try1

scripts/bigquery_analytical_layer.py

@@ -8,27 +8,32 @@ Filtering Order:
 3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps)
 4. Filter countries with ALL pillars (FIXED SET)
 5. Filter indicators with consistent presence across FIXED countries
-   → TIDAK menghapus baris year < max_start_year
+6. Determine SDG start year & assign framework (MDGs/SDGs) per ROW per year
-   → Semua baris tetap ada; label framework ditentukan di Step 6
+7. Verify no gaps
-6. Assign framework (MDGs/SDGs) per indicator PER ROW
+8. Calculate norm_value_1_100 per indicator per country (min-max, direction-aware)
-   → Indikator TIDAK di SDG_ONLY_KEYWORDS           → 'MDGs' selalu
-   → Indikator DI SDG_ONLY_KEYWORDS + year >= sdg_transition_year → 'SDGs'
-   → Indikator DI SDG_ONLY_KEYWORDS + year <  sdg_transition_year → 'MDGs'
-   → sdg_transition_year = min(actual_start_year) dari semua SDG-only indicators
-     yang lolos filter (= tahun pertama data SDG-only konsisten di semua countries)
-7. Verify no gaps (dari actual_start_year per indikator, bukan start_year global)
-8. Calculate norm_value_1_100 per indicator (min-max, direction-aware, global)
 9. Calculate YoY per indicator per country
 10. Analyze indicator availability by year
 11. Save analytical table
 
-FRAMEWORK LOGIC:
+NORMALISASI (Step 8):
-- sdg_transition_year dihitung SATU KALI dari actual_start_year SDG-only indicators
+- norm_value_1_100 = min-max normalisasi nilai raw per indikator, skala 1-100
-- Semua SDG-only indicators menggunakan sdg_transition_year yang SAMA
+- Direction-aware: lower_better diinvert sehingga nilai tinggi selalu = lebih baik
-  sehingga label berubah serentak di satu titik waktu
+- Normalisasi dilakukan GLOBAL per indikator (semua negara, semua tahun sekaligus)
-- Baris sebelum sdg_transition_year → 'MDGs' (data tetap ada, tidak dihapus)
+  sehingga nilai antar negara dan antar tahun tetap comparable
-- Baris mulai sdg_transition_year   → 'SDGs'
+- Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio
-- Indikator non-SDG-only            → 'MDGs' selalu
+
+FRAMEWORK LOGIC (Per-Row, bukan per indikator):
+- sdg_start_year dideteksi dari data: tahun pertama indikator FIES lengkap
+  di semua fixed countries (setelah Step 3-5 filter selesai)
+- Proxy deteksi sdg_start_year: HANYA FIES ("food insecurity", "food insecure")
+  Anemia TIDAK dipakai sebagai proxy karena datanya sudah ada sebelum era SDGs
+- Framework di-assign PER BARIS (per year), bukan per indikator:
+    * row['year'] >= sdg_start_year AND nama ada di SDG_INDICATOR_KEYWORDS -> 'SDGs'
+    * Selain itu -> 'MDGs'
+- Ini menangani indikator "shared" (anemia, stunting, wasting, undernourishment)
+  yang datanya ada sebelum SDGs:
+    * row lama (year < sdg_start_year) -> 'MDGs'
+    * row baru (year >= sdg_start_year) -> 'SDGs'
 """
 
 import pandas as pd
@@ -55,14 +60,17 @@ from google.cloud import bigquery
 
 
 # =============================================================================
-# SDG-ONLY INDICATOR KEYWORDS
+# SDG INDICATOR KEYWORDS
+# Daftar nama indikator (lowercase) yang masuk SDG framework.
+# Indikator ini akan di-assign 'SDGs' untuk baris dengan year >= sdg_start_year,
+# dan 'MDGs' untuk baris dengan year < sdg_start_year.
 # =============================================================================
-# Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini.
-# Indikator di set ini → 'SDGs' mulai dari sdg_transition_year.
-# Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' di semua tahun.
 
-SDG_ONLY_KEYWORDS = frozenset([
+SDG_INDICATOR_KEYWORDS = frozenset([
-    # TARGET 2.1.2 — FIES (SDGs only)
+    # TARGET 2.1.1 — Prevalence of undernourishment (shared: ada sebelum SDGs)
+    "prevalence of undernourishment (percent) (3-year average)",
+    "number of people undernourished (million) (3-year average)",
+    # TARGET 2.1.2 — FIES (SDGs only — murni baru di era SDGs)
     "prevalence of severe food insecurity in the total population (percent) (3-year average)",
     "prevalence of severe food insecurity in the male adult population (percent) (3-year average)",
     "prevalence of severe food insecurity in the female adult population (percent) (3-year average)",
@@ -75,14 +83,45 @@ SDG_ONLY_KEYWORDS = frozenset([
     "number of moderately or severely food insecure people (million) (3-year average)",
     "number of moderately or severely food insecure male adults (million) (3-year average)",
     "number of moderately or severely food insecure female adults (million) (3-year average)",
-    # TARGET 2.2.3 — Anaemia (SDGs only)
+    # TARGET 2.2.1 — Stunting (shared: ada sebelum SDGs)
+    "percentage of children under 5 years of age who are stunted (modelled estimates) (percent)",
+    "number of children under 5 years of age who are stunted (modeled estimates) (million)",
+    # TARGET 2.2.2 — Wasting & Overweight (shared: ada sebelum SDGs)
+    "percentage of children under 5 years affected by wasting (percent)",
+    "number of children under 5 years affected by wasting (million)",
+    "percentage of children under 5 years of age who are overweight (modelled estimates) (percent)",
+    "number of children under 5 years of age who are overweight (modeled estimates) (million)",
+    # TARGET 2.2.3 — Anaemia (shared: data ada sebelum SDGs, listed here agar
+    # baris >= sdg_start_year di-assign 'SDGs')
     "prevalence of anemia among women of reproductive age (15-49 years) (percent)",
     "number of women of reproductive age (15-49 years) affected by anemia (million)",
 ])
 
