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sdgs year v4

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Debby
2026-04-01 07:43:31 +07:00
parent 64e3095e7a
commit 236d4b4dc8
1 changed files with 164 additions and 139 deletions
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301
scripts/bigquery_analytical_layer.py
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@@ -8,7 +8,7 @@ Filtering Order:
3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps) 3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps)
4. Filter countries with ALL pillars (FIXED SET) 4. Filter countries with ALL pillars (FIXED SET)
5. Filter indicators with consistent presence across FIXED countries 5. Filter indicators with consistent presence across FIXED countries
6. Determine SDG start year & assign framework (MDGs/SDGs) per indicator PER ROW 6. Assign framework (MDGs/SDGs) per indicator PER ROW
7. Verify no gaps 7. Verify no gaps
8. Calculate norm_value_1_100 per indicator per country (min-max, direction-aware) 8. Calculate norm_value_1_100 per indicator per country (min-max, direction-aware)
9. Calculate YoY per indicator per country 9. Calculate YoY per indicator per country
@@ -22,16 +22,17 @@ NORMALISASI (Step 8):
sehingga nilai antar negara dan antar tahun tetap comparable sehingga nilai antar negara dan antar tahun tetap comparable
- Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio - Kolom ini memungkinkan perbandingan antar indikator yang berbeda satuan di Looker Studio
FRAMEWORK LOGIC (Row-Level Assignment): FRAMEWORK LOGIC (FIX - Per Indicator, Per Row):
- SDG start year dideteksi dari data: tahun pertama indikator FIES/anaemia lengkap - Framework di-assign PER BARIS dengan mempertimbangkan actual_start_year MASING-MASING
di semua fixed countries (setelah Step 3-5 filter selesai) indikator, bukan satu sdg_start_year global.
- Framework di-assign PER BARIS (per tahun): - Logika:
* year < sdg_start_year → selalu 'MDGs' (semua indikator) * Jika nama indikator TIDAK ada di SDG_ONLY_KEYWORDS → selalu 'MDGs' (semua tahun)
* year >= sdg_start_year + nama di SDG_ONLY_KEYWORDS → 'SDGs' * Jika nama indikator ADA di SDG_ONLY_KEYWORDS:
* selain itu (implisit) → 'MDGs' - row['year'] >= actual_start_year[indicator] → 'SDGs'
- Hanya FIES dan anaemia yang masuk SDG_ONLY_KEYWORDS karena murni baru di era SDGs. - row['year'] < actual_start_year[indicator] → 'MDGs'
- Shared indicators (stunting, wasting, overweight, undernourishment) tidak terdaftar - Baris dengan year < actual_start_year TETAP ADA di data (tidak dihapus di Step 5),
di SDG_ONLY_KEYWORDS sehingga secara implisit selalu berlabel 'MDGs' di semua tahun. hanya mendapat label 'MDGs'.
- actual_start_year per indikator = max(min_year per country) setelah Step 3-4 filter
""" """
import pandas as pd import pandas as pd
@@ -61,8 +62,8 @@ from google.cloud import bigquery
# SDG-ONLY INDICATOR KEYWORDS # SDG-ONLY INDICATOR KEYWORDS
# ============================================================================= # =============================================================================
# Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini. # Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini.
# Baris dengan year >= sdg_start_year + nama ada di set ini → 'SDGs'. # Indikator di set ini → 'SDGs' mulai dari actual_start_year indikator tersebut.
# Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' secara implisit. # Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' di semua tahun.
