Peramalan Harga Minyak Goreng Sawit di Indonesia: Perbandingan Model ARIMAX-GARCH, SVR, dan LSTM
DOI:
https://doi.org/10.26418/justin.v14i2.97766Keywords:
Harga, Minyak Goreng Sawit, ARIMAX, GARCH, SVR, LSTMAbstract
Fluktuasi harga minyak goreng sawit sebagai komoditas pangan strategis memiliki dampak langsung terhadap inflasi dan stabilitas ekonomi rumah tangga di Indonesia. Meskipun berbagai penelitian telah mengembangkan model peramalan harga berbasis statistik konvensional maupun machine learning, sebagian besar masih membandingkan metode dalam pendekatan yang sama serta jarang mengintegrasikan indikator permintaan berbasis big data. Penelitian ini menawarkan kontribusi baru dengan membandingkan secara langsung pendekatan statistik konvensional ARIMAX–GARCH dan metode machine learning, yaitu Support Vector Regression (SVR) dan Long Short-Term Memory (LSTM), dalam meramalkan harga minyak goreng sawit kemasan di Indonesia, dengan memasukkan Indeks Google Trends sebagai proksi sisi permintaan. Hasil evaluasi menunjukkan bahwa model ARIMAX(2,1,2)–GARCH(1,1) menghasilkan kinerja terbaik dengan RMSE sebesar 1.274,29 dan MAPE 4,89%, dibandingkan dengan model SVR dan LSTM. Keunggulan model ini terletak pada kemampuannya dalam memodelkan heteroskedastisitas dan fenomena volatility clustering yang umum terjadi pada data harga pangan, sehingga menghasilkan estimasi titik dan interval peramalan yang lebih andal. Model terpilih memproyeksikan harga minyak goreng sawit cenderung menurun secara bertahap dari Rp21.692 pada Januari 2025 menjadi sekitar Rp20.988 pada Desember 2025. Hasil penelitian ini dapat dimanfaatkan oleh Kementerian Perdagangan sebagai komponen utama dalam pengembangan sistem peringatan dini harga minyak goreng sawit, dengan memanfaatkan interval kepercayaan sebagai sinyal awal untuk mempertimbangkan intervensi pasar, seperti operasi pasar atau kebijakan Domestic Market Obligation (DMO), guna menjaga stabilitas harga dan ketersediaan pasokan domestik.References
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