+# =============================================================================
+# SDG ERA PROXY KEYWORDS
+# HANYA indikator yang MURNI baru di era SDGs (FIES saja).
+# Dipakai untuk mendeteksi sdg_start_year dari data.
+#
+# PENTING — Anemia/anaemia TIDAK dipakai sebagai proxy:
+#   Data anemia sudah ada sebelum era SDGs sehingga actual_start_year-nya
+#   lebih awal dari sdg_start_year. Jika dipakai sebagai proxy, sdg_start_year
+#   akan terdeteksi terlalu awal dan seluruh baris anemia akan menjadi 'SDGs'.
+#   FIES adalah satu-satunya indikator yang benar-benar murni baru di era SDGs
+#   dan dapat dipakai sebagai penanda tahun mulainya era SDGs.
+# =============================================================================
+_SDG_ERA_PROXY_KEYWORDS = frozenset([
+    "food insecurity",
+    "food insecure",
+])
+
 # =============================================================================
 # THRESHOLD KONDISI (fixed absolute, skala 1-100)
 # =============================================================================
+# Digunakan untuk assign kondisi di analysis_layer.
+# Didefinisikan di sini agar konsisten antara kedua file.
+# bad      : norm_value_1_100 < THRESHOLD_BAD
+# good     : norm_value_1_100 > THRESHOLD_GOOD
+# moderate : di antara keduanya
 
 THRESHOLD_BAD  = 40.0
 THRESHOLD_GOOD = 60.0
@@ -91,6 +130,8 @@ THRESHOLD_GOOD = 60.0
 def assign_condition(norm_value_1_100: float) -> str:
     """
     Assign kondisi berdasarkan norm_value_1_100 (skala 1-100, sudah direction-aware).
+    Nilai tinggi selalu berarti lebih baik (lower_better sudah diinvert).
+
     Returns: 'good' / 'moderate' / 'bad'
     """
     if pd.isna(norm_value_1_100):
@@ -102,6 +143,44 @@ def assign_condition(norm_value_1_100: float) -> str:
     return 'moderate'
 
 
+def assign_framework_per_row(
+    indicator_name: str,
+    year: int,
+    sdg_start_year: int,
+) -> str:
+    """
+    Tentukan framework (MDGs/SDGs) per BARIS (per row year), bukan per indikator.
+
+    Logic:
+    - 'SDGs' jika KEDUA kondisi terpenuhi:
+        1. Nama indikator ada di SDG_INDICATOR_KEYWORDS
+        2. year (tahun baris ini) >= sdg_start_year
+    - 'MDGs' untuk semua kasus lain.
+
+    Mengapa per row, bukan per indikator?
+    Indikator "shared" seperti anemia, stunting, wasting, undernourishment
+    memiliki data yang ada SEBELUM era SDGs dimulai. Jika assign dilakukan
+    per indikator menggunakan actual_start_year, indikator-indikator ini
+    akan selalu di-assign 'MDGs' karena actual_start_year < sdg_start_year.
+    Dengan assign per row menggunakan year baris:
+        - baris lama (year < sdg_start_year) -> 'MDGs' (benar: belum era SDGs)
+        - baris baru (year >= sdg_start_year) -> 'SDGs' (benar: sudah era SDGs)
+
+    Contoh anemia (sdg_start_year = 2016):
+        - row year=2013 -> 'MDGs'
+        - row year=2014 -> 'MDGs'
+        - row year=2015 -> 'MDGs'
+        - row year=2016 -> 'SDGs'
+        - row year=2017 -> 'SDGs'
+        - ...
+    """
+    name_lower  = str(indicator_name).lower().strip()
+    in_sdg_list = name_lower in SDG_INDICATOR_KEYWORDS
+    if in_sdg_list and int(year) >= sdg_start_year:
+        return 'SDGs'
+    return 'MDGs'
+
+
 # =============================================================================
 # ANALYTICAL LAYER CLASS
 # =============================================================================
@@ -115,17 +194,14 @@ class AnalyticalLayerLoader:
         indicator_id, indicator_name, direction, framework,
         pillar_id, pillar_name,
         time_id, year, value,
-        norm_value_1_100,
+        norm_value_1_100,   <- min-max norm per indikator, skala 1-100, direction-aware
         yoy_change, yoy_pct
 
-    FRAMEWORK LOGIC:
+    Catatan framework:
-    - Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' di SEMUA tahun
+        Framework di-assign PER BARIS (per year), sehingga indikator shared
-    - Indikator DI SDG_ONLY_KEYWORDS:
+        seperti anemia dapat memiliki framework berbeda di baris yang berbeda:
-        year <  sdg_transition_year → 'MDGs'  (data tetap ada, tidak dihapus)
+        - baris sebelum sdg_start_year -> 'MDGs'
-        year >= sdg_transition_year → 'SDGs'
+        - baris sejak sdg_start_year   -> 'SDGs'
-    - sdg_transition_year = min(actual_start_year) dari semua SDG-only indicators
-      yang lolos filter Step 3-5. Semua SDG-only indicators menggunakan
-      sdg_transition_year yang SAMA agar label berubah serentak.
     """
 
     def __init__(self, client: bigquery.Client):
@@ -138,13 +214,13 @@ class AnalyticalLayerLoader:
         self.df_country   = None
         self.df_pillar    = None
 