SDG_ONLY_KEYWORDS = frozenset([ SDG_ONLY_KEYWORDS = frozenset([
# TARGET 2.1.2 — FIES (SDGs only) # TARGET 2.1.2 — FIES (SDGs only)
@@ -83,19 +84,9 @@ SDG_ONLY_KEYWORDS = frozenset([
"number of women of reproductive age (15-49 years) affected by anemia (million)", "number of women of reproductive age (15-49 years) affected by anemia (million)",
]) ])
# Proxy keywords untuk deteksi era SDGs dari data (indikator murni baru di SDGs)
_SDG_ERA_PROXY_KEYWORDS = frozenset([
"food insecurity",
"anemia",
"anaemia",
])
# ============================================================================= # =============================================================================
# THRESHOLD KONDISI (fixed absolute, skala 1-100) # THRESHOLD KONDISI (fixed absolute, skala 1-100)
# ============================================================================= # =============================================================================
# bad : norm_value_1_100 < THRESHOLD_BAD
# good : norm_value_1_100 > THRESHOLD_GOOD
# moderate : di antara keduanya
THRESHOLD_BAD = 40.0 THRESHOLD_BAD = 40.0
THRESHOLD_GOOD = 60.0 THRESHOLD_GOOD = 60.0
@@ -104,8 +95,6 @@ THRESHOLD_GOOD = 60.0
def assign_condition(norm_value_1_100: float) -> str: def assign_condition(norm_value_1_100: float) -> str:
""" """
Assign kondisi berdasarkan norm_value_1_100 (skala 1-100, sudah direction-aware). 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' Returns: 'good' / 'moderate' / 'bad'
""" """
if pd.isna(norm_value_1_100): if pd.isna(norm_value_1_100):
@@ -117,38 +106,6 @@ def assign_condition(norm_value_1_100: float) -> str:
return 'moderate' return 'moderate'
def assign_framework_for_row(
indicator_name: str,
row_year: int,
sdg_start_year: int,
) -> str:
"""
Tentukan framework (MDGs/SDGs) PER BARIS (per tahun).
Logic:
─────────────────────────────────────────────────────────────────────────
RULE 1: row_year < sdg_start_year
→ selalu 'MDGs', tanpa kecuali.
RULE 2: row_year >= sdg_start_year AND nama ada di SDG_ONLY_KEYWORDS
→ 'SDGs'
RULE 3 (implisit): semua kondisi lain
→ 'MDGs'
Ini mencakup shared indicators (stunting, wasting, overweight,
undernourishment) yang tidak terdaftar di SDG_ONLY_KEYWORDS,
sehingga tidak perlu di-list secara eksplisit.
─────────────────────────────────────────────────────────────────────────
"""
if row_year < sdg_start_year:
return 'MDGs'
if str(indicator_name).lower().strip() in SDG_ONLY_KEYWORDS:
return 'SDGs'
return 'MDGs'
# ============================================================================= # =============================================================================
# ANALYTICAL LAYER CLASS # ANALYTICAL LAYER CLASS
# ============================================================================= # =============================================================================
@@ -162,13 +119,16 @@ class AnalyticalLayerLoader:
indicator_id, indicator_name, direction, framework, indicator_id, indicator_name, direction, framework,
pillar_id, pillar_name, pillar_id, pillar_name,
time_id, year, value, time_id, year, value,
norm_value_1_100, <- min-max norm per indikator, skala 1-100, direction-aware norm_value_1_100,
yoy_change, yoy_pct yoy_change, yoy_pct
FRAMEWORK LOGIC: FRAMEWORK LOGIC (FIX):
- year < sdg_start_year → 'MDGs' (semua indikator) - Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' di SEMUA tahun
- year >= sdg_start_year + nama di SDG_ONLY_KEYWORDS → 'SDGs' (FIES + anaemia) - Indikator DI SDG_ONLY_KEYWORDS:
- selain itu (implisit) → 'MDGs' year >= actual_start_year[indikator] → 'SDGs'
year < actual_start_year[indikator] → 'MDGs'
- actual_start_year per indikator = max(min_year per country) setelah Step 3-4 filter
- Baris year < actual_start_year TETAP ADA, hanya berlabel 'MDGs'
""" """
def __init__(self, client: bigquery.Client): def __init__(self, client: bigquery.Client):
@@ -182,12 +142,13 @@ class AnalyticalLayerLoader:
self.df_pillar = 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.start_year = 2013 self.start_year = 2013
self.end_year = None self.end_year = None
self.baseline_year = 2023 # hardcode per syarat dosen (tahun terlengkap) self.baseline_year = 2023
self.sdg_start_year = None self.sdg_start_year = None # disimpan untuk metadata/logging saja
self.pipeline_metadata = { self.pipeline_metadata = {
'source_class' : self.__class__.__name__, 'source_class' : self.__class__.__name__,
@@ -490,19 +451,22 @@ class AnalyticalLayerLoader:
if not valid_indicators: if not valid_indicators:
raise ValueError("No valid indicators found after filtering!") raise ValueError("No valid indicators found after filtering!")
