izu/airflow-coolify
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

translate indo

Browse Source
This commit is contained in:
Debby
2026-06-07 09:10:14 +07:00
parent ca1e0d3949
commit 777865ffe1
1 changed files with 77 additions and 189 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/bigquery_analytical_layer.py
+77 -189
View File
@@ -13,10 +13,7 @@ Filtering Order:
ADDED: ADDED:
- Kolom indicator_name_id dan pillar_name_id (terjemahan Bahasa Indonesia) - Kolom indicator_name_id dan pillar_name_id (terjemahan Bahasa Indonesia)
- Kolom country_name_id (terjemahan Bahasa Indonesia nama negara) - Kolom country_name_id (terjemahan Bahasa Indonesia nama negara)
- Terjemahan indikator via Google Translate (deep-translator), tanpa dict statis
FIXED:
- Nama pilar "Access" -> "Akses" (konsisten di semua mapping)
- Nama negara ASEAN dalam Bahasa Indonesia
""" """
import pandas as pd import pandas as pd
@@ -40,43 +37,40 @@ from scripts.bigquery_helpers import (
save_etl_metadata, save_etl_metadata,
) )
from google.cloud import bigquery from google.cloud import bigquery
from deep_translator import GoogleTranslator
# ============================================================================= # =============================================================================
# TRANSLATION DICTIONARIES # TRANSLATION DICTIONARIES (country & pillar only, indikator via Google Translate)
# ============================================================================= # =============================================================================
COUNTRY_NAME_ID_MAP: dict = { COUNTRY_NAME_ID_MAP: dict = {
# Nama resmi -> Bahasa Indonesia "Brunei Darussalam" : "Brunei Darussalam",
"Brunei Darussalam" : "Brunei Darussalam", "Cambodia" : "Kamboja",
"Cambodia" : "Kamboja", "Indonesia" : "Indonesia",
"Indonesia" : "Indonesia", "Lao People's Democratic Republic" : "Laos",
"Lao People's Democratic Republic" : "Laos", "Lao PDR" : "Laos",
"Lao PDR" : "Laos", "Malaysia" : "Malaysia",
"Malaysia" : "Malaysia", "Myanmar" : "Myanmar",
"Myanmar" : "Myanmar", "Philippines" : "Filipina",
"Philippines" : "Filipina", "Singapore" : "Singapura",
"Singapore" : "Singapura", "Thailand" : "Thailand",
"Thailand" : "Thailand", "Timor-Leste" : "Timor-Leste",
"Timor-Leste" : "Timor-Leste", "Viet Nam" : "Vietnam",
"Viet Nam" : "Vietnam", "Vietnam" : "Vietnam",
"Vietnam" : "Vietnam",
} }
PILLAR_TRANSLATION_ID: dict = { PILLAR_TRANSLATION_ID: dict = {
# 4 pilar utama Food Security — "Access" -> "Akses" (FIXED, bukan "Keterjangkauan")
"Availability" : "Ketersediaan", "Availability" : "Ketersediaan",
"Access" : "Akses", "Access" : "Akses",
"Utilization" : "Pemanfaatan", "Utilization" : "Pemanfaatan",
"Stability" : "Stabilitas", "Stability" : "Stabilitas",
"Sustainability" : "Keberlanjutan", "Sustainability" : "Keberlanjutan",
# Variasi penulisan huruf kecil
"availability" : "Ketersediaan", "availability" : "Ketersediaan",
"access" : "Akses", "access" : "Akses",
"utilization" : "Pemanfaatan", "utilization" : "Pemanfaatan",
"stability" : "Stabilitas", "stability" : "Stabilitas",
"sustainability" : "Keberlanjutan", "sustainability" : "Keberlanjutan",
# Variasi dengan prefix "Food"
"Food Availability" : "Ketersediaan Pangan", "Food Availability" : "Ketersediaan Pangan",
"Food Access" : "Akses Pangan", "Food Access" : "Akses Pangan",
"Food Utilization" : "Pemanfaatan Pangan", "Food Utilization" : "Pemanfaatan Pangan",
@@ -84,140 +78,6 @@ PILLAR_TRANSLATION_ID: dict = {