-        self.selected_country_ids    = None
+        self.selected_country_ids = None
-        self.indicator_max_start_map = {}   # indicator_id → max_start_year (dari Step 5)
-        self.sdg_transition_year     = None # tahun SDGs mulai berlaku (dari Step 6)
 
         self.start_year    = 2013
         self.end_year      = None
-        self.baseline_year = 2023
+        self.baseline_year = 2023   # hardcode per syarat dosen (tahun terlengkap)
+
+        self.sdg_start_year = None
 
         self.pipeline_metadata = {
             'source_class'      : self.__class__.__name__,
@@ -230,6 +306,15 @@ class AnalyticalLayerLoader:
         self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES")
         self.logger.info("=" * 80)
 
+        # Filter single years only (is_year_range == False)
+        if 'is_year_range' in self.df_clean.columns:
+            before = len(self.df_clean)
+            self.df_clean = self.df_clean[self.df_clean['is_year_range'] == False].copy()
+            self.logger.info(
+                f"  Filter single years only: {before:,} -> {len(self.df_clean):,} rows"
+            )
+
+        # baseline_year = 2023 hardcode (syarat dosen: minimal 2023)
         df_baseline              = self.df_clean[self.df_clean['year'] == self.baseline_year]
         baseline_indicator_count = df_baseline['indicator_id'].nunique()
 
@@ -394,8 +479,6 @@ class AnalyticalLayerLoader:
         self.logger.info("STEP 5: FILTER INDICATORS WITH CONSISTENT PRESENCE")
         self.logger.info("=" * 80)
 
-        # Hitung max_start_year per indikator = max(min_year per country)
-        # = tahun pertama di mana SEMUA fixed countries sudah punya data
         indicator_country_start = self.df_clean.groupby([
             'indicator_id', 'indicator_name', 'country_id'
         ])['year'].min().reset_index()
@@ -424,8 +507,6 @@ class AnalyticalLayerLoader:
                 })
                 continue
 
-            # Cek apakah semua tahun dari max_start s/d end_year
-            # hadir di SEMUA fixed countries
             expected_years     = list(range(max_start, self.end_year + 1))
             ind_data           = self.df_clean[self.df_clean['indicator_id'] == indicator_id]
             all_years_complete = True
@@ -448,173 +529,140 @@ class AnalyticalLayerLoader:
         self.logger.info(f"\n  [+] Valid:   {len(valid_indicators)}")
         self.logger.info(f"  [-] Removed: {len(removed_indicators)}")
 
+        if removed_indicators:
+            self.logger.info(f"\n  Removed indicators:")
+            for item in removed_indicators:
+                self.logger.info(f"    [-] {item['indicator_name'][:60]} | {item['reason']}")
+
         if not valid_indicators:
             raise ValueError("No valid indicators found after filtering!")
 
-        # ----------------------------------------------------------------
-        # Filter hanya indikator yang valid.
-        # PENTING: TIDAK menghapus baris year < max_start_year.
-        # Semua baris tetap ada — label framework ditentukan di Step 6.
-        # max_start_year disimpan sebagai lookup untuk Step 6 & 7.
-        # ----------------------------------------------------------------
         original_count = len(self.df_clean)
         self.df_clean  = self.df_clean[
             self.df_clean['indicator_id'].isin(valid_indicators)
         ].copy()
 
-        # Simpan max_start_year per indicator_id untuk Step 6 dan Step 7
+        self.df_clean = self.df_clean.merge(
-        self.indicator_max_start_map = (
+            indicator_max_start[['indicator_id', 'max_start_year']],
-            indicator_max_start[indicator_max_start['indicator_id'].isin(valid_indicators)]
+            on='indicator_id', how='left'
-            .set_index('indicator_id')['max_start_year']
-            .to_dict()
         )
+        self.df_clean = self.df_clean[
+            self.df_clean['year'] >= self.df_clean['max_start_year']
+        ].copy()
+        self.df_clean = self.df_clean.drop('max_start_year', axis=1)
 
-        self.logger.info(f"\n  Rows before : {original_count:,}")
+        self.logger.info(f"\n  Rows before: {original_count:,}")
-        self.logger.info(f"  Rows after  : {len(self.df_clean):,}")
+        self.logger.info(f"  Rows after:  {len(self.df_clean):,}")
-        self.logger.info(f"  Countries   : {self.df_clean['country_id'].nunique()}")
+        self.logger.info(f"  Countries:   {self.df_clean['country_id'].nunique()}")
-        self.logger.info(f"  Indicators  : {self.df_clean['indicator_id'].nunique()}")
+        self.logger.info(f"  Indicators:  {self.df_clean['indicator_id'].nunique()}")
-        self.logger.info(f"  Pillars     : {self.df_clean['pillar_id'].nunique()}")
+        self.logger.info(f"  Pillars:     {self.df_clean['pillar_id'].nunique()}")
-        self.logger.info(
-            f"\n  [NOTE] Baris year < max_start_year TETAP ADA di data. "
-            f"Label framework akan ditentukan di Step 6."
-        )
         return self.df_clean
 