# ----------------------------------------------------------------
# Filter hanya indikator yang valid
# TIDAK menghapus baris year < max_start_year —
# semua baris tetap ada, label framework ditentukan di Step 6
# ----------------------------------------------------------------
original_count = len(self.df_clean) original_count = len(self.df_clean)
self.df_clean = self.df_clean[ self.df_clean = self.df_clean[
self.df_clean['indicator_id'].isin(valid_indicators) self.df_clean['indicator_id'].isin(valid_indicators)
].copy() ].copy()
self.df_clean = self.df_clean.merge( # Simpan max_start_year sebagai lookup untuk Step 6
indicator_max_start[['indicator_id', 'max_start_year']], self.indicator_max_start_map = (
on='indicator_id', how='left' indicator_max_start[indicator_max_start['indicator_id'].isin(valid_indicators)]
.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):,}")
@@ -512,74 +476,123 @@ class AnalyticalLayerLoader:
return self.df_clean return self.df_clean
# ------------------------------------------------------------------ # ------------------------------------------------------------------
# STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL) # STEP 6: ASSIGN FRAMEWORK PER ROW (per-indicator actual_start_year)
# ------------------------------------------------------------------ # ------------------------------------------------------------------
def determine_sdg_start_year(self): def determine_sdg_start_year(self):
self.logger.info("\n" + "=" * 80) self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 6: DETERMINE SDG START YEAR & ASSIGN FRAMEWORK (ROW-LEVEL)") self.logger.info("STEP 6: ASSIGN FRAMEWORK PER ROW (per-indicator actual_start_year)")
self.logger.info("=" * 80) self.logger.info("=" * 80)
indicator_actual_start = ( # ----------------------------------------------------------------
self.df_clean # Hitung actual_start_year PER INDIKATOR dari indicator_max_start_map
.groupby(['indicator_id', 'indicator_name', 'country_id'])['year'] # yang sudah dihitung di Step 5.
.min().reset_index() # actual_start_year = max(min_year per country) per indikator
.groupby(['indicator_id', 'indicator_name'])['year'] # = tahun di mana semua fixed countries sudah punya data
.max().reset_index() # ----------------------------------------------------------------
) indicator_actual_start = pd.DataFrame([
indicator_actual_start.columns = ['indicator_id', 'indicator_name', 'actual_start_year'] {'indicator_id': ind_id, 'actual_start_year': start_yr}
for ind_id, start_yr in self.indicator_max_start_map.items()
])
# Deteksi sdg_start_year dari proxy SDGs-only (FIES & anaemia) # Merge indicator_name untuk keperluan logging
proxy_mask = indicator_actual_start['indicator_name'].str.lower().apply( indicator_actual_start = indicator_actual_start.merge(
lambda n: any(kw in n for kw in _SDG_ERA_PROXY_KEYWORDS) self.df_clean[['indicator_id', 'indicator_name']].drop_duplicates(),
on='indicator_id', how='left'
) )
df_proxy = indicator_actual_start[proxy_mask]
if df_proxy.empty: # Tandai mana yang SDG-only
indicator_actual_start['is_sdg_only'] = (
indicator_actual_start['indicator_name']
.str.lower().str.strip()
.isin(SDG_ONLY_KEYWORDS)
)
# sdg_start_year global = min(actual_start_year dari SDG-only indicators)
# Disimpan hanya untuk metadata/logging
sdg_only_df = indicator_actual_start[indicator_actual_start['is_sdg_only']]
if sdg_only_df.empty:
raise ValueError( raise ValueError(
"Tidak ada indikator proxy SDGs (FIES/anaemia) yang lolos filter. " "Tidak ada indikator SDG-only (FIES/anaemia) yang lolos filter. "
"Pastikan indikator FIES dan anaemia ada di data." "Pastikan indikator FIES dan anaemia ada di data."
) )
self.sdg_start_year = int(sdg_only_df['actual_start_year'].min())
self.sdg_start_year = int(df_proxy['actual_start_year'].min()) self.logger.info(f"\n SDG-only indicators dan actual_start_year masing-masing:")
self.logger.info(f"\n sdg_start_year = {self.sdg_start_year}") self.logger.info(f" {'-'*80}")
self.logger.info(f" Proxy indicators (penentu sdg_start_year):") for _, row in indicator_actual_start[indicator_actual_start['is_sdg_only']].iterrows():
for _, row in df_proxy.iterrows():
self.logger.info(f" [{int(row['actual_start_year'])}] {row['indicator_name']}")
self.logger.info(f"\n Assigning framework PER ROW...")
