Update app.py

app.py

@@ -903,7 +903,7 @@ def build_agg_wilayah_jenis(df_filtered: pd.DataFrame, faktor_wilayah_jenis: pd.
 
     return agg
 # ============================================================
-# 8) AGREGAT WILAYAH (KESELURUHAN) — FIX: avg3 + tampilkan POP/TARGET/GAP per jenis
+# 8) AGREGAT WILAYAH (KESELURUHAN) — FIX: avg3 + tampilkan POP/TARGET/N per jenis
 # ============================================================
 
 def build_agg_wilayah_total_from_jenis(agg_jenis: pd.DataFrame, faktor_wilayah_jenis: pd.DataFrame, kew_value: str):
@@ -956,32 +956,69 @@ def build_agg_wilayah_total_from_jenis(agg_jenis: pd.DataFrame, faktor_wilayah_j
         Indeks_Final_Wilayah_0_100=("Indeks_Final_Agregat_0_100", "mean"),
     )
 
-    # --- tempel POP/TARGET/SAMPEL/GAP per jenis ke agg_total ---
+    # --- tempel POP/TARGET/N/COVERAGE/FAKTOR per jenis ke agg_total ---
     if faktor_wilayah_jenis is not None and not faktor_wilayah_jenis.empty:
         fw = faktor_wilayah_jenis.copy()
         fw["Jenis"] = fw["Jenis"].astype(str).str.lower().str.strip()
 
-        # pivot target/pop/n/gap per jenis
+        # pivot per jenis
         piv = fw.pivot_table(
             index=["group_key", label_name],
             columns="Jenis",
-            values=["pop_total_jenis", "target_total_68_jenis", "n_jenis", "gap_target68_jenis"],
+            values=[
+                "pop_total_jenis",
+                "target_total_68_jenis",
+                "n_jenis",
+                "coverage_jenis_%",
+                "faktor_penyesuaian_jenis",
+                "gap_target68_jenis"
+            ],
             aggfunc="first"
         )
-
-        # flatten columns
         piv.columns = [f"{v}_{k}" for v, k in piv.columns]
         piv = piv.reset_index()
 
         out = out.merge(piv, on=["group_key", label_name], how="left")
 
-        # rapihin NaN -> 0
-        for j in ["sekolah","umum","khusus"]:
+        # rapihin NaN -> 0 + tipe
+        for j in ["sekolah", "umum", "khusus"]:
             for basecol in ["pop_total_jenis", "target_total_68_jenis", "n_jenis", "gap_target68_jenis"]:
                 c = f"{basecol}_{j}"
                 if c in out.columns:
-                    if basecol == "pop_total_jenis" or basecol == "target_total_68_jenis" or basecol == "n_jenis" or basecol == "gap_target68_jenis":
-                        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0).round(0).astype(int)
+                    out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0).round(0).astype(int)
+
+            c_cov = f"coverage_jenis_%_{j}"
+            if c_cov in out.columns:
+                out[c_cov] = pd.to_numeric(out[c_cov], errors="coerce").fillna(0.0).round(2)
+
+            c_fac = f"faktor_penyesuaian_jenis_{j}"
+            if c_fac in out.columns:
+                out[c_fac] = pd.to_numeric(out[c_fac], errors="coerce").fillna(0.0).round(3)
+
+        # === TOTAL UPDATE (sesuai data POP yang ada) ===
+        # Total Pop dan Target = sum 3 jenis (sekolah+umum+khusus)
+        def _sum3(a, b, c):
+            return (
+                pd.to_numeric(a, errors="coerce").fillna(0)
+                + pd.to_numeric(b, errors="coerce").fillna(0)
+                + pd.to_numeric(c, errors="coerce").fillna(0)
+            )
+
+        if ("pop_total_jenis_sekolah" in out.columns) and ("pop_total_jenis_umum" in out.columns) and ("pop_total_jenis_khusus" in out.columns):
+            out["Pop_Total_Update"] = _sum3(out["pop_total_jenis_sekolah"], out["pop_total_jenis_umum"], out["pop_total_jenis_khusus"]).round(0).astype(int)
+
+        if ("target_total_68_jenis_sekolah" in out.columns) and ("target_total_68_jenis_umum" in out.columns) and ("target_total_68_jenis_khusus" in out.columns):
+            out["Target68_Total_Update"] = _sum3(out["target_total_68_jenis_sekolah"], out["target_total_68_jenis_umum"], out["target_total_68_jenis_khusus"]).round(0).astype(int)
+
+        if ("n_jenis_sekolah" in out.columns) and ("n_jenis_umum" in out.columns) and ("n_jenis_khusus" in out.columns):
+            out["Terkumpul_Total_Update"] = _sum3(out["n_jenis_sekolah"], out["n_jenis_umum"], out["n_jenis_khusus"]).round(0).astype(int)
+
+        # coverage_total_update = min(terkumpul/target,1)*100
+        if ("Terkumpul_Total_Update" in out.columns) and ("Target68_Total_Update" in out.columns):
+            den = pd.to_numeric(out["Target68_Total_Update"], errors="coerce").fillna(0).astype(float)
+            num = pd.to_numeric(out["Terkumpul_Total_Update"], errors="coerce").fillna(0).astype(float)
+            cov = np.where(den > 0, np.minimum(num / den, 1.0) * 100.0, 0.0)
+            out["Coverage_Target68_Total_Update_%"] = pd.Series(cov).round(2)
 