     # ------------------------------------------------------------------
-    # STEP 6: ASSIGN FRAMEWORK PER ROW
+    # STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK PER ROW
     # ------------------------------------------------------------------
 
     def determine_sdg_start_year(self):
         self.logger.info("\n" + "=" * 80)
-        self.logger.info("STEP 6: ASSIGN FRAMEWORK PER ROW")
+        self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK PER ROW")
         self.logger.info("=" * 80)
+        self.logger.info(
+            "  Proxy: FIES only (food insecurity/food insecure).\n"
+            "  Anemia TIDAK dipakai sebagai proxy — datanya ada sebelum era SDGs.\n"
+            "  Framework di-assign PER BARIS (year), bukan per indikator."
+        )
+
+        # actual_start_year per indikator = max(min_year per country)
+        # = konsisten dengan max_start_year di Step 5
+        indicator_actual_start = (
+            self.df_clean
+            .groupby(['indicator_id', 'indicator_name', 'country_id'])['year']
+            .min().reset_index()
+            .groupby(['indicator_id', 'indicator_name'])['year']
+            .max().reset_index()
+        )
+        indicator_actual_start.columns = ['indicator_id', 'indicator_name', 'actual_start_year']
+
+        # Deteksi sdg_start_year dari proxy SDGs-only (FIES saja, BUKAN anemia)
+        proxy_mask = indicator_actual_start['indicator_name'].str.lower().apply(
+            lambda n: any(kw in n for kw in _SDG_ERA_PROXY_KEYWORDS)
+        )
+        df_proxy = indicator_actual_start[proxy_mask]
+
+        if df_proxy.empty:
+            raise ValueError(
+                "Tidak ada indikator proxy SDGs (FIES) yang lolos filter. "
+                "Pastikan indikator FIES (food insecurity/food insecure) ada di data."
+            )
+
+        self.sdg_start_year = int(df_proxy['actual_start_year'].min())
+        self.logger.info(f"\n  sdg_start_year = {self.sdg_start_year}")
+        self.logger.info(f"  Proxy indicators (FIES only):")
+        for _, row in df_proxy.iterrows():
+            self.logger.info(f"    [{int(row['actual_start_year'])}] {row['indicator_name']}")
 
         # ----------------------------------------------------------------
-        # Bangun tabel actual_start_year per indikator dari
+        # Assign framework PER BARIS menggunakan year baris, bukan actual_start_year
-        # indicator_max_start_map yang sudah ditetapkan di Step 5.
+        # Sehingga indikator "shared" (anemia, stunting, dll) mendapat:
+        #   - 'MDGs' untuk baris sebelum sdg_start_year
+        #   - 'SDGs' untuk baris sejak sdg_start_year
         # ----------------------------------------------------------------
-        indicator_actual_start = pd.DataFrame([
+        self.df_clean['framework'] = self.df_clean.apply(
-            {'indicator_id': ind_id, 'actual_start_year': int(start_yr)}
+            lambda row: assign_framework_per_row(
-            for ind_id, start_yr in self.indicator_max_start_map.items()
+                indicator_name = row['indicator_name'],
-        ])
+                year           = int(row['year']),
+                sdg_start_year = self.sdg_start_year,
+            ),
+            axis=1
+        )
 
-        # Merge indicator_name untuk logging
+        # ----------------------------------------------------------------
-        indicator_actual_start = indicator_actual_start.merge(
+        # Logging: ringkasan per indikator (frameworks apa yang muncul)
-            self.df_clean[['indicator_id', 'indicator_name']].drop_duplicates(),
+        # ----------------------------------------------------------------
+        ind_fw_summary = (
+            self.df_clean
+            .groupby(['indicator_id', 'indicator_name'])['framework']
+            .unique()
+            .reset_index()
+        )
+        ind_fw_summary['frameworks'] = ind_fw_summary['framework'].apply(
+            lambda x: '/'.join(sorted(x))
+        )
+        ind_fw_summary = ind_fw_summary.merge(
+            indicator_actual_start[['indicator_id', 'actual_start_year']],
             on='indicator_id', how='left'
         )
 
-        # Tandai mana yang SDG-only
+        self.logger.info(f"\n  Framework assignment per indikator:")
-        indicator_actual_start['is_sdg_only'] = (
+        self.logger.info(f"  {'-'*85}")
-            indicator_actual_start['indicator_name']
+        self.logger.info(f"  {'ID':<5} {'Frameworks':<18} {'ActualStart':<13} {'Indicator Name'}")
-            .str.lower().str.strip()
+        self.logger.info(f"  {'-'*85}")
-            .isin(SDG_ONLY_KEYWORDS)
+        for _, row in ind_fw_summary.sort_values(
-        )
+            ['frameworks', 'actual_start_year', 'indicator_name']
-
+        ).iterrows():
-        # ----------------------------------------------------------------
-        # sdg_transition_year = min(actual_start_year) dari semua SDG-only
-        # indicators yang lolos filter.
-        # Ini adalah satu titik waktu di mana semua SDG-only indicators
-        # berubah dari 'MDGs' ke 'SDGs' secara SERENTAK.
-        # ----------------------------------------------------------------
-        sdg_only_df = indicator_actual_start[indicator_actual_start['is_sdg_only']]
-        if sdg_only_df.empty:
-            raise ValueError(
-                "Tidak ada indikator SDG-only (FIES/anaemia) yang lolos filter. "
-                "Pastikan indikator FIES dan anaemia ada di data."
-            )
-
-        self.sdg_transition_year = int(sdg_only_df['actual_start_year'].min())
-
-        self.logger.info(f"\n  SDG-only indicators dan actual_start_year masing-masing:")
-        self.logger.info(f"  {'-'*80}")
-        for _, row in sdg_only_df.iterrows():
             self.logger.info(
-                f"  [SDG-only] actual_start={int(row['actual_start_year'])} | "
+                f"  {int(row['indicator_id']):<5} {row['frameworks']:<18} "
-                f"{row['indicator_name']}"
+                f"{int(row['actual_start_year']):<13} {row['indicator_name'][:48]}"
             )
 
