Update app.py

app.py

@@ -605,9 +605,17 @@ def load_default_files(force=False):
 
 # ============================================================
 # 6) FAKTOR WILAYAH (TOTAL) — hanya untuk faktor/target/pop/coverage
+#    UPDATE: total wilayah (pop_total & target_total_68) ditambah kontribusi POP_KHUSUS
+#            TANPA bikin kolom khusus baru (langsung dijumlahkan)
 # ============================================================
 
-def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_prov: pd.DataFrame, pop_khusus: pd.DataFrame, kew_value: str):
+def build_faktor_wilayah(
+    df_filtered: pd.DataFrame,
+    pop_kab: pd.DataFrame,
+    pop_prov: pd.DataFrame,
+    pop_khusus: pd.DataFrame,
+    kew_value: str
+):
     if df_filtered is None or df_filtered.empty:
         return pd.DataFrame()
 
@@ -622,7 +630,17 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total"
         pop_field = "Pop_Total"
         name_field = "Kab_Kota_Label"
-        khusus_group_key = "kab_key"
+
+        # khs (kab) -> pakai kab_key
+        khs = pop_khusus.copy() if (pop_khusus is not None and not pop_khusus.empty) else pd.DataFrame()
+        if not khs.empty and "kab_key" in khs.columns:
+            khs_kab = khs.groupby("kab_key", as_index=False).agg({
+                "Target68_Total_Jenis": "sum",
+                "Pop_Total_Jenis": "sum"
+            }).set_index("kab_key")
+        else:
+            khs_kab = pd.DataFrame().set_index(pd.Index([]))
+
     elif "PROV" in kew_norm:
         key_col = "prov_key"
         label_col = "PROV_DISP"
@@ -631,7 +649,17 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total_Prov"
         pop_field = "Pop_Total_Prov"
         name_field = "Provinsi_Label"
-        khusus_group_key = "prov_key"
+
+        # khs (prov) -> agregasi dari pop_khusus ke prov_key
+        khs = pop_khusus.copy() if (pop_khusus is not None and not pop_khusus.empty) else pd.DataFrame()
+        if not khs.empty and "prov_key" in khs.columns:
+            khs_prov = khs.groupby("prov_key", as_index=False).agg({
+                "Target68_Total_Jenis": "sum",
+                "Pop_Total_Jenis": "sum"
+            }).set_index("prov_key")
+        else:
+            khs_prov = pd.DataFrame().set_index(pd.Index([]))
+
     else:
         key_col = "kab_key"
         label_col = "KAB_DISP"
@@ -640,22 +668,59 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total"
         pop_field = "Pop_Total"
         name_field = "Kab_Kota_Label"
-        khusus_group_key = "kab_key"
+
+        khs = pop_khusus.copy() if (pop_khusus is not None and not pop_khusus.empty) else pd.DataFrame()
+        if not khs.empty and "kab_key" in khs.columns:
+            khs_kab = khs.groupby("kab_key", as_index=False).agg({
+                "Target68_Total_Jenis": "sum",
+                "Pop_Total_Jenis": "sum"
+            }).set_index("kab_key")
+        else:
+            khs_kab = pd.DataFrame().set_index(pd.Index([]))
 
     base = df.groupby([key_col, label_col], dropna=False).agg(
         n_total=("Indeks_Dasar_0_100", "size"),
     ).reset_index().rename(columns={key_col: "group_key", label_col: label_name})
 