"Food Sustainability": "Keberlanjutan Pangan", "Food Sustainability": "Keberlanjutan Pangan",
} }
INDICATOR_TRANSLATION_ID: dict = {
# -------------------------------------------------------------------------
# AVAILABILITY
# -------------------------------------------------------------------------
"Average dietary energy supply adequacy (percent) (3-year average)":
"Kecukupan rata-rata pasokan energi makanan (persen) (rata-rata 3 tahun)",
"Average value of food production (constant 2014-2016 thousand US$) (3-year average)":
"Nilai rata-rata produksi pangan (ribu US$ konstan 2014-2016) (rata-rata 3 tahun)",
"Share of dietary energy supply derived from cereals, roots and tubers (percent) (3-year average)":
"Proporsi pasokan energi makanan dari serealia, akar, dan umbi-umbian (persen) (rata-rata 3 tahun)",
"Average protein supply (g/cap/day) (3-year average)":
"Rata-rata pasokan protein (g/kapita/hari) (rata-rata 3 tahun)",
"Average supply of protein of animal origin (g/cap/day) (3-year average)":
"Rata-rata pasokan protein hewani (g/kapita/hari) (rata-rata 3 tahun)",
"Cereal import dependency ratio (percent) (3-year average)":
"Rasio ketergantungan impor sereal (persen) (rata-rata 3 tahun)",
"Percent of arable land equipped for irrigation (percent) (3-year average)":
"Persentase lahan pertanian yang dilengkapi irigasi (persen) (rata-rata 3 tahun)",
"Crop production index (2014-2016 = 100)":
"Indeks produksi tanaman pangan (2014-2016 = 100)",
"Livestock production index (2014-2016 = 100)":
"Indeks produksi peternakan (2014-2016 = 100)",
"Value of food imports over total merchandise exports (percent) (3-year average)":
"Nilai impor pangan terhadap total ekspor barang (persen) (rata-rata 3 tahun)",
"Food production variability (constant 2014-2016 thousand US$ per capita)":
"Variabilitas produksi pangan (ribu US$ konstan 2014-2016 per kapita)",
"Food supply variability (kcal/cap/day)":
"Variabilitas pasokan pangan (kkal/kapita/hari)",
# -------------------------------------------------------------------------
# ACCESS
# -------------------------------------------------------------------------
"Gross domestic product per capita, PPP (constant 2017 international $)":
"Produk domestik bruto per kapita, PPP (internasional konstan 2017 US$)",
"Domestic food price level index (2015 = 1.00)":
"Indeks tingkat harga pangan domestik (2015 = 1,00)",
"Domestic food price volatility index":
"Indeks volatilitas harga pangan domestik",
"Prevalence of undernourishment (percent) (3-year average)":
"Prevalensi kekurangan gizi (persen) (rata-rata 3 tahun)",
"Number of people undernourished (million) (3-year average)":
"Jumlah penduduk kekurangan gizi (juta jiwa) (rata-rata 3 tahun)",
"Depth of the food deficit (kcal/capita/day) (3-year average)":
"Kedalaman defisit pangan (kkal/kapita/hari) (rata-rata 3 tahun)",
"Percentage of population using at least basic drinking water services (percent)":
"Persentase penduduk yang menggunakan layanan air minum dasar (persen)",
"Percentage of population using safely managed drinking water services (percent)":
"Persentase penduduk yang menggunakan layanan air minum yang dikelola dengan aman (persen)",
"Percentage of population using at least basic sanitation services (percent)":
"Persentase penduduk yang menggunakan layanan sanitasi dasar (persen)",
"Percentage of population using safely managed sanitation services (percent)":
"Persentase penduduk yang menggunakan layanan sanitasi yang dikelola dengan aman (persen)",