     # rounding index
     for c in [
@@ -995,14 +1032,17 @@ def build_agg_wilayah_total_from_jenis(agg_jenis: pd.DataFrame, faktor_wilayah_j
         if c in out.columns:
             out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
 
-    return out
+    # n_total integer
+    if "n_total" in out.columns:
+        out["n_total"] = pd.to_numeric(out["n_total"], errors="coerce").fillna(0).round(0).astype(int)
 
+    return out
 
 # ============================================================
-# 9) SUMMARY (PER JENIS) + KESELURUHAN (FIX ÷3 + selalu tampil 3 jenis)
+# 9) SUMMARY (PER JENIS) + KESELURUHAN (FIX ÷3 + tambah cakupan & penyesuaian)
 # ============================================================
 
-def build_summary_per_jenis(agg_jenis: pd.DataFrame, agg_total: pd.DataFrame):
+def build_summary_per_jenis(agg_jenis: pd.DataFrame, agg_total: pd.DataFrame, faktor_wilayah_jenis: pd.DataFrame | None = None):
     jenis_list = ["sekolah", "umum", "khusus"]
 
     def _row_default(jenis):
@@ -1010,44 +1050,92 @@ def build_summary_per_jenis(agg_jenis: pd.DataFrame, agg_total: pd.DataFrame):
             "Jenis": jenis,
             "Jumlah_Wilayah": 0,
             "Total_Perpus": 0,
+
+            # POP/TARGET/N per jenis (sum nasional pada scope filter)
+            "Pop_Total_Jenis": 0,
+            "Target68_Total_Jenis": 0,
+            "Terkumpul_Jenis": 0,
+            "Coverage_Target68_Jenis_%": 0.00,
+
+            # skor
             "Rata2_sub_koleksi": 0.0,
             "Rata2_sub_sdm": 0.0,
             "Rata2_sub_pelayanan": 0.0,
             "Rata2_sub_pengelolaan": 0.0,
             "Rata2_dim_kepatuhan": 0.0,
             "Rata2_dim_kinerja": 0.0,
+
+            # dasar & final + penyesuaian poin
+            "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}
 
+    # ===== ambil POP/TARGET/N per jenis dari faktor_wilayah_jenis =====
+    fw_sum = {}
+    if faktor_wilayah_jenis is not None and (not faktor_wilayah_jenis.empty):
+        fw = faktor_wilayah_jenis.copy()
+        fw["Jenis"] = fw["Jenis"].astype(str).str.lower().str.strip()
+
+        for j in jenis_list:
+            sub = fw[fw["Jenis"] == j].copy()
+            if sub.empty:
+                fw_sum[j] = {"pop": 0, "target": 0, "n": 0, "cov": 0.0}
+                continue
+
+            pop = int(pd.to_numeric(sub.get("pop_total_jenis", 0), errors="coerce").fillna(0).sum())
+            target = int(pd.to_numeric(sub.get("target_total_68_jenis", 0), errors="coerce").fillna(0).sum())
+            n = int(pd.to_numeric(sub.get("n_jenis", 0), errors="coerce").fillna(0).sum())
+
+            cov = 0.0
+            if target > 0:
+                cov = float(min(n / float(target), 1.0) * 100.0)
+
+            fw_sum[j] = {"pop": pop, "target": target, "n": n, "cov": cov}
+
+    # ===== isi ringkasan dari agg_jenis =====
     if agg_jenis is not None and not agg_jenis.empty:
         for jenis in jenis_list:
             sub = agg_jenis[agg_jenis["Jenis"].astype(str).str.lower() == jenis].copy()
             if sub.empty:
+                # tetap isi pop/target/n kalau ada
+                if jenis in fw_sum:
+                    rows_by_jenis[jenis]["Pop_Total_Jenis"] = fw_sum[jenis]["pop"]
+                    rows_by_jenis[jenis]["Target68_Total_Jenis"] = fw_sum[jenis]["target"]
+                    rows_by_jenis[jenis]["Terkumpul_Jenis"] = fw_sum[jenis]["n"]
+                    rows_by_jenis[jenis]["Coverage_Target68_Jenis_%"] = fw_sum[jenis]["cov"]
                 continue
 