-    # --- ambil target & pop dari POP_KAB / POP_PROV (existing) ---
     target_vals, pop_vals, label_fix = [], [], []
     for _, r in base.iterrows():
         gk = r["group_key"]
+
+        # ===== ambil target/pop dari POP utama (kab/prov) =====
         if gk in pop.index:
             target_total = pop.loc[gk, target_field] if target_field in pop.columns else np.nan
             pop_total = pop.loc[gk, pop_field] if pop_field in pop.columns else np.nan
             nm = pop.loc[gk, name_field] if name_field in pop.columns else r[label_name]
         else:
             target_total, pop_total, nm = np.nan, np.nan, r[label_name]
+
+        # ===== UPDATE: tambah target/pop dari POP_KHUSUS (langsung dijumlah) =====
+        add_target, add_pop = 0.0, 0.0
+        if "PROV" in kew_norm:
+            if "khs_prov" in locals() and (gk in khs_prov.index):
+                add_target = khs_prov.loc[gk, "Target68_Total_Jenis"] if "Target68_Total_Jenis" in khs_prov.columns else 0.0
+                add_pop = khs_prov.loc[gk, "Pop_Total_Jenis"] if "Pop_Total_Jenis" in khs_prov.columns else 0.0
+        else:
+            if "khs_kab" in locals() and (gk in khs_kab.index):
+                add_target = khs_kab.loc[gk, "Target68_Total_Jenis"] if "Target68_Total_Jenis" in khs_kab.columns else 0.0
+                add_pop = khs_kab.loc[gk, "Pop_Total_Jenis"] if "Pop_Total_Jenis" in khs_kab.columns else 0.0
+
+        try:
+            add_target = float(pd.to_numeric(add_target, errors="coerce") or 0.0)
+        except Exception:
+            add_target = 0.0
+        try:
+            add_pop = float(pd.to_numeric(add_pop, errors="coerce") or 0.0)
+        except Exception:
+            add_pop = 0.0
+
+        # jumlahkan (kalau NaN -> treat 0 dulu)
+        t0 = float(pd.to_numeric(target_total, errors="coerce")) if pd.notna(target_total) else 0.0
+        p0 = float(pd.to_numeric(pop_total, errors="coerce")) if pd.notna(pop_total) else 0.0
+
+        target_total = t0 + add_target
+        pop_total = p0 + add_pop
+
         target_vals.append(target_total)
         pop_vals.append(pop_total)
         label_fix.append(nm)
@@ -664,59 +729,31 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
     base["target_total_68"] = pd.to_numeric(pd.Series(target_vals), errors="coerce")
     base["pop_total"] = pd.to_numeric(pd.Series(pop_vals), errors="coerce")
 
-    # fallback pop dari target kalau pop kosong
+    # fallback pop_total bila kosong tapi target ada
     m = base["pop_total"].isna() & base["target_total_68"].notna() & (base["target_total_68"] > 0)
     base.loc[m, "pop_total"] = base.loc[m, "target_total_68"] / float(FALLBACK_TARGET_RATIO)
 
-    # =========================================================
-    # ✅ UPDATE LO: Tambahkan komponen KHUSUS dari POP_KHUSUS
-    # target_total_68 += target_khusus
-    # pop_total       += pop_khusus
-    # =========================================================
-    if (pop_khusus is not None) and (not pop_khusus.empty):
-        pk = pop_khusus.copy()
-
-        # pastikan numeric
-        pk["Target68_Total_Jenis"] = pd.to_numeric(pk.get("Target68_Total_Jenis", 0), errors="coerce").fillna(0.0)
-        pk["Pop_Total_Jenis"]      = pd.to_numeric(pk.get("Pop_Total_Jenis", 0), errors="coerce").fillna(0.0)
-
-        if khusus_group_key in pk.columns:
-            add_df = pk.groupby(khusus_group_key, as_index=False).agg(
-                add_target=("Target68_Total_Jenis", "sum"),
-                add_pop=("Pop_Total_Jenis", "sum"),
-            ).rename(columns={khusus_group_key: "group_key"})
-
-            base = base.merge(add_df, on="group_key", how="left")
-            base["add_target"] = pd.to_numeric(base.get("add_target", 0), errors="coerce").fillna(0.0)
-            base["add_pop"]    = pd.to_numeric(base.get("add_pop", 0), errors="coerce").fillna(0.0)
-
-            base["target_total_68"] = pd.to_numeric(base.get("target_total_68", 0), errors="coerce").fillna(0.0) + base["add_target"]
-            base["pop_total"]       = pd.to_numeric(base.get("pop_total", 0), errors="coerce").fillna(0.0) + base["add_pop"]
-
-            base = base.drop(columns=[c for c in ["add_target", "add_pop"] if c in base.columns])
-
-    # faktor penyesuaian pakai TOTAL baru
+    # faktor pakai target_total_68 yang SUDAH ditambah khusus
     base["faktor_penyesuaian"] = [
         faktor_penyesuaian_total(n, t)
         for n, t in zip(
             pd.to_numeric(base["n_total"], errors="coerce").fillna(0).astype(float).tolist(),
-            pd.to_numeric(base["target_total_68"], errors="coerce").fillna(0).astype(float).tolist()
+            pd.to_numeric(base["target_total_68"], errors="coerce").tolist()
         )
     ]
 