-        self.logger.info(
+        # Indikator dengan framework split (MDGs/SDGs) — highlight untuk validasi
-            f"\n  sdg_transition_year = {self.sdg_transition_year} "
+        split_inds = ind_fw_summary[ind_fw_summary['frameworks'] == 'MDGs/SDGs']
-            f"(min actual_start_year dari semua SDG-only indicators)"
+        if not split_inds.empty:
-        )
-
-        self.logger.info(f"\n  Logika assign framework (PER BARIS):")
-        self.logger.info(f"  ──────────────────────────────────────────────────────────")
-        self.logger.info(f"  Indikator TIDAK di SDG_ONLY_KEYWORDS:")
-        self.logger.info(f"    → 'MDGs' di semua tahun")
-        self.logger.info(f"  Indikator DI SDG_ONLY_KEYWORDS:")
-        self.logger.info(f"    year <  {self.sdg_transition_year} → 'MDGs'  (data tetap ada)")
-        self.logger.info(f"    year >= {self.sdg_transition_year} → 'SDGs'")
-        self.logger.info(f"  ──────────────────────────────────────────────────────────")
-
-        # ----------------------------------------------------------------
-        # Assign framework dengan vectorized operation menggunakan
-        # sdg_transition_year (SATU nilai untuk semua SDG-only indicators)
-        # ----------------------------------------------------------------
-        # Tandai apakah setiap baris adalah SDG-only indicator
-        sdg_only_ids = set(
-            indicator_actual_start.loc[
-                indicator_actual_start['is_sdg_only'], 'indicator_id'
-            ]
-        )
-        self.df_clean['_is_sdg_only'] = self.df_clean['indicator_id'].isin(sdg_only_ids)
-
-        # Assign framework:
-        # - Bukan SDG-only                              → 'MDGs'
-        # - SDG-only AND year >= sdg_transition_year    → 'SDGs'
-        # - SDG-only AND year <  sdg_transition_year    → 'MDGs'
-        self.df_clean['framework'] = np.where(
-            self.df_clean['_is_sdg_only'] &
-            (self.df_clean['year'] >= self.sdg_transition_year),
-            'SDGs',
-            'MDGs'
-        )
-
-        # Drop kolom bantu
-        self.df_clean = self.df_clean.drop(columns=['_is_sdg_only'])
-
-        # ----------------------------------------------------------------
-        # Log verifikasi per indikator
-        # ----------------------------------------------------------------
-        self.logger.info(f"\n  Verifikasi framework per indikator:")
-        self.logger.info(f"  {'-'*110}")
-        self.logger.info(
-            f"  {'ID':<5} {'Indicator Name':<52} {'Data From':<12} "
-            f"{'MDGs rows':<12} {'SDGs rows':<12} {'Note'}"
-        )
-        self.logger.info(f"  {'-'*110}")
-
-        for ind_id, grp in self.df_clean.groupby('indicator_id'):
-            ind_name    = grp['indicator_name'].iloc[0]
-            mdgs_rows   = (grp['framework'] == 'MDGs').sum()
-            sdgs_rows   = (grp['framework'] == 'SDGs').sum()
-            is_sdg_only = ind_id in sdg_only_ids
-            data_from   = int(grp['year'].min())
-
-            if is_sdg_only:
-                note = f"SDGs from {self.sdg_transition_year}, MDGs before"
-            else:
-                note = "MDGs always"
-
             self.logger.info(
-                f"  {int(ind_id):<5} {ind_name[:50]:<52} {data_from:<12} "
+                f"\n  [INFO] {len(split_inds)} indikator memiliki framework split "
-                f"{mdgs_rows:<12} {sdgs_rows:<12} {note}"
+                f"(MDGs sebelum {self.sdg_start_year}, SDGs sejak {self.sdg_start_year}):"
             )
+            for _, row in split_inds.iterrows():
+                self.logger.info(f"    - {row['indicator_name'][:60]}")
 
         fw_summary = self.df_clean['framework'].value_counts()
-        self.logger.info(f"\n  Ringkasan rows: " + " | ".join(
+        self.logger.info(
-            f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items()
+            f"\n  Ringkasan rows: " +
-        ))
+            " | ".join(f"{fw}: {cnt:,}" for fw, cnt in fw_summary.items())
-
+        )
-        end_year_df    = self.df_clean[self.df_clean['year'] == self.end_year]
-        fw_ind_summary = end_year_df.groupby('framework')['indicator_id'].nunique()
-        self.logger.info(f"  Indicators di year={self.end_year}: " + " | ".join(
-            f"{fw}: {cnt}" for fw, cnt in fw_ind_summary.items()
-        ))
 
         self.logger.info(
-            f"\n  [OK] 'framework' ditambahkan — "
+            f"\n  [OK] 'framework' ditambahkan per row — "
             f"MDGs: {(self.df_clean['framework'] == 'MDGs').sum():,} rows | "
             f"SDGs: {(self.df_clean['framework'] == 'SDGs').sum():,} rows"
         )
@@ -629,44 +677,23 @@ class AnalyticalLayerLoader:
         self.logger.info("STEP 7: VERIFY NO GAPS")
         self.logger.info("=" * 80)
 
