{
  "data": {
    "schema_version": "1.0",
    "id": "cand-food-inflation-nowcasting",
    "title": "Nowcasting & Peramalan Tekanan Inflasi Volatile Foods Berbasis Indikator Nilai Tukar Petani (NTP)",
    "topic_id": "topic-food-security-inflation",
    "provenance": {
      "acquisition_method": "bps_mcp",
      "source_type": "bps_mcp",
      "source_name": "BPS Inflasi IHK & Kerangka Sampel Area Padi",
      "retrieved_at": "2026-03-05",
      "query_or_tool": "BPS MCP WebAPI Subject 3 / 1498 / Var 1",
      "verification_status": "verified"
    },
    "status": "shortlisted",
    "problem_statement": "Lonjakan inflasi komponen makanan (volatile foods) berdampak langsung pada penurunan daya beli masyarakat rentan dan stabilitas moneter daerah, namun intervensi operasi pasar TPID sering terlambat karena menunggu rilis resmi IHK.",
    "signal_ids": [
      "sig-food-price-volatility"
    ],
    "potential_dataset_ids": [
      "ds-bps-food-security-inflation-monthly"
    ],
    "potential_target": "Inflasi Kelompok Makanan Bulanan MoM / YoY (Var ID 1890) pada 90 Kota IHK",
    "potential_features": [
      "Nilai Tukar Petani Gabungan & Tanaman Pangan (Var ID 10 & 14)",
      "Indeks Harga Diterima Petani / It (Var ID 11)",
      "Indeks Harga Dibayar Petani / Ib (Var ID 12)",
      "Luas Panen & Produksi Padi KSA (Var ID 1498 / 23)",
      "Inflasi Umum Bulanan Lag 1–3 Bulan (Var ID 1)"
    ],
    "scores": {
      "relevance": 0.95,
      "data_evidence": 0.9,
      "impact": 0.95,
      "data_quality": 0.85,
      "temporal_spatial_coverage": 0.8,
      "coverage": 0.8,
      "ml_potential": 0.85,
      "novelty": 0.75,
      "feasibility": 0.85
    },
    "confidence": "high",
    "limitations": [
      "Perubahan tahun dasar IHK pada awal 2024 membutuhkan penyambungan indeks (splicing)"
    ],
    "leakage_risks": [
      "Fitur inflasi umum bulan berjalan tidak boleh digunakan untuk memprediksi inflasi kelompok makanan pada bulan yang sama"
    ],
    "decision": "shortlisted",
    "decision_reason": "Data runtun waktu bulanan 2018–2024 (84 bulan × 34 provinsi / 90 kota) sangat ideal untuk pemodelan multivariate time series forecasting (SARIMAX, LSTM, SVR).",
    "project_slug": null
  },
  "meta": {
    "id": "cand-food-inflation-nowcasting",
    "timestamp": "2026-09-30T08:06:09.687Z"
  },
  "errors": []
}