.\n"
+        "3) Interpretasi harus nyambung langsung ke angka dan relasinya antardimensi: gunakan istilah lebih besar/kecil, selisih, gap, dominan, konsisten/tidak konsisten, kontribusi, proporsi.\n"
+        "4) Interpretasi juga harus menjelaskan kondisi riil berbasis dimensi:\n"
+        "   - Koleksi: pengembangan, ketersediaan, pemanfaatan koleksi (sebagai fungsi layanan),\n"
+        "   - Tenaga: kecukupan/kapasitas SDM dan pengembangan kompetensi,\n"
+        "   - Pelayanan: aktivitas layanan dan pemanfaatan layanan,\n"
+        "   - Pengelolaan: tata kelola, kebijakan, kolaborasi, dukungan anggaran layanan,\n"
+        "   - Kepatuhan = gabungan koleksi+tenaga; Kinerja = gabungan pelayanan+pengelolaan.\n"
+        "   Jelaskan tanpa menghakimi; fokus pada apa yang angka itu representasikan.\n"
+        "5) Rekomendasi harus operasional dan spesifik (2–3 butir singkat) untuk tiap baris. Gunakan pola angka untuk menurunkan aksi.\n"
+        "6) Output HARUS JSON valid saja (tanpa teks tambahan), dengan struktur persis.\n"
+    )
+
+    user = (
+        "Kembalikan JSON:\n"
+        "{\n"
+        '  "rows": [\n'
+        '    {"No":"...","Dimensi":"...","Nilai":..., "Interpretasi":"...","Rekomendasi":"..."}\n'
+        "  ]\n"
+        "}\n"
+        "- Urutan dan jumlah baris harus sama.\n"
+        "- 'Rekomendasi' boleh berupa bullet dengan tanda '-' dalam satu string.\n\n"
+        f"INPUT:\n{json.dumps(payload, ensure_ascii=False)}"
+    )
+
+    try:
+        resp = client.chat_completion(
+            model=LLM_MODEL_NAME,
+            messages=[
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            max_tokens=1100,
+            temperature=0.2,
+            top_p=0.9,
+        )
+        text = resp.choices[0].message.content.strip()
+        data = json.loads(text)
+        rows_out = data.get("rows", [])
+        if not isinstance(rows_out, list) or len(rows_out) != len(rows):
+            raise ValueError("Format JSON rows tidak sesuai.")
+        cleaned = []
+        for i, r in enumerate(rows_out):
+            cleaned.append({
+                "No": str(r.get("No", rows[i]["No"])),
+                "Dimensi": str(r.get("Dimensi", rows[i]["Dimensi"])),
+                "Nilai": rows[i]["Nilai"],  # paksa nilai dari aplikasi
+                "Interpretasi": str(r.get("Interpretasi","") or ""),
+                "Rekomendasi": str(r.get("Rekomendasi","") or ""),
+            })
+        return cleaned, "LLM mengisi Interpretasi & Rekomendasi."
+    except Exception as e:
+        out = []
+        for r in rows:
+            out.append({k: r.get(k) for k in ["No","Dimensi","Nilai"]} | {"Interpretasi":"", "Rekomendasi":""})
+        return out, f"LLM error: {repr(e)}"
+
+
+def _set_cell_shading(cell, fill_hex="1F1F1F"):
+    tcPr = cell._tc.get_or_add_tcPr()
+    shd = OxmlElement("w:shd")
+    shd.set(qn("w:val"), "clear")
+    shd.set(qn("w:color"), "auto")
+    shd.set(qn("w:fill"), fill_hex)
+    tcPr.append(shd)
+
+def _set_cell_text_color(cell, rgb_hex="FFFFFF"):
+    for p in cell.paragraphs:
+        for run in p.runs:
+            rPr = run._r.get_or_add_rPr()
+            color = OxmlElement("w:color")
+            color.set(qn("w:val"), rgb_hex)
+            rPr.append(color)
+
+def _set_table_borders(table):
+    tbl = table._tbl
+    tblPr = tbl.tblPr
+    if tblPr is None:
+        tblPr = OxmlElement("w:tblPr")
+        tbl.append(tblPr)
+    tblBorders = OxmlElement("w:tblBorders")
+    for edge in ("top", "left", "bottom", "right", "insideH", "insideV"):