+            dasar = float(pd.to_numeric(sub["Indeks_Dasar_Agregat_0_100"], errors="coerce").fillna(0).mean())
+            final = float(pd.to_numeric(sub["Indeks_Final_Agregat_0_100"], errors="coerce").fillna(0).mean())
+
             rows_by_jenis[jenis] = {
                 "Jenis": jenis,
                 "Jumlah_Wilayah": int(sub.shape[0]),
                 "Total_Perpus": int(pd.to_numeric(sub["Jumlah"], errors="coerce").fillna(0).sum()),
+
+                "Pop_Total_Jenis": int(fw_sum.get(jenis, {}).get("pop", 0)),
+                "Target68_Total_Jenis": int(fw_sum.get(jenis, {}).get("target", 0)),
+                "Terkumpul_Jenis": int(fw_sum.get(jenis, {}).get("n", 0)),
+                "Coverage_Target68_Jenis_%": float(fw_sum.get(jenis, {}).get("cov", 0.0)),
+
                 "Rata2_sub_koleksi": float(pd.to_numeric(sub["Rata2_sub_koleksi"], errors="coerce").fillna(0).mean()),
                 "Rata2_sub_sdm": float(pd.to_numeric(sub["Rata2_sub_sdm"], errors="coerce").fillna(0).mean()),
                 "Rata2_sub_pelayanan": float(pd.to_numeric(sub["Rata2_sub_pelayanan"], errors="coerce").fillna(0).mean()),
                 "Rata2_sub_pengelolaan": float(pd.to_numeric(sub["Rata2_sub_pengelolaan"], errors="coerce").fillna(0).mean()),
                 "Rata2_dim_kepatuhan": float(pd.to_numeric(sub["Rata2_dim_kepatuhan"], errors="coerce").fillna(0).mean()),
                 "Rata2_dim_kinerja": float(pd.to_numeric(sub["Rata2_dim_kinerja"], errors="coerce").fillna(0).mean()),
-                "Indeks_Final_Disesuaikan_0_100": float(pd.to_numeric(sub["Indeks_Final_Agregat_0_100"], errors="coerce").fillna(0).mean()),
+
+                "Indeks_Dasar_0_100": dasar,
+                "Indeks_Final_Disesuaikan_0_100": final,
+                "Penyesuaian_Poin": float(final - dasar),
             }
 
     rows = [rows_by_jenis[j] for j in jenis_list]
 
-    final_all = (
-        float(rows_by_jenis["sekolah"]["Indeks_Final_Disesuaikan_0_100"])
-        + float(rows_by_jenis["umum"]["Indeks_Final_Disesuaikan_0_100"])
-        + float(rows_by_jenis["khusus"]["Indeks_Final_Disesuaikan_0_100"])
-    ) / 3.0
-
+    # ===== keseluruhan = avg3 tetap ÷3 (missing=0) =====
     def _avg3(field):
         return (
             float(rows_by_jenis["sekolah"][field])
@@ -1055,34 +1143,59 @@ def build_summary_per_jenis(agg_jenis: pd.DataFrame, agg_total: pd.DataFrame):
             + float(rows_by_jenis["khusus"][field])
         ) / 3.0
 
+    final_all = _avg3("Indeks_Final_Disesuaikan_0_100")
+    dasar_all = _avg3("Indeks_Dasar_0_100")
+
     total_perpus_all = int(rows_by_jenis["sekolah"]["Total_Perpus"] + rows_by_jenis["umum"]["Total_Perpus"] + rows_by_jenis["khusus"]["Total_Perpus"])
     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"]))
 
+    # Total POP/TARGET/N keseluruhan = sum3 (bukan avg3), karena ini memang total cakupan
+    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"]["Target68_Total_Jenis"] + rows_by_jenis["umum"]["Target68_Total_Jenis"] + rows_by_jenis["khusus"]["Target68_Total_Jenis"])
+    n_all = int(rows_by_jenis["sekolah"]["Terkumpul_Jenis"] + rows_by_jenis["umum"]["Terkumpul_Jenis"] + rows_by_jenis["khusus"]["Terkumpul_Jenis"])
+    cov_all = float(min(n_all / float(target_all), 1.0) * 100.0) if target_all > 0 else 0.0
+
     rows.append({
         "Jenis": "keseluruhan",
         "Jumlah_Wilayah": jumlah_wilayah_all,
         "Total_Perpus": total_perpus_all,
+
+        "Pop_Total_Jenis": pop_all,
+        "Target68_Total_Jenis": target_all,
+        "Terkumpul_Jenis": n_all,
+        "Coverage_Target68_Jenis_%": cov_all,
+
         "Rata2_sub_koleksi": _avg3("Rata2_sub_koleksi"),
         "Rata2_sub_sdm": _avg3("Rata2_sub_sdm"),
         "Rata2_sub_pelayanan": _avg3("Rata2_sub_pelayanan"),
         "Rata2_sub_pengelolaan": _avg3("Rata2_sub_pengelolaan"),
         "Rata2_dim_kepatuhan": _avg3("Rata2_dim_kepatuhan"),
         "Rata2_dim_kinerja": _avg3("Rata2_dim_kinerja"),
+
+        "Indeks_Dasar_0_100": dasar_all,
         "Indeks_Final_Disesuaikan_0_100": final_all,
+        "Penyesuaian_Poin": float(final_all - dasar_all),
     })
 
     out = pd.DataFrame(rows)
 
+    # rounding
     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)
 
-    out["Indeks_Final_Disesuaikan_0_100"] = pd.to_numeric(out["Indeks_Final_Disesuaikan_0_100"], errors="coerce").fillna(0.0).round(2)
+    for c in ["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
+    # integer columns
+    for c in ["Jumlah_Wilayah","Total_Perpus","Pop_Total_Jenis","Target68_Total_Jenis","Terkumpul_Jenis"]:
+        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0).round(0).astype(int)
 
+    out["Coverage_Target68_Jenis_%"] = pd.to_numeric(out["Coverage_Target68_Jenis_%"], errors="coerce").fillna(0.0).round(2)
+
+    return out
 
 # ============================================================
 # 10) DETAIL ENTITAS: Final menempel dari agg_total (wilayah)
@@ -1287,75 +1400,29 @@ def _make_bell_curve(dfp: pd.DataFrame, xcol: str, title: str, label_col: str |
 
 
 # ============================================================
-# 13) KPI DASHBOARD (PATCH: cakupan 68% PER JENIS -> avg3)
+# 13) KPI DASHBOARD (PATCH: hanya FINAL & DASAR)
 # ============================================================
 