-        # ----------------------------------------------------------------
-        # Verifikasi dilakukan PER INDIKATOR dari actual_start_year-nya,
-        # bukan dari self.start_year global, karena tiap indikator bisa
-        # punya start year berbeda.
-        # Baris sebelum actual_start_year (yang berlabel MDGs) tidak dicek
-        # karena memang tidak semua country punya data di sana.
-        # ----------------------------------------------------------------
         expected_countries = len(self.selected_country_ids)
-        all_good           = True
+        verification       = self.df_clean.groupby(
-        bad_rows           = []
+            ['indicator_id', 'year']
-
+        )['country_id'].nunique().reset_index()
-        for ind_id, grp in self.df_clean.groupby('indicator_id'):
+        verification.columns = ['indicator_id', 'year', 'country_count']
-            actual_start = self.indicator_max_start_map.get(ind_id)
+        all_good = (verification['country_count'] == expected_countries).all()
-            if actual_start is None:
-                continue
-
-            expected_years = list(range(int(actual_start), self.end_year + 1))
-
-            for year in expected_years:
-                country_count = grp[grp['year'] == year]['country_id'].nunique()
-                if country_count != expected_countries:
-                    all_good = False
-                    bad_rows.append({
-                        'indicator_id' : int(ind_id),
-                        'year'         : int(year),
-                        'country_count': int(country_count),
-                    })
 
         if all_good:
             self.logger.info(
-                f"  VERIFICATION PASSED — all combinations from actual_start_year "
+                f"  VERIFICATION PASSED — all combinations have {expected_countries} countries"
-                f"have {expected_countries} countries"
             )
         else:
-            for row in bad_rows[:10]:
+            bad = verification[verification['country_count'] != expected_countries]
+            for _, row in bad.head(10).iterrows():
                 self.logger.error(
-                    f"  Indicator {row['indicator_id']}, Year {row['year']}: "
+                    f"  Indicator {int(row['indicator_id'])}, Year {int(row['year'])}: "
-                    f"{row['country_count']} countries (expected {expected_countries})"
+                    f"{int(row['country_count'])} countries (expected {expected_countries})"
                 )
             raise ValueError("Gap verification failed!")
 
@@ -679,7 +706,22 @@ class AnalyticalLayerLoader:
     def calculate_norm_value(self):
         """
         Hitung norm_value_1_100 per indikator — min-max normalisasi skala 1-100,
-        direction-aware, global per indikator (semua negara + semua tahun).
+        direction-aware.
+
+        CARA KERJA:
+        - Normalisasi dilakukan GLOBAL per indikator (semua negara + semua tahun sekaligus)
+          sehingga nilai antar negara dan antar tahun tetap comparable.
+        - lower_better diinvert: nilai tinggi selalu = kondisi lebih baik.
+          Contoh: undernourishment 5% (rendah = baik) → norm tinggi setelah invert.
+        - Skala 1-100 (bukan 0-100) untuk menghindari nilai absolut nol di Looker Studio.
+        - Kolom ini memungkinkan perbandingan lintas indikator yang berbeda satuan
+          (persen, juta orang, dll) karena sudah dinormalisasi ke skala yang sama.
+
+        Catatan:
+        - Berbeda dengan norm_value di _get_norm_value_df() di analysis_layer
+          yang skala 0-1 dan dipakai untuk agregasi composite score.
+        - norm_value_1_100 ini adalah per baris (per country per year per indicator),
+          untuk ditampilkan langsung di Looker Studio.
         """
         self.logger.info("\n" + "=" * 80)
         self.logger.info("STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR")
@@ -693,10 +735,7 @@ class AnalyticalLayerLoader:
         norm_parts = []
 
         indicators = df.groupby(['indicator_id', 'indicator_name', 'direction'])
-        self.logger.info(
+        self.logger.info(f"\n  {'ID':<5} {'Direction':<15} {'Invert':<8} {'Min':>10} {'Max':>10} {'Indicator Name'}")
-            f"\n  {'ID':<5} {'Direction':<15} {'Invert':<8} "
-            f"{'Min':>10} {'Max':>10} {'Indicator Name'}"
-        )
         self.logger.info(f"  {'-'*90}")
 
         for (ind_id, ind_name, direction), grp in indicators:
@@ -710,17 +749,21 @@ class AnalyticalLayerLoader:
                 norm_parts.append(grp)
                 continue
 
-            raw    = grp.loc[valid_mask, 'value'].values
+            raw      = grp.loc[valid_mask, 'value'].values
-            v_min  = raw.min()
+            v_min    = raw.min()
-            v_max  = raw.max()
+            v_max    = raw.max()
-            normed = np.full(len(grp), np.nan)
+            normed   = np.full(len(grp), np.nan)
 
             if v_min == v_max:
+                # Semua nilai sama → beri nilai tengah (50.5 pada skala 1-100)
                 normed[valid_mask.values] = 50.5
             else:
+                # Min-max ke 0-1 dulu
                 scaled = (raw - v_min) / (v_max - v_min)
+                # Invert jika lower_better
                 if do_invert:
                     scaled = 1.0 - scaled
+                # Scale ke 1-100
                 normed[valid_mask.values] = 1.0 + scaled * 99.0
 
             grp['norm_value_1_100'] = normed
@@ -733,6 +776,7 @@ class AnalyticalLayerLoader:
 
         self.df_clean = pd.concat(norm_parts, ignore_index=True)
 
+        # Statistik ringkasan
         valid_norm = self.df_clean['norm_value_1_100'].notna().sum()
         null_norm  = self.df_clean['norm_value_1_100'].isna().sum()
         self.logger.info(f"\n  norm_value_1_100 — valid: {valid_norm:,} | null: {null_norm:,}")
@@ -742,14 +786,10 @@ class AnalyticalLayerLoader:
             f"{self.df_clean['norm_value_1_100'].max():.2f}"
         )
 