-    # coverage pakai TOTAL baru
     base["coverage_total_%"] = [
         (safe_div(n, p) * 100) if (p is not None and not pd.isna(p) and float(p) > 0) else np.nan
         for n, p in zip(
             pd.to_numeric(base["n_total"], errors="coerce").fillna(0).astype(float).tolist(),
-            pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).astype(float).tolist()
+            base["pop_total"].tolist()
         )
     ]
 
-    # ===== display fix (sesuai permintaan lo sebelumnya) =====
+    # ====== UPDATE SESUAI PERMINTAAN (DISPLAY) ======
     base["target_total_68"] = pd.to_numeric(base["target_total_68"], errors="coerce").fillna(0).round(0).astype(int)
-    base["pop_total"]       = pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).round(0).astype(int)
-    base["coverage_total_%"]= pd.to_numeric(base["coverage_total_%"], errors="coerce").fillna(0.0).round(2)
+    base["pop_total"] = pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).round(0).astype(int)
+    base["coverage_total_%"] = pd.to_numeric(base["coverage_total_%"], errors="coerce").fillna(0.0).round(2)
     base["faktor_penyesuaian"] = pd.to_numeric(base["faktor_penyesuaian"], errors="coerce").fillna(1.0).round(3)
 
     return base
@@ -726,148 +763,110 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
 # 7) AGREGAT WILAYAH × JENIS (Final pakai faktor wilayah)
 # ============================================================
 
-def build_agg_wilayah_total_from_jenis(
-    agg_jenis: pd.DataFrame,
-    faktor_wilayah: pd.DataFrame,
-    pop_khusus: pd.DataFrame,          # <-- PATCH: tambah
-    kew_value: str
-):
-    if agg_jenis is None or agg_jenis.empty:
+def build_agg_wilayah_jenis(df_filtered: pd.DataFrame, faktor_wilayah: pd.DataFrame, pop_khusus: pd.DataFrame, kew_value: str):
+    if df_filtered is None or df_filtered.empty:
         return pd.DataFrame()
 
     kew_norm = str(kew_value or "").upper()
-    label_name = "Kab/Kota" if ("KAB" in kew_norm or "KOTA" in kew_norm) else ("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")
+    df = df_filtered.copy()
 
-    cols_need = [
-        "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",
-    ]
-    cols_present = [c for c in cols_need if c in a.columns]
+    if "KAB" in kew_norm or "KOTA" in kew_norm:
+        key_col = "kab_key"
+        label_col = "KAB_DISP"
+        label_name = "Kab/Kota"
+    elif "PROV" in kew_norm:
+        key_col = "prov_key"
+        label_col = "PROV_DISP"
+        label_name = "Provinsi"
+    else:
+        key_col = "kab_key"
+        label_col = "KAB_DISP"
+        label_name = "Kab/Kota"
 
-    full = full.merge(
-        a[["group_key", label_name, "Jenis"] + cols_present],
-        on=["group_key", label_name, "Jenis"],
-        how="left"
-    )
+    df = df[df["_dataset"].isin(["sekolah", "umum", "khusus"])].copy()
+    if df.empty:
+        return pd.DataFrame()
 
-    # missing=0 (kunci avg3 tetap ÷3)
-    for c in cols_present:
-        full[c] = pd.to_numeric(full[c], errors="coerce").fillna(0.0)
+    agg = 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()
 
-    # keseluruhan wilayah = avg3 dari 3 jenis
-    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"),
-    )
+    agg = agg.rename(columns={key_col: "group_key", label_col: label_name, "_dataset": "Jenis"})
 