-def compute_dashboard_kpis(summary_jenis: pd.DataFrame, agg_total: pd.DataFrame, agg_jenis: pd.DataFrame, faktor_wilayah_jenis: pd.DataFrame | None = None):
-    def _get_final(j):
+def compute_dashboard_kpis(summary_jenis: pd.DataFrame, agg_total: pd.DataFrame, agg_jenis: pd.DataFrame):
+    def _get_val(j, col):
         sub = summary_jenis[summary_jenis["Jenis"].astype(str).str.lower() == j]
         if sub.empty:
             return 0.0
-        return float(pd.to_numeric(sub["Indeks_Final_Disesuaikan_0_100"], errors="coerce").fillna(0).iloc[0])
-
-    final_sekolah = _get_final("sekolah")
-    final_umum    = _get_final("umum")
-    final_khusus  = _get_final("khusus")
-    final_all     = (final_sekolah + final_umum + final_khusus) / 3.0
-
-    def _get_dasar(j):
-        if agg_jenis is None or agg_jenis.empty:
-            return 0.0
-        sub = agg_jenis[agg_jenis["Jenis"].astype(str).str.lower() == j]
-        if sub.empty:
-            return 0.0
-        return float(pd.to_numeric(sub["Indeks_Dasar_Agregat_0_100"], errors="coerce").fillna(0).mean())
+        return float(pd.to_numeric(sub[col], errors="coerce").fillna(0).iloc[0])
 
-    dasar_sekolah = _get_dasar("sekolah")
-    dasar_umum    = _get_dasar("umum")
-    dasar_khusus  = _get_dasar("khusus")
-    dasar_all     = (dasar_sekolah + dasar_umum + dasar_khusus) / 3.0
+    final_all = _get_val("keseluruhan", "Indeks_Final_Disesuaikan_0_100")
+    dasar_all = _get_val("keseluruhan", "Indeks_Dasar_0_100")
 
-    # === PATCH: cakupan dihitung PER JENIS (total n_jenis / total target_jenis), lalu avg3 ===
-    cakupan_pct = 0.0
-    faktor_mean = 1.0
-    if faktor_wilayah_jenis is not None and (not faktor_wilayah_jenis.empty):
-        fw = faktor_wilayah_jenis.copy()
-        fw["Jenis"] = fw["Jenis"].astype(str).str.lower().str.strip()
-
-        factors = []
-        for j in ["sekolah","umum","khusus"]:
-            sub = fw[fw["Jenis"] == j]
-            if sub.empty:
-                factors.append(0.0)
-                continue
-
-            n_sum = float(pd.to_numeric(sub.get("n_jenis", 0), errors="coerce").fillna(0).sum())
-            t_sum = float(pd.to_numeric(sub.get("target_total_68_jenis", 0), errors="coerce").fillna(0).sum())
-            f = min(n_sum / t_sum, 1.0) if (t_sum and t_sum > 0) else 0.0
-            factors.append(f)
-
-        cakupan_pct = (sum(factors) / 3.0) * 100.0
-
-        # faktor_mean juga dibuat avg3 dari faktor total per jenis (opsional)
-        # kalau mau: mean faktor per-wilayah×jenis
-        faktor_mean = float(pd.to_numeric(fw.get("faktor_penyesuaian_jenis", 1.0), errors="coerce").fillna(1.0).mean())
-
-    dampak = final_all - dasar_all
-
-    return {
-        "final_all": final_all,
-        "dasar_all": dasar_all,
-        "cakupan_pct": cakupan_pct,
-        "faktor_mean": faktor_mean,
-        "dampak": dampak
-    }
+    return {"final_all": final_all, "dasar_all": dasar_all}
 
 
 def build_kpi_markdown(summary_jenis: pd.DataFrame, agg_total: pd.DataFrame, agg_jenis: pd.DataFrame, faktor_wilayah_jenis: pd.DataFrame | None = None) -> str:
+    # faktor_wilayah_jenis tetap diterima supaya pemanggil run_calc tidak perlu diubah banyak,
+    # tapi tidak dipakai lagi di KPI dashboard (dipindah ke tabel ringkasan).
     if summary_jenis is None or summary_jenis.empty:
         return ""
 
-    k = compute_dashboard_kpis(summary_jenis, agg_total, agg_jenis, faktor_wilayah_jenis=faktor_wilayah_jenis)
+    k = compute_dashboard_kpis(summary_jenis, agg_total, agg_jenis)
 
     def fmt(x, nd=2):
         return "NA" if pd.isna(x) else f"{x:.{nd}f}"
@@ -1373,131 +1440,42 @@ def build_kpi_markdown(summary_jenis: pd.DataFrame, agg_total: pd.DataFrame, agg
     <div style="font-size:26px; font-weight:700;">{fmt(k["dasar_all"],2)}</div>
     <div style="opacity:0.7;">Rata-rata 3 jenis (tetap ÷3)</div>
   </div>
-
-  <div style="border:1px solid #333; border-radius:10px; padding:10px 12px; min-width:220px;">
-    <div style="opacity:0.8;">Cakupan Sampel (berdasarkan target 68%)</div>
-    <div style="font-size:26px; font-weight:700;">{fmt(k["cakupan_pct"],0)}%</div>
-    <div style="opacity:0.7;">Rumus: min(total_terkumpul/target_68, 1.0)</div>
-  </div>
-
-  <div style="border:1px solid #333; border-radius:10px; padding:10px 12px; min-width:220px;">
-    <div style="opacity:0.8;">Penyesuaian Nilai (rata-rata)</div>
-    <div style="font-size:26px; font-weight:700;">{fmt(k["dampak"],2)} poin</div>
-    <div style="opacity:0.7;">Faktor penyesuaian (mean): {fmt(k["faktor_mean"],3)}</div>
-  </div>
 </div>
 """.strip()
 