"Access to electricity (percent of rural population)":
"Akses listrik (persen penduduk pedesaan)",
"Proportion of population with access to electricity (percent)":
"Proporsi penduduk dengan akses listrik (persen)",
"Road infrastructure index":
"Indeks infrastruktur jalan",
"Rail lines density (total route-km per 100 square km of land area)":
"Kepadatan jalur kereta api (total rute-km per 100 km2 lahan)",
"Gross national income per capita (Atlas method, current US$)":
"Pendapatan nasional bruto per kapita (metode Atlas, US$ terkini)",
"Food Insecurity Experience Scale (FIES)":
"Skala Pengalaman Ketidakamanan Pangan (FIES)",
# -------------------------------------------------------------------------
# UTILIZATION
# -------------------------------------------------------------------------
"Prevalence of severe food insecurity in the total population (percent) (3-year average)":
"Prevalensi kerawanan pangan berat pada total penduduk (persen) (rata-rata 3 tahun)",
"Prevalence of severe food insecurity in the male adult population (percent) (3-year average)":
"Prevalensi kerawanan pangan berat pada penduduk laki-laki dewasa (persen) (rata-rata 3 tahun)",
"Prevalence of severe food insecurity in the female adult population (percent) (3-year average)":
"Prevalensi kerawanan pangan berat pada penduduk perempuan dewasa (persen) (rata-rata 3 tahun)",
"Prevalence of moderate or severe food insecurity in the total population (percent) (3-year average)":
"Prevalensi kerawanan pangan sedang atau berat pada total penduduk (persen) (rata-rata 3 tahun)",
"Prevalence of moderate or severe food insecurity in the male adult population (percent) (3-year average)":
"Prevalensi kerawanan pangan sedang atau berat pada penduduk laki-laki dewasa (persen) (rata-rata 3 tahun)",
"Prevalence of moderate or severe food insecurity in the female adult population (percent) (3-year average)":
"Prevalensi kerawanan pangan sedang atau berat pada penduduk perempuan dewasa (persen) (rata-rata 3 tahun)",
"Number of severely food insecure people (million) (3-year average)":
"Jumlah penduduk yang mengalami kerawanan pangan berat (juta jiwa) (rata-rata 3 tahun)",
"Number of severely food insecure male adults (million) (3-year average)":
"Jumlah laki-laki dewasa yang mengalami kerawanan pangan berat (juta jiwa) (rata-rata 3 tahun)",
"Number of severely food insecure female adults (million) (3-year average)":
"Jumlah perempuan dewasa yang mengalami kerawanan pangan berat (juta jiwa) (rata-rata 3 tahun)",
"Number of moderately or severely food insecure people (million) (3-year average)":
"Jumlah penduduk yang mengalami kerawanan pangan sedang atau berat (juta jiwa) (rata-rata 3 tahun)",
"Number of moderately or severely food insecure male adults (million) (3-year average)":
"Jumlah laki-laki dewasa yang mengalami kerawanan pangan sedang atau berat (juta jiwa) (rata-rata 3 tahun)",
"Number of moderately or severely food insecure female adults (million) (3-year average)":
"Jumlah perempuan dewasa yang mengalami kerawanan pangan sedang atau berat (juta jiwa) (rata-rata 3 tahun)",
"Percentage of children under 5 years of age who are stunted (modelled estimates) (percent)":
"Persentase anak di bawah 5 tahun yang mengalami stunting (estimasi model) (persen)",
"Number of children under 5 years of age who are stunted (modeled estimates) (million)":
"Jumlah anak di bawah 5 tahun yang mengalami stunting (estimasi model) (juta jiwa)",