-        self.df_clean['_condition_preview'] = (
+        # Log distribusi kondisi berdasarkan threshold
-            self.df_clean['norm_value_1_100'].apply(assign_condition)
+        self.df_clean['_condition_preview'] = self.df_clean['norm_value_1_100'].apply(assign_condition)
-        )
         cond_dist = self.df_clean['_condition_preview'].value_counts()
-        self.logger.info(
+        self.logger.info(f"\n  Distribusi kondisi (threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}):")
-            f"\n  Distribusi kondisi "
-            f"(threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}):"
-        )
         for cond, cnt in cond_dist.items():
             self.logger.info(f"    {cond}: {cnt:,} rows")
         self.df_clean = self.df_clean.drop(columns=['_condition_preview'])
@@ -822,39 +862,45 @@ class AnalyticalLayerLoader:
             'start_year', 'end_year', 'country_count'
         ]
 
-        fw_at_end = (
+        # Framework summary per indikator (bisa MDGs, SDGs, atau MDGs/SDGs split)
-            self.df_clean[self.df_clean['year'] == self.end_year]
+        ind_fw = (
+            self.df_clean
             .groupby('indicator_id')['framework']
-            .first()
+            .unique()
             .reset_index()
         )
-        indicator_details = indicator_details.merge(fw_at_end, on='indicator_id', how='left')
+        ind_fw['framework_label'] = ind_fw['framework'].apply(
-        indicator_details['framework'] = indicator_details['framework'].fillna('MDGs')
+            lambda x: '/'.join(sorted(x))
+        )
+        indicator_details = indicator_details.merge(
+            ind_fw[['indicator_id', 'framework_label']],
+            on='indicator_id', how='left'
+        )
 
         indicator_details['year_range'] = (
             indicator_details['start_year'].astype(int).astype(str) + '-' +
             indicator_details['end_year'].astype(int).astype(str)
         )
         indicator_details = indicator_details.sort_values(
-            ['framework', 'pillar_name', 'start_year', 'indicator_name']
+            ['framework_label', 'pillar_name', 'start_year', 'indicator_name']
         )
 
         self.logger.info(f"\nTotal Indicators: {len(indicator_details)}")
-        self.logger.info(f"Framework breakdown (at end_year={self.end_year}):")
+        self.logger.info(f"Framework breakdown (per indicator label):")
-        for fw, count in indicator_details.groupby('framework').size().items():
+        for fw, count in indicator_details.groupby('framework_label').size().items():
             self.logger.info(f"  {fw}: {count} indicators")
 
-        self.logger.info(f"\n{'-'*110}")
+        self.logger.info(f"\n{'-'*115}")
         self.logger.info(
             f"{'ID':<5} {'Indicator Name':<55} {'Pillar':<15} "
-            f"{'Framework':<10} {'Years':<12} {'Dir':<8} {'Countries'}"
+            f"{'Framework':<15} {'Years':<12} {'Dir':<8} {'Countries'}"
         )
-        self.logger.info(f"{'-'*110}")
+        self.logger.info(f"{'-'*115}")
         for _, row in indicator_details.iterrows():
             direction = 'higher+' if row['direction'] == 'higher_better' else 'lower-'
             self.logger.info(
                 f"{int(row['indicator_id']):<5} {row['indicator_name'][:52]:<55} "
-                f"{row['pillar_name'][:13]:<15} {row['framework']:<10} "
+                f"{row['pillar_name'][:13]:<15} {row['framework_label']:<15} "
                 f"{row['year_range']:<12} {direction:<8} {int(row['country_count'])}"
             )
 
@@ -917,20 +963,22 @@ class AnalyticalLayerLoader:
 
             self.logger.info(f"  Total rows: {len(analytical_df):,}")
 
+            # Framework distribution per row
             fw_dist_rows = analytical_df['framework'].value_counts()
             self.logger.info(f"  Framework distribution (rows):")
             for fw, cnt in fw_dist_rows.items():
                 self.logger.info(f"    {fw}: {cnt:,} rows")
 
-            fw_dist_ind = (
+            # Framework distribution per indikator (label)
-                analytical_df[analytical_df['year'] == self.end_year]
+            ind_fw_label = (
-                .drop_duplicates('indicator_id')['framework']
+                analytical_df
+                .groupby('indicator_id')['framework']
+                .unique()
+                .apply(lambda x: '/'.join(sorted(x)))
                 .value_counts()
             )
-            self.logger.info(
+            self.logger.info(f"  Framework distribution (per indicator label):")
-                f"  Framework distribution (indicators at year={self.end_year}):"
+            for fw, cnt in ind_fw_label.items():
-            )
-            for fw, cnt in fw_dist_ind.items():
                 self.logger.info(f"    {fw}: {cnt} indicators")
 