-
 # ============================================================
 # 14) LLM + WORD
 # ============================================================
 
-_HF_CLIENT = None
-
-def get_llm_client():
-    global _HF_CLIENT
-    if _HF_CLIENT is not None:
-        return _HF_CLIENT
-    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 build_context(summary_jenis: pd.DataFrame, agg_total: pd.DataFrame, verif_total: pd.DataFrame, wilayah: str, kew: str) -> str:
-    lines = []
-    lines.append(f"Wilayah filter: {wilayah}")
-    lines.append(f"Kewenangan: {kew}")
-    lines.append("Metode: Indeks dasar dihitung per entitas (Yeo-Johnson + MinMax nasional per indikator), lalu diagregasi per wilayah×jenis. Setelah itu dilakukan penyesuaian berbasis kecukupan sampel minimum 68% pada level wilayah.")
-    lines.append("Rumus penyesuaian: faktor = min(total_terkumpul / target_total_68, 1.0). Faktor wilayah dipakai untuk semua jenis.")
-    lines.append("Rumus keseluruhan wilayah (FIX): nilai keseluruhan wilayah diambil dari rata-rata 3 jenis (sekolah+umum+khusus) ÷ 3 (missing=0, tetap ÷3).")
-
-    if summary_jenis is not None and not summary_jenis.empty:
-        lines.append("\nRingkasan (jenis + keseluruhan):")
-        for _, r in summary_jenis.iterrows():
-            lines.append(
-                f"- {r['Jenis']}: wilayah={int(r['Jumlah_Wilayah'])}, total_perpus={int(r['Total_Perpus'])}, "
-                f"dim_kepatuhan={float(r['Rata2_dim_kepatuhan']):.3f}, dim_kinerja={float(r['Rata2_dim_kinerja']):.3f}, "
-                f"final_disesuaikan={float(r['Indeks_Final_Disesuaikan_0_100']):.2f}"
-            )
-
-    if agg_total is not None and not agg_total.empty:
-        label_col = "Kab/Kota" if "Kab/Kota" in agg_total.columns else ("Provinsi" if "Provinsi" in agg_total.columns else None)
-        lines.append("\nTop 5 wilayah (Final disesuaikan tertinggi):")
-        top = agg_total.sort_values("Indeks_Final_Wilayah_0_100", ascending=False).head(5)
-        for _, r in top.iterrows():
-            wl = r.get(label_col, "(wilayah)") if label_col else "(wilayah)"
-            lines.append(f"- {wl}: Final={float(r['Indeks_Final_Wilayah_0_100']):.2f} | Faktor={float(r.get('faktor_penyesuaian', 1.0)):.3f} | total={int(r.get('n_total', 0))}")
-
-    if verif_total is not None and not verif_total.empty and "GAP_Ke_Target68_Total" in verif_total.columns:
-        lines.append("\nTop 5 wilayah (GAP ke target 68% terbesar):")
-        tmp = verif_total.sort_values("GAP_Ke_Target68_Total", ascending=False).head(5)
-        name_col = "Kab/Kota" if "Kab/Kota" in tmp.columns else ("Provinsi" if "Provinsi" in tmp.columns else None)
-        for _, r in tmp.iterrows():
-            nm = r.get(name_col, "") if name_col else ""
-            lines.append(f"- {nm}: GAP={int(r['GAP_Ke_Target68_Total'])}")
-
-    return "\n".join(lines)
-
-def generate_llm_analysis(summary_jenis, agg_total, verif_total, wilayah, kew):
-    ctx = build_context(summary_jenis, agg_total, verif_total, wilayah, kew)
-    client = get_llm_client()
-    if client is None or not USE_LLM:
-        return "Analisis otomatis (LLM) tidak tersedia. Pastikan token HuggingFace tersedia dan model bisa diakses."
-
-    system_prompt = (
-        "Anda adalah analis kebijakan perpustakaan dan literasi di Indonesia. "
-        "Tugas Anda menyusun analisis berbasis data IPLM secara formal, tajam, dan operasional."
-    )
-    user_prompt = f"""
-DATA RINGKAS IPLM (PENYESUAIAN BERBASIS KECUKUPAN SAMPEL 68% DI LEVEL WILAYAH):
-
-{ctx}
-
-TULISKAN ANALISIS BAHASA INDONESIA FORMAL, STRUKTUR:
-1) Gambaran umum hasil wilayah (1 paragraf).
-2) Analisis jenis sekolah, umum, khusus serta indeks keseluruhan (2 paragraf).
-3) Penjelasan makna penyesuaian berbasis kecukupan sampel (1 paragraf, bahasa netral non-konfrontatif).
-4) Rekomendasi program 3–5 tahun (2 paragraf, konkret dan dapat dieksekusi).
-
-ATURAN:
-- Jangan memakai label eksplisit "rendah/sedang/tinggi".
-- Gunakan frasa netral: "memerlukan penguatan", "memerlukan konsolidasi", dsb.
-- Fokus pada Indeks FINAL WILAYAH (disesuaikan), bukan individu.
-"""
-    try:
-        resp = client.chat_completion(
-            model=LLM_MODEL_NAME,
-            messages=[{"role":"system","content":system_prompt},{"role":"user","content":user_prompt}],
-            max_tokens=1100,
-            temperature=0.25,
-            top_p=0.9,
-        )
-        text = resp.choices[0].message.content.strip()
-        return text if text else "LLM mengembalikan respon kosong."
-    except Exception as e:
-        return f"⚠️ Error saat memanggil LLM: {repr(e)}"
-
-def generate_word_report(wilayah, summary_jenis, agg_total, agg_jenis, analysis_text):
+_def generate_word_report(wilayah, summary_jenis, agg_total, agg_jenis, analysis_text):
     doc = Document()
     doc.add_heading(f"Laporan IPLM — {wilayah}", level=1)
 