self.logger.info(f" year < {self.sdg_start_year} → MDGs (semua indikator)")
self.logger.info(f" year >= {self.sdg_start_year} + nama in SDG_ONLY_KEYWORDS → SDGs")
self.logger.info(f" selain itu (implisit) → MDGs")
self.df_clean['framework'] = self.df_clean.apply(
lambda row: assign_framework_for_row(
indicator_name = row['indicator_name'],
row_year = int(row['year']),
sdg_start_year = self.sdg_start_year,
),
axis=1
)
# Log ringkasan per indikator untuk verifikasi
self.logger.info(f"\n {'Framework Assignment per Indicator':}")
self.logger.info(f" {'-'*100}")
self.logger.info( self.logger.info(
f" {'ID':<5} {'Indicator Name':<52} " f" [SDG-only] start={int(row['actual_start_year'])} | {row['indicator_name']}"
f"{'Pre-SDG':<10} {'MDGs':<10} {'SDGs':<10} {'SDG-Only?'}"
) )
self.logger.info(f" {'-'*100}") self.logger.info(
f"\n sdg_start_year (earliest SDG-only, for metadata): {self.sdg_start_year}"
)
# Lookup: indicator_id → actual_start_year (hanya SDG-only, untuk logging)
sdg_only_start_map = (
indicator_actual_start[indicator_actual_start['is_sdg_only']]
.set_index('indicator_id')['actual_start_year']
.to_dict()
)
self.logger.info(f"\n Logika assign framework (PER BARIS, PER INDIKATOR):")
self.logger.info(f" ─────────────────────────────────────────────────────")
self.logger.info(f" Jika indikator TIDAK di SDG_ONLY_KEYWORDS:")
self.logger.info(f" → 'MDGs' di semua tahun (shared indicators)")
self.logger.info(f" Jika indikator DI SDG_ONLY_KEYWORDS:")
self.logger.info(f" year >= actual_start_year[indikator] → 'SDGs'")
self.logger.info(f" year < actual_start_year[indikator] → 'MDGs'")
self.logger.info(f" ─────────────────────────────────────────────────────")
# ----------------------------------------------------------------
# Assign framework dengan vectorized merge
# ----------------------------------------------------------------
self.df_clean = self.df_clean.merge(
indicator_actual_start[['indicator_id', 'is_sdg_only', 'actual_start_year']],
on='indicator_id',
how='left'
)
# Assign framework:
# - Jika bukan SDG-only → 'MDGs'
# - Jika SDG-only AND year >= actual_start_year → 'SDGs'
# - Jika SDG-only AND year < actual_start_year → 'MDGs'
self.df_clean['framework'] = np.where(
self.df_clean['is_sdg_only'] & (self.df_clean['year'] >= self.df_clean['actual_start_year']),
'SDGs',
'MDGs'
)
# Drop kolom bantu
self.df_clean = self.df_clean.drop(columns=['is_sdg_only', 'actual_start_year'])
# ----------------------------------------------------------------
# Log verifikasi per indikator
# ----------------------------------------------------------------
self.logger.info(f"\n Verifikasi framework per indikator:")
self.logger.info(f" {'-'*105}")
self.logger.info(
f" {'ID':<5} {'Indicator Name':<52} {'Start':<8} "
f"{'MDGs rows':<12} {'SDGs rows':<12} {'Expected'}"
)
self.logger.info(f" {'-'*105}")
for ind_id, grp in self.df_clean.groupby('indicator_id'): for ind_id, grp in self.df_clean.groupby('indicator_id'):
ind_name = grp['indicator_name'].iloc[0] ind_name = grp['indicator_name'].iloc[0]
pre_sdg = (grp['year'] < self.sdg_start_year).sum()
mdgs_rows = (grp['framework'] == 'MDGs').sum() mdgs_rows = (grp['framework'] == 'MDGs').sum()
sdgs_rows = (grp['framework'] == 'SDGs').sum() sdgs_rows = (grp['framework'] == 'SDGs').sum()
is_sdg_only = ind_name.lower().strip() in SDG_ONLY_KEYWORDS is_sdg_only = ind_name.lower().strip() in SDG_ONLY_KEYWORDS
start_yr = int(grp['year'].min())
if is_sdg_only:
ind_start = sdg_only_start_map.get(ind_id, '?')
expected = f"SDGs from {ind_start}, MDGs before"
else:
expected = "MDGs always"
self.logger.info( self.logger.info(
f" {int(ind_id):<5} {ind_name[:50]:<52} " f" {int(ind_id):<5} {ind_name[:50]:<52} {start_yr:<8} "
f"{pre_sdg:<10} {mdgs_rows:<10} {sdgs_rows:<10} " f"{mdgs_rows:<12} {sdgs_rows:<12} {expected}"
f"{'YES' if is_sdg_only else 'no'}"
) )
fw_summary = self.df_clean['framework'].value_counts() fw_summary = self.df_clean['framework'].value_counts()
@@ -609,23 +622,41 @@ class AnalyticalLayerLoader:
self.logger.info("STEP 7: VERIFY NO GAPS") self.logger.info("STEP 7: VERIFY NO GAPS")
self.logger.info("=" * 80) 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.