"Percentage of children under 5 years affected by wasting (percent)":
"Persentase anak di bawah 5 tahun yang mengalami wasting (persen)",
"Number of children under 5 years affected by wasting (million)":
"Jumlah anak di bawah 5 tahun yang mengalami wasting (juta jiwa)",
"Percentage of children under 5 years of age who are overweight (modelled estimates) (percent)":
"Persentase anak di bawah 5 tahun yang mengalami kelebihan berat badan (estimasi model) (persen)",
"Number of children under 5 years of age who are overweight (modeled estimates) (million)":
"Jumlah anak di bawah 5 tahun yang mengalami kelebihan berat badan (estimasi model) (juta jiwa)",
"Prevalence of anemia among women of reproductive age (15-49 years) (percent)":
"Prevalensi anemia pada perempuan usia reproduksi (15-49 tahun) (persen)",
"Number of women of reproductive age (15-49 years) affected by anemia (million)":
"Jumlah perempuan usia reproduksi (15-49 tahun) yang menderita anemia (juta jiwa)",
"Prevalence of obesity in the adult population (18 years and older) (percent)":
"Prevalensi obesitas pada penduduk dewasa (18 tahun ke atas) (persen)",
"Prevalence of exclusive breastfeeding among infants 0-5 months of age (percent)":
"Prevalensi pemberian ASI eksklusif pada bayi usia 0-5 bulan (persen)",
"Minimum dietary diversity for women (MDD-W) (percent)":
"Keragaman pola makan minimum untuk perempuan (MDD-W) (persen)",
# -------------------------------------------------------------------------
# STABILITY
# -------------------------------------------------------------------------
"Cereal import dependency ratio (percent)":
"Rasio ketergantungan impor sereal (persen)",
"Political stability and absence of violence/terrorism (index)":
"Stabilitas politik dan tidak adanya kekerasan/terorisme (indeks)",
"Domestic food price volatility":
"Volatilitas harga pangan domestik",
"Per capita food supply variability (kcal/cap/day)":
"Variabilitas pasokan pangan per kapita (kkal/kapita/hari)",
"Percentage of arable land equipped for irrigation (percent)":
"Persentase lahan pertanian yang dilengkapi irigasi (persen)",
"GDP per capita growth (annual %)":
"Pertumbuhan PDB per kapita (% tahunan)",
"GDP growth (annual %)":
"Pertumbuhan PDB (% tahunan)",
}
def translate_country(name: str) -> str: def translate_country(name: str) -> str:
"""Terjemahkan nama negara ke Bahasa Indonesia. Fallback ke nama asli.""" """Terjemahkan nama negara ke Bahasa Indonesia. Fallback ke nama asli."""
@@ -226,13 +86,6 @@ def translate_country(name: str) -> str:
return COUNTRY_NAME_ID_MAP.get(name.strip(), name) return COUNTRY_NAME_ID_MAP.get(name.strip(), name)
def translate_indicator(name: str) -> str:
"""Terjemahkan nama indikator ke Bahasa Indonesia. Fallback ke nama asli."""
if not name:
return name
return INDICATOR_TRANSLATION_ID.get(name, name)
def translate_pillar(name: str) -> str: def translate_pillar(name: str) -> str:
"""Terjemahkan nama pillar ke Bahasa Indonesia. Fallback ke nama asli.""" """Terjemahkan nama pillar ke Bahasa Indonesia. Fallback ke nama asli."""
if not name: if not name:
@@ -240,6 +93,27 @@ def translate_pillar(name: str) -> str:
return PILLAR_TRANSLATION_ID.get(name, name) return PILLAR_TRANSLATION_ID.get(name, name)
def translate_all_indicators(indicator_names: list[str], logger: logging.Logger) -> dict[str, str]:
"""
Terjemahkan SEMUA nama indikator ke Bahasa Indonesia via Google Translate.
Setiap indikator diterjemahkan satu per satu (no batch).
Fallback ke nama asli jika terjemahan gagal.