     doc.add_heading("Ringkasan Dashboard", level=2)
 
-    k = compute_dashboard_kpis(summary_jenis, agg_total, agg_jenis)
-    doc.add_paragraph(f"Indeks IPLM FINAL (Disesuaikan): {k['final_all']:.2f} (rata-rata 3 jenis, tetap ÷3; missing=0)")
-    doc.add_paragraph(f"Indeks Dasar (Tanpa Penyesuaian): {k['dasar_all']:.2f} (rata-rata 3 jenis, tetap ÷3; missing=0)")
-    doc.add_paragraph(f"Cakupan Sampel (berdasarkan target 68%): {k['cakupan_pct']:.0f}% (min(total_terkumpul/target_68, 1.0))")
-    doc.add_paragraph(f"Penyesuaian Nilai (rata-rata): {k['dampak']:.2f} poin (faktor penyesuaian mean: {k['faktor_mean']:.3f})")
+    # KPI hanya FINAL & DASAR (cakupan + penyesuaian dipindah ke tabel ringkasan)
+    k_final = 0.0
+    k_dasar = 0.0
+    try:
+        if summary_jenis is not None and (not summary_jenis.empty):
+            row_all = summary_jenis[summary_jenis["Jenis"].astype(str).str.lower() == "keseluruhan"]
+            if not row_all.empty:
+                k_final = float(pd.to_numeric(row_all["Indeks_Final_Disesuaikan_0_100"], errors="coerce").fillna(0).iloc[0])
+                k_dasar = float(pd.to_numeric(row_all["Indeks_Dasar_0_100"], errors="coerce").fillna(0).iloc[0])
+    except Exception:
+        k_final, k_dasar = 0.0, 0.0
 
-    doc.add_paragraph("Ringkasan (Jenis + Keseluruhan):")
+    doc.add_paragraph(f"Indeks IPLM FINAL (Disesuaikan): {k_final:.2f} (rata-rata 3 jenis, tetap ÷3; missing=0)")
+    doc.add_paragraph(f"Indeks Dasar (Tanpa Penyesuaian): {k_dasar:.2f} (rata-rata 3 jenis, tetap ÷3; missing=0)")
+
+    doc.add_paragraph("Ringkasan (Jenis + Keseluruhan) — termasuk Pop/Target68/Terkumpul/Coverage + Penyesuaian Poin:")
     show = summary_jenis.copy()
+
     preferred = [
-        "Jenis","Jumlah_Wilayah","Total_Perpus",
-        "Rata2_dim_kepatuhan","Rata2_dim_kinerja",
-        "Indeks_Final_Disesuaikan_0_100"
+        "Jenis", "Jumlah_Wilayah", "Total_Perpus",
+        "Pop_Total_Jenis", "Target68_Total_Jenis", "Terkumpul_Jenis", "Coverage_Target68_Jenis_%",
+        "Rata2_dim_kepatuhan", "Rata2_dim_kinerja",
+        "Indeks_Dasar_0_100", "Indeks_Final_Disesuaikan_0_100", "Penyesuaian_Poin"
     ]
     show = show[[c for c in preferred if c in show.columns]]
 
@@ -1512,15 +1490,17 @@ def generate_word_report(wilayah, summary_jenis, agg_total, agg_jenis, analysis_
             v = row[c]
             if pd.isna(v):
                 cells[i].text = ""
+            elif isinstance(v, (int, np.integer)):
+                cells[i].text = str(int(v))
             elif isinstance(v, (float, np.floating)):
-                if "Indeks" in c:
+                if "Coverage" in c:
                     cells[i].text = f"{float(v):.2f}"
                 elif "Rata2_" in c:
                     cells[i].text = f"{float(v):.3f}"
+                elif "Indeks" in c or "Penyesuaian" in c:
+                    cells[i].text = f"{float(v):.2f}"
                 else:
-                    cells[i].text = f"{float(v):.0f}"
-            elif isinstance(v, (int, np.integer)):
-                cells[i].text = str(int(v))
+                    cells[i].text = f"{float(v):.2f}"
             else:
                 cells[i].text = str(v)
 
@@ -1568,7 +1548,9 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
         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 di repo/server.")
 
+        # =========================
         # FILTER ANALISIS (df_all)
+        # =========================
         df = df_all.copy()
         if prov_value and prov_value != "(Semua)":
             df = df[df["PROV_DISP"] == prov_value]
@@ -1580,21 +1562,51 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
         if df.empty:
             return _empty_outputs("Tidak ada data untuk filter ini.")
 