+        elem = OxmlElement(f"w:{edge}")
+        elem.set(qn("w:val"), "single")
+        elem.set(qn("w:sz"), "8")
+        elem.set(qn("w:space"), "0")
+        elem.set(qn("w:color"), "FFFFFF")
+        tblBorders.append(elem)
+    tblPr.append(tblBorders)
+
+def generate_word_table_interpretasi(header, rows_filled, wilayah_label, jumlah_perpus_by_jenis):
+    if (not DOCX_AVAILABLE) or (Document is None):
+        return None
+
+    doc = Document()
+
+    # Title
+    title = doc.add_paragraph()
+    run = title.add_run(header["judul"])
+    run.bold = True
+    run.font.size = Pt(18)
+
+    doc.add_paragraph(f"Target sampel per jenis: {header['target_sampel']}")
+
+    # Table
+    cols = ["No", "Dimensi", "Nilai", "Interpretasi", "Rekomendasi"]
+    table = doc.add_table(rows=1, cols=len(cols))
+    table.autofit = True
+    _set_table_borders(table)
+
+    hdr_cells = table.rows[0].cells
+    for i, c in enumerate(cols):
+        hdr_cells[i].text = c
+        _set_cell_shading(hdr_cells[i], "1A1A1A")
+        _set_cell_text_color(hdr_cells[i], "FFFFFF")
+        for p in hdr_cells[i].paragraphs:
+            for rr in p.runs:
+                rr.bold = True
+
+    for r in rows_filled:
+        row_cells = table.add_row().cells
+        row_cells[0].text = str(r.get("No",""))
+        row_cells[1].text = str(r.get("Dimensi",""))
+
+        # format nilai:
+        # - sub/dim biasanya 0–1 → tampilkan 3 desimal
+        # - IPLM 0–100 → tampilkan 2 desimal
+        try:
+            dim = str(r.get("Dimensi","")).strip().lower()
+            val = _to_float(r.get("Nilai", 0.0), 0.0)
+            if dim == "nilai iplm":
+                row_cells[2].text = f"{val:.2f}"
+            else:
+                row_cells[2].text = f"{val:.3f}"
+        except Exception:
+            row_cells[2].text = str(r.get("Nilai",""))
+
+        row_cells[3].text = str(r.get("Interpretasi","") or "")
+        row_cells[4].text = str(r.get("Rekomendasi","") or "")
+
+        for c in row_cells:
+            _set_cell_shading(c, "262626")
+            _set_cell_text_color(c, "FFFFFF")
+
+    # ===== tambahan: deskripsi jumlah perpustakaan sumber data (gambar 2) =====
+    doc.add_paragraph("")  # spacer
+    j = jumlah_perpus_by_jenis or {"sekolah":0,"umum":0,"khusus":0,"total":0}
+    p = doc.add_paragraph()
+    p.add_run("Sumber data (jumlah perpustakaan pada tabel agregat wilayah × jenis): ").bold = True
+    doc.add_paragraph(
+        f"Perpustakaan sekolah = {int(j.get('sekolah',0))}, "
+        f"perpustakaan umum = {int(j.get('umum',0))}, "
+        f"perpustakaan khusus = {int(j.get('khusus',0))}, "
+        f"total = {int(j.get('total',0))}."
+    )
+
+    outpath = tempfile.mktemp(suffix=".docx")
+    doc.save(outpath)
+    return outpath
+
+
+# ============================================================
+# 15) CORE RUN
+# ============================================================
+
+def _empty_outputs(msg="Data belum siap."):
+    empty = pd.DataFrame()
+    empty_fig = go.Figure()
+    return (
+        "",  # kpi_md
+        empty, empty, empty, empty, empty,
+        None, None, None, None, None,
+        empty_fig, empty_fig, empty_fig,
+        msg,  # msg
+        "LLM belum tersedia.",  # status llm
+        None  # word path
+    )
+
+def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta):
+    try:
+        if df_all is None or df_all.empty or df_raw is None or df_raw.empty:
+            return _empty_outputs("Data belum ter-load. Pastikan file tersedia.")