# ----------------------------------------------------------------
expected_countries = len(self.selected_country_ids) expected_countries = len(self.selected_country_ids)
verification = self.df_clean.groupby( all_good = True
['indicator_id', 'year'] bad_rows = []
)['country_id'].nunique().reset_index()
verification.columns = ['indicator_id', 'year', 'country_count'] for ind_id, grp in self.df_clean.groupby('indicator_id'):
all_good = (verification['country_count'] == expected_countries).all() actual_start = self.indicator_max_start_map.get(ind_id)
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: if all_good:
self.logger.info( self.logger.info(
f" VERIFICATION PASSED — all combinations have {expected_countries} countries" f" VERIFICATION PASSED — all combinations have {expected_countries} countries"
) )
else: else:
bad = verification[verification['country_count'] != expected_countries] for row in bad_rows[:10]:
for _, row in bad.head(10).iterrows():
self.logger.error( self.logger.error(
f" Indicator {int(row['indicator_id'])}, Year {int(row['year'])}: " f" Indicator {row['indicator_id']}, Year {row['year']}: "
f"{int(row['country_count'])} countries (expected {expected_countries})" f"{row['country_count']} countries (expected {expected_countries})"
) )
raise ValueError("Gap verification failed!") raise ValueError("Gap verification failed!")
@@ -638,13 +669,7 @@ class AnalyticalLayerLoader:
def calculate_norm_value(self): def calculate_norm_value(self):
""" """
Hitung norm_value_1_100 per indikator — min-max normalisasi skala 1-100, Hitung norm_value_1_100 per indikator — min-max normalisasi skala 1-100,
direction-aware. direction-aware, global per indikator (semua negara + semua tahun).
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.
- Skala 1-100 (bukan 0-100) untuk menghindari nilai absolut nol di Looker Studio.
""" """
self.logger.info("\n" + "=" * 80) self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR") self.logger.info("STEP 8: CALCULATE NORM_VALUE_1_100 PER INDICATOR")
@@ -936,9 +961,9 @@ class AnalyticalLayerLoader:
'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_logic' : (
'row-level: year < sdg_start_year → MDGs always; ' 'per-indicator actual_start_year: '
'year >= sdg_start_year + SDG_ONLY_KEYWORDS → SDGs; ' 'SDG-only indicator → SDGs from its own actual_start_year, MDGs before; '
'else (implicit) → MDGs' 'shared/other indicators → MDGs always'
), ),
'sdg_only_keywords_count' : len(SDG_ONLY_KEYWORDS), 'sdg_only_keywords_count' : len(SDG_ONLY_KEYWORDS),
'condition_thresholds' : { 'condition_thresholds' : {
@@ -975,7 +1000,7 @@ class AnalyticalLayerLoader:
self.logger.info("Output: fact_asean_food_security_selected -> fs_asean_gold") 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("Kolom baru: norm_value_1_100 (min-max 1-100, direction-aware)")
self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}") self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
self.logger.info("Framework: year < sdg_start_year → MDGs | SDG_ONLY → SDGs | else → MDGs (implicit)") self.logger.info("Framework: per-indicator actual_start_year (baris year < actual_start_year tetap ada, berlabel MDGs)")
self.logger.info("=" * 80) self.logger.info("=" * 80)
self.load_source_data() self.load_source_data()
@@ -1026,7 +1051,7 @@ if __name__ == "__main__":
print("Output: fact_asean_food_security_selected -> fs_asean_gold") print("Output: fact_asean_food_security_selected -> fs_asean_gold")
print(f"Norm: min-max 1-100 per indicator, direction-aware") print(f"Norm: min-max 1-100 per indicator, direction-aware")
print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}") print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
print(f"Framework: year < sdg_start_year → MDGs | SDG_ONLY → SDGs | else → MDGs (implicit)") print("Framework: per-indicator actual_start_year (baris year < actual_start_year tetap ada, berlabel MDGs)")
print("=" * 80) print("=" * 80)
logger = setup_logging() logger = setup_logging()