-        # ==== PIPELINE BARU (PATCH: faktor 68% PER JENIS) ====
-        faktor_wilayah_jenis = build_faktor_wilayah_jenis(df, pop_kab, pop_prov, pop_khusus, kew_value or "(Semua)")
-        agg_jenis_full = build_agg_wilayah_jenis(df, faktor_wilayah_jenis, kew_value or "(Semua)")
-        agg_total      = build_agg_wilayah_total_from_jenis(agg_jenis_full, faktor_wilayah_jenis, kew_value or "(Semua)")
-        summary_jenis  = build_summary_per_jenis(agg_jenis_full, agg_total)
-        
-        # verif_total: kalau kamu masih mau tabel verifikasi, sekarang logikanya sebaiknya per jenis
-        # tapi karena request kamu fokus ringkasan + agg_total, verif_total bisa tetap pakai "ringkas" dari faktor_wilayah_jenis
-        # (opsional) kita bikin verif_total sederhana dari faktor_wilayah_jenis:
-        verif_total = build_verif_jenis(faktor_wilayah_jenis, kew_value or "(Semua)")
-        detail_view  = attach_final_to_detail(df, agg_total, meta, kew_value or "(Semua)")
+        # ==================================================
+        # PIPELINE BARU (FAKTOR 68% PER JENIS)
+        # ==================================================
+        faktor_wilayah_jenis = build_faktor_wilayah_jenis(
+            df,
+            pop_kab,
+            pop_prov,
+            pop_khusus,
+            kew_value or "(Semua)"
+        )
 
+        agg_jenis_full = build_agg_wilayah_jenis(
+            df,
+            faktor_wilayah_jenis,
+            kew_value or "(Semua)"
+        )
 
-        # ====== UPDATE SESUAI PERMINTAAN (UI ONLY) ======
+        agg_total = build_agg_wilayah_total_from_jenis(
+            agg_jenis_full,
+            faktor_wilayah_jenis,
+            kew_value or "(Semua)"
+        )
+
+        summary_jenis = build_summary_per_jenis(
+            agg_jenis_full,
+            agg_total,
+            faktor_wilayah_jenis=faktor_wilayah_jenis
+        )
+
+        verif_total = build_verif_jenis(
+            faktor_wilayah_jenis,
+            kew_value or "(Semua)"
+        )
+
+        detail_view = attach_final_to_detail(
+            df,
+            agg_total,
+            meta,
+            kew_value or "(Semua)"
+        )
+
+        # ==================================================
+        # UPDATE SESUAI PERMINTAAN (UI ONLY)
         # Tabel Agregat Wilayah × Jenis cukup sampai Indeks_Dasar_Agregat_0_100
+        # ==================================================
         if agg_jenis_full is None or agg_jenis_full.empty:
             agg_jenis_view = agg_jenis_full
         else:
@@ -1605,14 +1617,16 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
                 label_name,
                 "Jenis",
                 "Jumlah",
-                "Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
-                "Rata2_dim_kepatuhan","Rata2_dim_kinerja",
+                "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()
 
+        # =========================
         # FILTER RAW DOWNLOAD (df_raw)
+        # =========================
         raw = df_raw.copy()
         if prov_value and prov_value != "(Semua)":
             raw = raw[raw["PROV_DISP"] == prov_value]
@@ -1621,7 +1635,9 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
         if kew_value and kew_value != "(Semua)":
             raw = raw[raw["KEW_NORM"] == kew_value]
 
+        # =========================
         # Bell curve per JENIS (per entitas)
+        # =========================
         if detail_view is None or detail_view.empty:
             fig_sekolah = _make_bell_curve(pd.DataFrame(), "Indeks_Dasar_0_100", "Bell Curve — Jenis: Sekolah", min_points=2)
             fig_umum    = _make_bell_curve(pd.DataFrame(), "Indeks_Dasar_0_100", "Bell Curve — Jenis: Umum", min_points=2)
@@ -1633,15 +1649,32 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
 
             def _fig_jenis_ent(jenis_key: str, judul: str):
                 d = detail_view[detail_view["Jenis"].astype(str).str.lower() == jenis_key].copy()
-                return _make_bell_curve(d, xcol=xcol_ent, title=judul, label_col=label_col_e, hover_cols=hover_cols_e, min_points=2)
+                return _make_bell_curve(
+                    d,
+                    xcol=xcol_ent,
+                    title=judul,
+                    label_col=label_col_e,
+                    hover_cols=hover_cols_e,
+                    min_points=2
+                )
 
             fig_sekolah = _fig_jenis_ent("sekolah", "Bell Curve — Jenis: Sekolah (Indeks per Entitas)")
             fig_umum    = _fig_jenis_ent("umum",    "Bell Curve — Jenis: Umum (Indeks per Entitas)")
             fig_khusus  = _fig_jenis_ent("khusus",  "Bell Curve — Jenis: Khusus (Indeks per Entitas)")
 
-        # KPI markdown (FINAL sumber Ringkasan)
-        kpi_md = build_kpi_markdown(summary_jenis, agg_total, agg_jenis_full, faktor_wilayah_jenis=faktor_wilayah_jenis)
+        # =========================
+        # KPI markdown (FINAL sumber Ringkasan) — hanya 2 kartu
+        # =========================
+        kpi_md = build_kpi_markdown(
+            summary_jenis,
+            agg_total,
+            agg_jenis_full,
+            faktor_wilayah_jenis=faktor_wilayah_jenis
+        )
 