+
+        # Filter
+        df = df_all.copy()
+        if prov_value and prov_value != "(Semua)":
+            df = df[df["PROV_DISP"] == prov_value]
+        if kab_value and kab_value != "(Semua)":
+            df = df[df["KAB_DISP"] == kab_value]
+        if kew_value and kew_value != "(Semua)":
+            df = df[df["KEW_NORM"] == kew_value]
+        if df.empty:
+            return _empty_outputs("Tidak ada data untuk filter ini.")
+
+        kew_norm = kew_value if (kew_value and kew_value != "(Semua)") else "(Semua)"
+        faktor_wilayah_jenis = build_faktor_wilayah_jenis(df, pop_kab, pop_prov, pop_khusus, kew_norm)
+        agg_jenis_full = build_agg_wilayah_jenis(df, faktor_wilayah_jenis, kew_norm)
+        agg_total = build_agg_wilayah_total_from_jenis(agg_jenis_full, faktor_wilayah_jenis, kew_norm)
+
+        summary_jenis = build_summary_per_jenis(agg_jenis_full, agg_total)
+        verif_total = build_verif_jenis(faktor_wilayah_jenis, kew_norm)
+        detail_view = attach_final_to_detail(df, agg_total, meta, kew_norm)
+
+        # agg_jenis view (UI hanya sampai indeks dasar)
+        if agg_jenis_full is None or agg_jenis_full.empty:
+            agg_jenis_view = agg_jenis_full
+        else:
+            kew_norm2 = str(kew_norm).upper()
+            label_name = "Kab/Kota" if ("KAB" in kew_norm2 or "KOTA" in kew_norm2) else ("Provinsi" if "PROV" in kew_norm2 else "Kab/Kota")
+            cols_upto = [
+                "group_key",
+                label_name,
+                "Jenis",
+                "Jumlah",
+                "Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
+                "Rata2_dim_kepatuhan","Rata2_dim_kinerja",
+                "Indeks_Dasar_Agregat_0_100",
+            ]
+            cols_upto = [c for c in cols_upto if c in agg_jenis_full.columns]
+            agg_jenis_view = agg_jenis_full[cols_upto].copy()
+
+        # RAW download (hasil filter)
+        raw = df_raw.copy()
+        if prov_value and prov_value != "(Semua)":
+            raw = raw[raw["PROV_DISP"] == prov_value]
+        if kab_value and kab_value != "(Semua)":
+            raw = raw[raw["KAB_DISP"] == kab_value]
+        if kew_value and kew_value != "(Semua)":
+            raw = raw[raw["KEW_NORM"] == kew_value]
+
+        # Bell curve per jenis
+        if detail_view is None or detail_view.empty:
+            fig_umum = _make_bell_curve_entitas(pd.DataFrame(), "Bell Curve — Jenis: Umum")
+            fig_sekolah = _make_bell_curve_entitas(pd.DataFrame(), "Bell Curve — Jenis: Sekolah")
+            fig_khusus = _make_bell_curve_entitas(pd.DataFrame(), "Bell Curve — Jenis: Khusus")
+        else:
+            hover_cols = [hc for hc in ["Provinsi", "Kab/Kota", "Jenis"] if hc in detail_view.columns]
+
+            def _fig(j):
+                d = detail_view[detail_view["Jenis"].astype(str).str.lower() == j].copy()
+                return _make_bell_curve_entitas(
+                    d,
+                    title=f"Bell Curve — Jenis: {j.title()} (Skor: Indeks_Dasar_0_100)",
+                    xcol="Indeks_Dasar_0_100",
+                    label_col=("nm_perpustakaan" if "nm_perpustakaan" in d.columns else "nm_perpustakaan"),
+                    hover_cols=hover_cols,
+                    min_points=2
+                )
+
+            fig_sekolah = _fig("sekolah")
+            fig_umum = _fig("umum")
+            fig_khusus = _fig("khusus")
+
+        kpi_md = build_kpi_markdown(summary_jenis)
+
+        # Export xlsx
+        tmpdir = tempfile.mkdtemp()
+        prov_slug = (_canon(prov_value or "SEMUA").upper() or "SEMUA")
+        kab_slug = (_canon(kab_value or "SEMUA").upper() or "SEMUA")