"""
translator = GoogleTranslator(source='en', target='id')
results = {}
for name in indicator_names:
try:
translated = translator.translate(name)
results[name] = translated
logger.info(f" [OK] {name[:60]:<62} -> {translated}")
except Exception as e:
results[name] = name
logger.warning(f" [FAIL] {name[:60]:<62} -> fallback (error: {e})")
return results
# ============================================================================= # =============================================================================
# ANALYTICAL LAYER CLASS # ANALYTICAL LAYER CLASS
# ============================================================================= # =============================================================================
@@ -258,7 +132,7 @@ class AnalyticalLayerLoader:
Kolom tambahan: Kolom tambahan:
- country_name_id : terjemahan Bahasa Indonesia dari country_name - country_name_id : terjemahan Bahasa Indonesia dari country_name
- indicator_name_id : terjemahan Bahasa Indonesia dari indicator_name - indicator_name_id : terjemahan Bahasa Indonesia dari indicator_name (via Google Translate)
- pillar_name_id : terjemahan Bahasa Indonesia dari pillar_name - pillar_name_id : terjemahan Bahasa Indonesia dari pillar_name
""" """
@@ -439,10 +313,12 @@ class AnalyticalLayerLoader:
self.logger.info(f"\n [+] Valid: {len(valid_combinations):,}") self.logger.info(f"\n [+] Valid: {len(valid_combinations):,}")
self.logger.info(f" [-] Removed: {len(removed_combinations):,}") self.logger.info(f" [-] Removed: {len(removed_combinations):,}")
df_valid = pd.DataFrame(valid_combinations) df_valid = pd.DataFrame(valid_combinations)
df_valid['key'] = df_valid['country_id'].astype(str) + '_' + df_valid['indicator_id'].astype(str) df_valid['key'] = df_valid['country_id'].astype(str) + '_' + df_valid['indicator_id'].astype(str)
self.df_clean['key'] = (self.df_clean['country_id'].astype(str) + '_' + self.df_clean['key'] = (
self.df_clean['indicator_id'].astype(str)) self.df_clean['country_id'].astype(str) + '_' +
self.df_clean['indicator_id'].astype(str)
)
original_count = len(self.df_clean) original_count = len(self.df_clean)
self.df_clean = self.df_clean[self.df_clean['key'].isin(df_valid['key'])].copy() self.df_clean = self.df_clean[self.df_clean['key'].isin(df_valid['key'])].copy()
@@ -656,49 +532,58 @@ class AnalyticalLayerLoader:
]].copy() ]].copy()
# ------------------------------------------------------------------ # ------------------------------------------------------------------
# TAMBAHAN: kolom terjemahan Bahasa Indonesia # Terjemahkan negara & pillar via dict statis
# country_name_id : terjemahan Bahasa Indonesia dari country_name [BARU]
# indicator_name_id : terjemahan Bahasa Indonesia dari indicator_name
# pillar_name_id : terjemahan Bahasa Indonesia dari pillar_name
# ------------------------------------------------------------------ # ------------------------------------------------------------------
analytical_df['country_name_id'] = analytical_df['country_name'].apply(translate_country) analytical_df['country_name_id'] = analytical_df['country_name'].apply(translate_country)
analytical_df['indicator_name_id'] = analytical_df['indicator_name'].apply(translate_indicator) analytical_df['pillar_name_id'] = analytical_df['pillar_name'].apply(translate_pillar)
analytical_df['pillar_name_id'] = analytical_df['pillar_name'].apply(translate_pillar)
# Log negara yang belum punya terjemahan # ------------------------------------------------------------------
# Terjemahkan SEMUA indikator unik via Google Translate (deep-translator)
# ------------------------------------------------------------------
all_indicators = analytical_df['indicator_name'].unique().tolist()
self.logger.info(
f"\n [TRANSLATE] Menerjemahkan {len(all_indicators)} indikator "
f"via Google Translate..."