-    # tempel faktor/target/pop/coverage (informasi verifikasi)
-    if faktor_wilayah is not None and not faktor_wilayah.empty:
+    # faktor wilayah (sama untuk semua jenis)
+    if faktor_wilayah is None or faktor_wilayah.empty:
+        agg["faktor_penyesuaian_wilayah"] = 1.0
+        agg["target_total_68"] = np.nan
+        agg["pop_total"] = np.nan
+        agg["coverage_total_%"] = np.nan
+    else:
         fw = faktor_wilayah.copy()
-        keep_fw = ["group_key", label_name, "target_total_68", "pop_total", "faktor_penyesuaian", "coverage_total_%"]
-        keep_fw = [c for c in keep_fw if c in fw.columns]
-        out = out.merge(fw[keep_fw], on=["group_key", label_name], how="left")
+        keep = ["group_key", label_name, "faktor_penyesuaian", "target_total_68", "pop_total", "coverage_total_%"]
+        keep = [c for c in keep if c in fw.columns]
+        fw = fw[keep].rename(columns={"faktor_penyesuaian": "faktor_penyesuaian_wilayah"})
+        agg = agg.merge(fw, on=["group_key", label_name], how="left")
+        agg["faktor_penyesuaian_wilayah"] = pd.to_numeric(agg["faktor_penyesuaian_wilayah"], errors="coerce").fillna(1.0)
+
+    agg["faktor_penyesuaian"] = agg["faktor_penyesuaian_wilayah"]
+    agg["Indeks_Final_Agregat_0_100"] = pd.to_numeric(agg["Indeks_Dasar_Agregat_0_100"], errors="coerce").fillna(0.0) * agg["faktor_penyesuaian"]
+
+    # target/pop/coverage per jenis (hanya khusus, dari POP_KHUSUS) — supaya tidak tampil null -> isi 0
+    agg["target_total_68_jenis"] = 0.0
+    agg["pop_total_jenis"] = 0.0
+    agg["coverage_jenis"] = 0.0
+
+    if (pop_khusus is not None) and (not pop_khusus.empty) and ("KAB" in kew_norm or "KOTA" in kew_norm or kew_norm in {"(SEMUA)", "(SEMUA)".upper()}):
+        pk = pop_khusus.set_index("kab_key")
+        for i, r in agg.iterrows():
+            if str(r.get("Jenis", "")).lower() != "khusus":
+                continue
+            gk = r.get("group_key", None)
+            if gk in pk.index:
+                t = pk.loc[gk, "Target68_Total_Jenis"] if "Target68_Total_Jenis" in pk.columns else np.nan
+                p = pk.loc[gk, "Pop_Total_Jenis"] if "Pop_Total_Jenis" in pk.columns else np.nan
+                if pd.notna(t):
+                    agg.at[i, "target_total_68_jenis"] = float(t)
+                if pd.notna(p):
+                    agg.at[i, "pop_total_jenis"] = float(p)
 
-    # =========================================================
-    # PATCH UTAMA:
-    # Tambahkan target_total_68 & pop_total dari POP_KHUSUS
-    # (tanpa kolom khusus tambahan)
-    # =========================================================
-    if (pop_khusus is not None) and (not pop_khusus.empty):
-        pk = pop_khusus.copy()
-
-        # pastikan numerik
-        for c in ["Target68_Total_Jenis", "Pop_Total_Jenis"]:
-            if c in pk.columns:
-                pk[c] = pd.to_numeric(pk[c], errors="coerce").fillna(0.0)
-
-        add_df = None
-
-        # KAB/KOTA: join langsung by kab_key == group_key
-        if ("KAB" in kew_norm or "KOTA" in kew_norm):
-            if "kab_key" in pk.columns:
-                add_df = pk.groupby("kab_key", as_index=False).agg(
-                    add_target=("Target68_Total_Jenis", "sum"),
-                    add_pop=("Pop_Total_Jenis", "sum"),
-                ).rename(columns={"kab_key": "group_key"})
-
-        # PROVINSI: agregasi by prov_key == group_key
-        elif ("PROV" in kew_norm):
-            if "prov_key" in pk.columns:
-                add_df = pk.groupby("prov_key", as_index=False).agg(
-                    add_target=("Target68_Total_Jenis", "sum"),
-                    add_pop=("Pop_Total_Jenis", "sum"),
-                ).rename(columns={"prov_key": "group_key"})
-
-        if add_df is not None and not add_df.empty:
-            out = out.merge(add_df, on="group_key", how="left")
-            out["add_target"] = pd.to_numeric(out.get("add_target", 0), errors="coerce").fillna(0.0)
-            out["add_pop"]    = pd.to_numeric(out.get("add_pop", 0), errors="coerce").fillna(0.0)
-
-            # tambah ke total existing (kalau belum ada kolomnya -> dianggap 0)
-            out["target_total_68"] = pd.to_numeric(out.get("target_total_68", 0), errors="coerce").fillna(0.0) + out["add_target"]
-            out["pop_total"]       = pd.to_numeric(out.get("pop_total", 0), errors="coerce").fillna(0.0) + out["add_pop"]
-
-            # opsional tapi konsisten: update coverage & faktor berdasarkan total baru (DI TABEL INI SAJA)
-            out["coverage_total_%"] = [
-                (safe_div(n, p) * 100) 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(out.get("n_total", 0), errors="coerce").fillna(0).astype(float).tolist(),
-                    pd.to_numeric(out.get("pop_total", 0), errors="coerce").fillna(0).astype(float).tolist()
-                )
-            ]
-            out["faktor_penyesuaian"] = [
-                faktor_penyesuaian_total(n, t)
-                for n, t in zip(
-                    pd.to_numeric(out.get("n_total", 0), errors="coerce").fillna(0).astype(float).tolist(),
-                    pd.to_numeric(out.get("target_total_68", 0), errors="coerce").fillna(0).astype(float).tolist()
-                )
-            ]
+    agg["target_total_68_jenis"] = pd.to_numeric(agg["target_total_68_jenis"], errors="coerce").fillna(0.0)
+    agg["pop_total_jenis"] = pd.to_numeric(agg["pop_total_jenis"], errors="coerce").fillna(0.0)
+
+    m_need_pop = (agg["pop_total_jenis"] <= 0) & (agg["target_total_68_jenis"] > 0)
+    agg.loc[m_need_pop, "pop_total_jenis"] = agg.loc[m_need_pop, "target_total_68_jenis"] / float(FALLBACK_TARGET_RATIO)
 
