init new dags

Dockerfile

@@ -1,13 +1,12 @@
 FROM apache/airflow:2.7.1
 
 USER root
-
-# Membuat folder dan membuka akses selebar-lebarnya (777)
-RUN mkdir -p /opt/airflow/logs /opt/airflow/dags /opt/airflow/plugins \
-    && chown -R airflow:root /opt/airflow \
-    && chmod -R 777 /opt/airflow/logs /opt/airflow/dags /opt/airflow/plugins
+# Buat folder scripts dan atur izin akses
+RUN mkdir -p /opt/airflow/scripts
+# Copy folder scripts dari Git ke container
+COPY --chown=airflow:root ./scripts /opt/airflow/scripts
+COPY --chown=airflow:root ./dags /opt/airflow/dags
 
 USER airflow
-
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt

dags/main_pipeline.py

@@ -0,0 +1,17 @@
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from datetime import datetime
+# Import fungsi dari folder scripts
+from scripts.scraper_pddikti import run_scraping_logic
+
+with DAG(
+    dag_id="etl_akademik_separated_v1",
+    start_date=datetime(2026, 3, 3),
+    schedule_interval="@daily",
+    catchup=False
+) as dag:
+
+    task_scraping = PythonOperator(
+        task_id="run_pddikti_scraping",
+        python_callable=run_scraping_logic # Memanggil fungsi eksternal
+    )

scripts/scraper_pddikti.py

@@ -0,0 +1,13 @@
+import requests
+import pandas as pd
+
+def run_scraping_logic():
+    # Contoh scraping
+    url = "http://universities.hipolabs.com/search?country=Indonesia"
+    res = requests.get(url)
+    df = pd.DataFrame(res.json())
+    
+    # Logika cleaning atau upload ke BigQuery ditaruh di sini
+    output_path = "/opt/airflow/logs/data_akademik.csv"
+    df.to_csv(output_path, index=False)
+    return f"Berhasil menarik {len(df)} data."