+        kew_slug = (_canon(kew_value or "SEMUA").upper() or "SEMUA")
+
+        p_summary = str(Path(tmpdir) / f"IPLM_RingkasanJenisKeseluruhan_33_88_{prov_slug}_{kab_slug}_{kew_slug}.xlsx")
+        p_total   = str(Path(tmpdir) / f"IPLM_AgregatWilayah_Keseluruhan_33_88_{prov_slug}_{kab_slug}_{kew_slug}.xlsx")
+        p_raw     = str(Path(tmpdir) / f"IPLM_RAW_DATA_{prov_slug}_{kab_slug}_{kew_slug}.xlsx")
+        p_detail  = str(Path(tmpdir) / f"IPLM_DetailEntitas_FinalMenempelWilayah_{prov_slug}_{kab_slug}_{kew_slug}.xlsx")
+        p_verif   = str(Path(tmpdir) / f"IPLM_KecukupanSampel_33_88_{prov_slug}_{kab_slug}_{kew_slug}.xlsx")
+
+        summary_jenis.to_excel(p_summary, index=False)
+        agg_total.to_excel(p_total, index=False)
+        raw.to_excel(p_raw, index=False)
+        detail_view.to_excel(p_detail, index=False)
+        verif_total.to_excel(p_verif, index=False)
+
+        # ====== Word tabel interpretasi & rekomendasi ======
+        wilayah_txt = kab_value if (kab_value and kab_value != "(Semua)") else (prov_value if (prov_value and prov_value != "(Semua)") else "Nasional/All")
+        header, rows = build_interpretasi_table_values(agg_total, wilayah_txt, TARGET_RATIO)
+
+        # jumlah perpustakaan sumber data (gambar 2)
+        jumlah_perpus = summarize_jumlah_perpus_dari_agg_jenis(agg_jenis_full, wilayah_txt, kew_norm)
+
+        rows_filled, llm_status = llm_fill_interpretasi_rekomendasi(
+            header=header,
+            rows=rows,
+            wilayah_label=wilayah_txt,
+            kew_label=(kew_value or "(Semua)"),
+            jumlah_perpus_by_jenis=jumlah_perpus
+        )
+        word_path = generate_word_table_interpretasi(header, rows_filled, wilayah_txt, jumlah_perpus)
+
+        msg = (
+            f"Selesai (TARGET {TARGET_RATIO*100:.2f}%): raw={len(raw)} | entitas={len(detail_view)} | "
+            f"wilayah(keseluruhan)={len(agg_total)} | jenis(agregat)={len(agg_jenis_full)}"
+            + ("" if DOCX_AVAILABLE else " | python-docx tidak tersedia (Word OFF)")
+        )
+
+        return (
+            kpi_md,
+            summary_jenis, agg_total, agg_jenis_view, detail_view, verif_total,
+            p_summary, p_total, p_raw, p_detail, p_verif,
+            fig_umum, fig_sekolah, fig_khusus,
+            msg,
+            llm_status,
+            (word_path if word_path else None)
+        )
+
+    except Exception as e:
+        return _empty_outputs(f"Runtime error: {repr(e)}")
+
+
+# ============================================================
+# 16) UI (NO UPLOAD)
+# ============================================================
+
+def ui_load(force=False):
+    df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info = load_default_files(force=force)
+    if df_all is None or (isinstance(df_all, pd.DataFrame) and df_all.empty):
+        return (
+            None, None, None, None, None, {}, info,
+            gr.update(choices=["(Semua)"], value="(Semua)"),
+            gr.update(choices=["(Semua)"], value="(Semua)"),
+            gr.update(choices=["(Semua)"], value="(Semua)"),
+        )
+
+    prov_vals = df_all["PROV_DISP"].dropna().astype(str).tolist()
+    prov_vals = [v for v in prov_vals if v and v.strip()]
+    prov_choices = ["(Semua)"] + sorted(set(prov_vals))
+
+    kab_choices = ["(Semua)"] + sorted([x for x in df_all["KAB_DISP"].dropna().unique().tolist() if x])
+    kew_choices = ["(Semua)"] + sorted([x for x in df_all["KEW_NORM"].dropna().unique().tolist() if x])
+    default_kew = "KAB/KOTA" if "KAB/KOTA" in kew_choices else ("PROVINSI" if "PROVINSI" in kew_choices else "(Semua)")
+
+    return (
+        df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info,