             self.logger.info(
@@ -974,30 +1022,26 @@ class AnalyticalLayerLoader:
                 'rows_loaded'        : rows_loaded,
                 'completeness_pct'   : 100.0,
                 'config_snapshot'    : json.dumps({
-                    'start_year'             : self.start_year,
+                    'start_year'           : self.start_year,
-                    'end_year'               : self.end_year,
+                    'end_year'             : self.end_year,
-                    'baseline_year'          : self.baseline_year,
+                    'baseline_year'        : self.baseline_year,
-                    'sdg_transition_year'    : self.sdg_transition_year,
+                    'sdg_start_year'       : self.sdg_start_year,
-                    'fixed_countries'        : len(self.selected_country_ids),
+                    'fixed_countries'      : len(self.selected_country_ids),
-                    'norm_scale'             : '1-100 per indicator global minmax direction-aware',
+                    'norm_scale'           : '1-100 per indicator global minmax direction-aware',
-                    'framework_logic'        : (
+                    'framework_assignment' : 'per-row by year (not per-indicator)',
-                        'sdg_transition_year = min(actual_start_year) dari SDG-only indicators; '
+                    'sdg_proxy_keywords'   : list(_SDG_ERA_PROXY_KEYWORDS),
-                        'SDG-only year >= sdg_transition_year → SDGs; '
+                    'condition_thresholds' : {
-                        'SDG-only year <  sdg_transition_year → MDGs (data tetap ada); '
-                        'non-SDG-only → MDGs selalu'
-                    ),
-                    'sdg_only_keywords_count': len(SDG_ONLY_KEYWORDS),
-                    'condition_thresholds'   : {
                         'bad'     : f'< {THRESHOLD_BAD}',
                         'moderate': f'{THRESHOLD_BAD}-{THRESHOLD_GOOD}',
                         'good'    : f'> {THRESHOLD_GOOD}',
                     },
                 }),
                 'validation_metrics' : json.dumps({
-                    'fixed_countries'    : len(self.selected_country_ids),
+                    'fixed_countries'      : len(self.selected_country_ids),
-                    'total_indicators'   : int(self.df_clean['indicator_id'].nunique()),
+                    'total_indicators'     : int(self.df_clean['indicator_id'].nunique()),
-                    'sdg_transition_year': self.sdg_transition_year,
+                    'sdg_start_year'       : self.sdg_start_year,
-                    'framework_dist_rows': fw_dist_rows.to_dict(),
+                    'framework_dist_rows'  : fw_dist_rows.to_dict(),
+                    'framework_dist_inds'  : ind_fw_label.to_dict(),
                 })
             }
             save_etl_metadata(self.client, metadata)
@@ -1020,11 +1064,9 @@ class AnalyticalLayerLoader:
         self.logger.info("\n" + "=" * 80)
         self.logger.info("Output: fact_asean_food_security_selected -> fs_asean_gold")
         self.logger.info("Kolom baru: norm_value_1_100 (min-max 1-100, direction-aware)")
+        self.logger.info("Framework: per-row by year (shared indicators split MDGs/SDGs)")
+        self.logger.info(f"SDG Proxy: FIES only (food insecurity/food insecure)")
         self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
-        self.logger.info(
-            "Framework: SDG-only indicators → SDGs mulai sdg_transition_year, "
-            "MDGs sebelumnya (data tetap ada). Non-SDG-only → MDGs selalu."
-        )
         self.logger.info("=" * 80)
 
         self.load_source_data()
@@ -1032,10 +1074,10 @@ class AnalyticalLayerLoader:
         self.filter_complete_indicators_per_country()
         self.select_countries_with_all_pillars()
         self.filter_indicators_consistent_across_fixed_countries()
-        self.determine_sdg_start_year()
+        self.determine_sdg_start_year()    # Step 6: per-row framework assignment
         self.verify_no_gaps()
-        self.calculate_norm_value()
+        self.calculate_norm_value()        # Step 8: norm_value_1_100
-        self.calculate_yoy()
+        self.calculate_yoy()               # Step 9: yoy_change, yoy_pct
         self.analyze_indicator_availability_by_year()
         self.save_analytical_table()
 
@@ -1045,12 +1087,12 @@ class AnalyticalLayerLoader:
         self.logger.info("\n" + "=" * 80)
         self.logger.info("COMPLETED")
         self.logger.info("=" * 80)
-        self.logger.info(f"  Duration           : {duration:.2f}s")
+        self.logger.info(f"  Duration      : {duration:.2f}s")
-        self.logger.info(f"  Year Range         : {self.start_year}-{self.end_year}")
+        self.logger.info(f"  Year Range    : {self.start_year}-{self.end_year}")
-        self.logger.info(f"  SDG Transition Year: {self.sdg_transition_year}")
+        self.logger.info(f"  SDG Start Yr  : {self.sdg_start_year}")
-        self.logger.info(f"  Countries          : {len(self.selected_country_ids)}")
+        self.logger.info(f"  Countries     : {len(self.selected_country_ids)}")
-        self.logger.info(f"  Indicators         : {self.df_clean['indicator_id'].nunique()}")
+        self.logger.info(f"  Indicators    : {self.df_clean['indicator_id'].nunique()}")
-        self.logger.info(f"  Rows Loaded        : {self.pipeline_metadata['rows_loaded']:,}")
+        self.logger.info(f"  Rows Loaded   : {self.pipeline_metadata['rows_loaded']:,}")
 
 
 # =============================================================================
@@ -1074,11 +1116,8 @@ if __name__ == "__main__":
     print("BIGQUERY ANALYTICAL LAYER - DATA FILTERING")
     print("Output: fact_asean_food_security_selected -> fs_asean_gold")
     print(f"Norm: min-max 1-100 per indicator, direction-aware")
+    print(f"Framework: per-row by year | SDG Proxy: FIES only")
     print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
-    print(
-        "Framework: SDG-only → SDGs mulai sdg_transition_year, MDGs sebelumnya. "
-        "Non-SDG-only → MDGs selalu."
-    )
     print("=" * 80)
 
     logger = setup_logging()
@@ -1088,6 +1127,6 @@ if __name__ == "__main__":
 
     print("\n" + "=" * 80)
     print("[OK] COMPLETED")
-    print(f"  SDG Transition Year : {loader.sdg_transition_year}")
+    print(f"  SDG Start Year : {loader.sdg_start_year}")
-    print(f"  Rows Loaded         : {loader.pipeline_metadata['rows_loaded']:,}")
+    print(f"  Rows Loaded    : {loader.pipeline_metadata['rows_loaded']:,}")
     print("=" * 80)