-            # bersihkan kolom helper
-            out = out.drop(columns=[c for c in ["add_target", "add_pop"] if c in out.columns])
+    m2 = agg["pop_total_jenis"] > 0
+    agg.loc[m2, "coverage_jenis"] = (pd.to_numeric(agg.loc[m2, "Jumlah"], errors="coerce").fillna(0.0) / agg.loc[m2, "pop_total_jenis"]) * 100.0
 
-    # rounding (yang sudah ada sebelumnya)
+    # rounding (tampilan konsisten)
     for c in [
         "Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
         "Rata2_dim_kepatuhan","Rata2_dim_kinerja"
     ]:
-        if c in out.columns:
-            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(3)
+        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_Wilayah_0_100"]:
-        if c in out.columns:
-            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
+    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)
 
-    if "faktor_penyesuaian" in out.columns:
-        out["faktor_penyesuaian"] = pd.to_numeric(out["faktor_penyesuaian"], errors="coerce").fillna(1.0).round(3)
+    for c in ["faktor_penyesuaian_wilayah","faktor_penyesuaian"]:
+        if c in agg.columns:
+            agg[c] = pd.to_numeric(agg[c], errors="coerce").fillna(1.0).round(3)
 
-    for c in ["target_total_68","pop_total","coverage_total_%"]:
-        if c in out.columns:
-            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0)
+    for c in ["target_total_68","pop_total","coverage_total_%","target_total_68_jenis","pop_total_jenis","coverage_jenis"]:
+        if c in agg.columns:
+            agg[c] = pd.to_numeric(agg[c], errors="coerce").fillna(0.0)
+
+    return agg
 
-    return out
 
 # ============================================================
 # 8) AGREGAT WILAYAH (KESELURUHAN) — RUMUS BARU: AVG3 dari 3 jenis
@@ -1496,17 +1495,6 @@ def generate_word_report(wilayah, summary_jenis, agg_total, agg_jenis, analysis_
 # 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, "Analisis belum tersedia."
-    )
-
 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:
@@ -1525,9 +1513,10 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
             return _empty_outputs("Tidak ada data untuk filter ini.")
 
         # ==== PIPELINE BARU (KUNCI KONSISTENSI) ====
+        # UPDATE: faktor_wilayah sekarang menambahkan POP_KHUSUS ke target_total_68 & pop_total (total wilayah)
         faktor_wilayah = build_faktor_wilayah(df, pop_kab, pop_prov, pop_khusus, kew_value or "(Semua)")
         agg_jenis_full = build_agg_wilayah_jenis(df, faktor_wilayah, pop_khusus, kew_value or "(Semua)")
-        agg_total      = build_agg_wilayah_total_from_jenis(agg_jenis_full, faktor_wilayah, pop_khusus, kew_value or "(Semua)")
+        agg_total      = build_agg_wilayah_total_from_jenis(agg_jenis_full, faktor_wilayah, kew_value or "(Semua)")
         summary_jenis  = build_summary_per_jenis(agg_jenis_full, agg_total)
         verif_total    = build_verif_total(faktor_wilayah)  # tanpa koma
         detail_view    = attach_final_to_detail(df, agg_total, meta, kew_value or "(Semua)")
@@ -1551,6 +1540,7 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
             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)":