+        # =========================
+        # SAVE OUTPUTS (Excel + Word)
+        # =========================
         tmpdir = tempfile.mkdtemp()
         prov_slug = (_canon(prov_value or "SEMUA").upper() or "SEMUA")
         kab_slug  = (_canon(kab_value  or "SEMUA").upper() or "SEMUA")
@@ -1660,9 +1693,22 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
         verif_total.to_excel(p_verif, index=False)
 
         wilayah_txt = kab_value if (kab_value and kab_value != "(Semua)") else (prov_value if (prov_value and prov_value != "(Semua)") else "Nasional/All")
-        analysis_text = generate_llm_analysis(summary_jenis, agg_total, verif_total, wilayah_txt, kew_value or "(Semua)")
 
-        word_path = generate_word_report(wilayah_txt, summary_jenis, agg_total, agg_jenis_full, analysis_text)
+        analysis_text = generate_llm_analysis(
+            summary_jenis,
+            agg_total,
+            verif_total,
+            wilayah_txt,
+            kew_value or "(Semua)"
+        )
+
+        word_path = generate_word_report(
+            wilayah_txt,
+            summary_jenis,
+            agg_total,
+            agg_jenis_full,
+            analysis_text
+        )
 
         msg = (
             f"✅ Selesai: raw={len(raw)} | entitas={len(detail_view)} | wilayah(keseluruhan)={len(agg_total)} | "
@@ -1699,9 +1745,9 @@ def ui_load(force=False):
     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  = "PROVINSI" if "PROVINSI" in kew_choices else ("KAB/KOTA" if "KAB/KOTA" in kew_choices else "(Semua)")
+    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 = "PROVINSI" if "PROVINSI" in kew_choices else ("KAB/KOTA" if "KAB/KOTA" in kew_choices else "(Semua)")
 
     return (
         df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info,
@@ -1734,13 +1780,13 @@ with gr.Blocks() as demo:
 **FIX UTAMA (konsistensi nilai):**
 - **Agregat Wilayah (Keseluruhan) = rata-rata 3 jenis (sekolah+umum+khusus) ÷ 3 (missing=0, tetap ÷3)**
 - Ringkasan selalu tampil **sekolah, umum, khusus, keseluruhan** (walau 0)
-- KPI FINAL dashboard sumber dari Ringkasan
+- KPI dashboard hanya menampilkan **FINAL** dan **DASAR**
+- **Cakupan (Target 68%) dan Penyesuaian Poin dipindahkan ke tabel Ringkasan**
 - Download Data Mentah = RAW hasil filter
-- Kecukupan Sampel 68%: tanpa angka koma
 
 **UPDATE (tampilan):**
-- target_total_68 & pop_total di faktor_wilayah = bilangan bulat
-- coverage_total_% = 2 desimal
+- Tabel Ringkasan memuat: Pop/Target68/Terkumpul/Coverage per jenis + Penyesuaian Poin
+- Tabel Agregat Wilayah memuat: Pop/Target68/Terkumpul per jenis + Total Update (sum 3 jenis)
 - Tabel "Agregat Wilayah × Jenis" ditampilkan hanya sampai Indeks_Dasar_Agregat_0_100
 """)
 
@@ -1755,20 +1801,21 @@ with gr.Blocks() as demo:
 
     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_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 dashboard (hanya 2 kartu)
     kpi_out = gr.Markdown()
 
-    gr.Markdown("## Ringkasan (Jenis + Keseluruhan)")
+    gr.Markdown("## Ringkasan (Jenis + Keseluruhan) — Pop/Target68/Terkumpul/Coverage + Penyesuaian")
     out_summary = gr.DataFrame(interactive=False)
 
-    gr.Markdown("## Agregat Wilayah (Keseluruhan) — FIX: avg3 dari 3 jenis")
+    gr.Markdown("## Agregat Wilayah (Keseluruhan) — FIX: avg3 dari 3 jenis + Pop/Target68/Terkumpul (per jenis & total)")
     out_agg_total = gr.DataFrame(interactive=False)
 
     gr.Markdown("## Agregat Wilayah × Jenis (Sekolah, Umum, Khusus) — (ditampilkan sampai Indeks_Dasar_Agregat_0_100)")
@@ -1795,10 +1842,10 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         dl_summary = gr.DownloadButton(label="Download Ringkasan (.xlsx)")
-        dl_total   = gr.DownloadButton(label="Download Agregat Wilayah (.xlsx)")
-        dl_jenis   = gr.DownloadButton(label="Download Data Mentah (.xlsx)")
-        dl_detail  = gr.DownloadButton(label="Download Detail Entitas (.xlsx)")
-        dl_word    = gr.DownloadButton(label="Download Laporan Word (.docx)")
+        dl_total = gr.DownloadButton(label="Download Agregat Wilayah (.xlsx)")
+        dl_jenis = gr.DownloadButton(label="Download Data Mentah (.xlsx)")
+        dl_detail = gr.DownloadButton(label="Download Detail Entitas (.xlsx)")
+        dl_word = gr.DownloadButton(label="Download Laporan Word (.docx)")
 
     run_btn.click(
         fn=run_calc,