)
indicator_translation_map = translate_all_indicators(all_indicators, self.logger)
analytical_df['indicator_name_id'] = analytical_df['indicator_name'].map(
indicator_translation_map
)
# Fallback ke nama asli jika map menghasilkan NaN
analytical_df['indicator_name_id'] = analytical_df['indicator_name_id'].fillna(
analytical_df['indicator_name']
)
# ------------------------------------------------------------------
# Log warning negara/pillar yang tidak punya terjemahan
# ------------------------------------------------------------------
no_trans_ctr = analytical_df[ no_trans_ctr = analytical_df[
analytical_df['country_name_id'] == analytical_df['country_name'] analytical_df['country_name_id'] == analytical_df['country_name']
]['country_name'].unique() ]['country_name'].unique()
if len(no_trans_ctr) > 0: if len(no_trans_ctr) > 0:
self.logger.warning( self.logger.warning(
f" [TRANSLATION] {len(no_trans_ctr)} country/countries tidak ada di kamus " f" [TRANSLATION] {len(no_trans_ctr)} negara tidak ada di kamus "
f"(menggunakan nama asli): {list(no_trans_ctr)}" f"(menggunakan nama asli): {list(no_trans_ctr)}"
) )
# Log indikator yang belum punya terjemahan (fallback ke nama asli)
no_trans_ind = analytical_df[
analytical_df['indicator_name_id'] == analytical_df['indicator_name']
]['indicator_name'].unique()
if len(no_trans_ind) > 0:
self.logger.warning(
f" [TRANSLATION] {len(no_trans_ind)} indicator(s) tidak ada di kamus "
f"(menggunakan nama asli): {list(no_trans_ind)[:5]}"
)
no_trans_pil = analytical_df[ no_trans_pil = analytical_df[
analytical_df['pillar_name_id'] == analytical_df['pillar_name'] analytical_df['pillar_name_id'] == analytical_df['pillar_name']
]['pillar_name'].unique() ]['pillar_name'].unique()
if len(no_trans_pil) > 0: if len(no_trans_pil) > 0:
self.logger.warning( self.logger.warning(
f" [TRANSLATION] {len(no_trans_pil)} pillar(s) tidak ada di kamus " f" [TRANSLATION] {len(no_trans_pil)} pillar tidak ada di kamus "
f"(menggunakan nama asli): {list(no_trans_pil)}" f"(menggunakan nama asli): {list(no_trans_pil)}"
) )
# ------------------------------------------------------------------
# Sort & pastikan tipe data konsisten
# ------------------------------------------------------------------
analytical_df = analytical_df.sort_values( analytical_df = analytical_df.sort_values(
['year', 'country_name', 'pillar_name', 'indicator_name'] ['year', 'country_name', 'pillar_name', 'indicator_name']
).reset_index(drop=True) ).reset_index(drop=True)
# Pastikan tipe data konsisten
analytical_df['country_id'] = analytical_df['country_id'].astype(int) analytical_df['country_id'] = analytical_df['country_id'].astype(int)
analytical_df['country_name'] = analytical_df['country_name'].astype(str) analytical_df['country_name'] = analytical_df['country_name'].astype(str)
analytical_df['country_name_id'] = analytical_df['country_name_id'].astype(str) analytical_df['country_name_id'] = analytical_df['country_name_id'].astype(str)
@@ -713,7 +598,7 @@ class AnalyticalLayerLoader:
analytical_df['year'] = analytical_df['year'].astype(int) analytical_df['year'] = analytical_df['year'].astype(int)
analytical_df['value'] = analytical_df['value'].astype(float) analytical_df['value'] = analytical_df['value'].astype(float)
self.logger.info(f" Kolom yang disimpan: {list(analytical_df.columns)}") self.logger.info(f"\n Kolom yang disimpan: {list(analytical_df.columns)}")
self.logger.info(f" Total rows: {len(analytical_df):,}") self.logger.info(f" Total rows: {len(analytical_df):,}")
# Log sample terjemahan negara # Log sample terjemahan negara
@@ -726,7 +611,9 @@ class AnalyticalLayerLoader:
for _, r in sample_ctr.iterrows(): for _, r in sample_ctr.iterrows():
self.logger.info(f" {r['country_name']:<35} -> {r['country_name_id']}") self.logger.info(f" {r['country_name']:<35} -> {r['country_name_id']}")
# ------------------------------------------------------------------
# Schema BigQuery # Schema BigQuery
# ------------------------------------------------------------------
schema = [ schema = [
bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"),
@@ -768,6 +655,7 @@ class AnalyticalLayerLoader:
'layer' : 'gold', 'layer' : 'gold',
'columns' : 'id + name + name_id (Looker Studio ready)', 'columns' : 'id + name + name_id (Looker Studio ready)',
'added_columns' : ['country_name_id', 'indicator_name_id', 'pillar_name_id'], 'added_columns' : ['country_name_id', 'indicator_name_id', 'pillar_name_id'],
'indicator_translation': 'Google Translate via deep-translator',
}), }),
'validation_metrics' : json.dumps({ 'validation_metrics' : json.dumps({
'fixed_countries' : len(self.selected_country_ids), 'fixed_countries' : len(self.selected_country_ids),