+        gr.update(choices=prov_choices, value="(Semua)"),
+        gr.update(choices=kab_choices, value="(Semua)"),
+        gr.update(choices=kew_choices, value=default_kew),
+    )
+
+def on_prov_change(prov_value):
+    df_all, _, _, _, _, _, _ = load_default_files(force=False)
+    if df_all is None or df_all.empty:
+        return gr.update(choices=["(Semua)"], value="(Semua)")
+    if prov_value is None or prov_value == "(Semua)":
+        vals = df_all["KAB_DISP"].dropna().unique().tolist()
+    else:
+        vals = df_all.loc[df_all["PROV_DISP"] == prov_value, "KAB_DISP"].dropna().unique().tolist()
+    vals = sorted([v for v in vals if v])
+    return gr.update(choices=["(Semua)"] + vals, value="(Semua)")
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"""
+# IPLM 2025 — Final (Target Sampel 33.88% per Jenis) — TANPA Kinerja Relatif / Percentile
+Mode NO UPLOAD (cache aktif). File dibaca dari repo/server:
+- DATA_FILE   = {DATA_FILE}
+- POP_KAB     = {POP_KAB}
+- POP_PROV    = {POP_PROV}
+- POP_KHUSUS  = {POP_KHUSUS}
+
+TARGET RATIO (per jenis): {TARGET_RATIO*100:.2f}%
+
+Dashboard KPI:
+- Indeks IPLM FINAL (disesuaikan 33.88%)
+- Indeks Dasar (tanpa penyesuaian)
+
+UPDATE LLM + WORD:
+- Tabel Word "Interpretasi & Rekomendasi" memakai NILAI APA ADANYA (tanpa dikali 100) untuk sub/dim.
+- Baris "Nilai IPLM" memakai Indeks_Final_Wilayah_0_100 apa adanya.
+- Di bawah tabel Word ditambahkan ringkasan jumlah perpustakaan sumber data (sekolah/umum/khusus/total) dari tabel agregat wilayah × jenis.
+""")
+
+    state_df = gr.State(None)
+    state_raw = gr.State(None)
+    state_pop_kab = gr.State(None)
+    state_pop_prov = gr.State(None)
+    state_pop_khusus = gr.State(None)
+    state_meta = gr.State({})
+
+    info_box = gr.Markdown()
+
+    with gr.Row():
+        dd_prov = gr.Dropdown(label="Provinsi", choices=["(Semua)"], value="(Semua)")
+        dd_kab = gr.Dropdown(label="Kab/Kota", choices=["(Semua)"], value="(Semua)")
+        dd_kew = gr.Dropdown(label="Kewenangan", choices=["(Semua)"], value="(Semua)")
+
+    dd_prov.change(fn=on_prov_change, inputs=[dd_prov], outputs=dd_kab)
+
+    run_btn = gr.Button("Jalankan Perhitungan")
+    msg_out = gr.Markdown()
+
+    kpi_out = gr.Markdown()
+
+    gr.Markdown("## Ringkasan (Jenis + Keseluruhan)")
+    out_summary = gr.DataFrame(interactive=False)
+
+    gr.Markdown("## Agregat Wilayah (Keseluruhan) — FIX avg3")
+    out_agg_total = gr.DataFrame(interactive=False)
+
+    gr.Markdown("## Agregat Wilayah x Jenis — (ditampilkan sampai Indeks Dasar)")
+    out_agg_jenis = gr.DataFrame(interactive=False)
+
+    gr.Markdown("## Detail Entitas (Final menempel dari wilayah)")
+    out_detail = gr.DataFrame(interactive=False)
+
+    gr.Markdown("## Kecukupan Sampel 33.88%")
+    out_verif = gr.DataFrame(interactive=False)
+
+    gr.Markdown("## Bell Curve — Indeks Dasar per Entitas (per Jenis) + Nama Perpustakaan")
+    gr.Markdown("### Perpustakaan Umum")
+    bell_umum = gr.Plot(scale=1)
+
+    gr.Markdown("### Perpustakaan Sekolah")
+    bell_sekolah = gr.Plot(scale=1)
+
+    gr.Markdown("### Perpustakaan Khusus")
+    bell_khusus = gr.Plot(scale=1)
+
+    gr.Markdown("## Status LLM (Isi Interpretasi & Rekomendasi)")
+    llm_status_out = gr.Markdown()
+
+    with gr.Row():
+        dl_summary = gr.DownloadButton(label="Download Ringkasan (.xlsx)")
+        dl_total = gr.DownloadButton(label="Download Agregat Wilayah (.xlsx)")
+        dl_raw = gr.DownloadButton(label="Download Data Mentah (.xlsx)")
+        dl_detail = gr.DownloadButton(label="Download Detail Entitas (.xlsx)")
+        dl_verif = gr.DownloadButton(label="Download Kecukupan Sampel (.xlsx)")
+        dl_word = gr.DownloadButton(label="Download Word: Interpretasi & Rekomendasi (.docx)" if DOCX_AVAILABLE else "Download Word (OFF)")
+
+    run_btn.click(
+        fn=run_calc,
+        inputs=[dd_prov, dd_kab, dd_kew, state_df, state_raw, state_pop_kab, state_pop_prov, state_pop_khusus, state_meta],
+        outputs=[
+            kpi_out,
+            out_summary, out_agg_total, out_agg_jenis, out_detail, out_verif,
+            dl_summary, dl_total, dl_raw, dl_detail, dl_verif,
+            bell_umum, bell_sekolah, bell_khusus,
+            msg_out,
+            llm_status_out,
+            dl_word
+        ]
+    )
+
+    demo.load(
+        fn=lambda: ui_load(force=False),
+        inputs=[],
+        outputs=[state_df, state_raw, state_pop_kab, state_pop_prov, state_pop_khusus, state_meta, info_box, dd_prov, dd_kab, dd_kew]
+    )
+
+demo.launch()

gitattributes

@@ -0,0 +1,12 @@
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL190226.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL200226.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALLL.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]IPLM[[:space:]]FINAL_CLEAN_pusat.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]IPLM[[:space:]]FINAL_CLEAN.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_iplm_final_05022026_update1.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_iplm_final_05022026.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_FINAL_CLEAN_pusat_15012026.xlsx filter=lfs diff=lfs merge=lfs -text
+File_IPLM_090226_All.xlsx filter=lfs diff=lfs merge=lfs -text
+IPLM_clean_manual_131225.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL200226[[:space:]]+[[:space:]]TORAJA[[:space:]]UTARA.xlsx filter=lfs diff=lfs merge=lfs -text

gitattributes (1)

@@ -0,0 +1,60 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+DM[[:space:]](1).xlsx filter=lfs diff=lfs merge=lfs -text
+DM.xlsx filter=lfs diff=lfs merge=lfs -text
+DM[[:space:]](3).xlsx filter=lfs diff=lfs merge=lfs -text
+data_iplm_clean_complete.xlsx filter=lfs diff=lfs merge=lfs -text
+DMx.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_clean_SNP_jenis[[:space:]](2).xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_clean_SNP_jenis.xlsx filter=lfs diff=lfs merge=lfs -text
+IPLM_clean_manual_131225.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]IPLM[[:space:]]FINAL_CLEAN.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]IPLM[[:space:]]FINAL_CLEAN_pusat.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_FINAL_CLEAN_pusat_12012026.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_FINAL_CLEAN_pusat_15012026.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_FINAL_CLEAN_pusat_15012026[[:space:]]HASIL[[:space:]]SEMENTARA.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_FINAL_CLEAN_pusat_15012026x.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_IPLM_GABUNGAN_DEDUP_FIX_SUPERGALAK.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]](1).xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]](2).xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_iplm_final_05022026.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA_iplm_final_05022026_update1.xlsx filter=lfs diff=lfs merge=lfs -text
+File_IPLM_090226_All.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALLL.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL190226.xlsx filter=lfs diff=lfs merge=lfs -text
+DATA[[:space:]]CLEAN[[:space:]]GABUNGAN[[:space:]]SANGGAH-TIDAK[[:space:]]SANGGAH[[:space:]]-[[:space:]]ALL200226.xlsx filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -0,0 +1,9 @@
+pandas
+numpy
+gradio
+python-docx
+openpyxl
+huggingface-hub
+scikit-learn
+plotly
+matplotlib