PERBANDINGAN AKURASI MODEL SARIMA, ETS, DAN NNETAR PADA PERAMALAN HARGA BERAS ECERAN KOTA BANDUNG TAHUN 2015 - 2025
DOI:
https://doi.org/10.26618/3ryatp32Keywords:
ETS, Harga Beras, NNETAR, Peramalan Deret Waktu, SARIMAAbstract
Tujuan: Penelitian ini bertujuan membandingkan kinerja tiga metode peramalan deret waktu, yaitu Seasonal Autoregressive Integrated Moving Average (SARIMA), Error Trend Seasonal (ETS), dan Neural Network Autoregression (NNETAR), dalam memprediksi harga beras eceran di Kota Bandung periode 2015–2025.
Metode: Penelitian menggunakan pendekatan kuantitatif dengan desain komparatif berbasis analisis deret waktu. Data yang digunakan berupa data sekunder harga beras eceran bulanan yang diperoleh dari Badan Pusat Statistik (BPS) Kota Bandung, mencakup 132 observasi periode Januari 2015 hingga Desember 2025. Data dibagi secara kronologis menjadi data pelatihan periode 2015–2021 (84 observasi) dan data pengujian periode 2022–2025 (48 observasi). Pengujian stasioneritas dilakukan menggunakan uji Augmented Dickey-Fuller (ADF) dan Kwiatkowski-Phillips-Schmidt-Shin (KPSS), pemilihan orde SARIMA berdasarkan nilai AICc, pemilihan arsitektur NNETAR melalui pencarian grid sistematis, serta evaluasi kinerja model menggunakan indikator MAE, RMSE, dan MAPE.
Hasil: Model SARIMA dengan spesifikasi ARIMA(1,1,1)(0,1,1)[12] menghasilkan akurasi peramalan terbaik dengan nilai MAE sebesar 1.651,85, RMSE sebesar 2.036,13, dan MAPE sebesar 10,96%, mengungguli NNETAR (MAPE 12,99%) dan ETS (MAPE 14,28%).
Simpulan: Temuan ini mengonfirmasi bahwa model SARIMA dengan komponen musiman eksplisit lebih efektif dalam menangkap pola tren dan musiman pada data harga komoditas pangan perkotaan, sekaligus memberikan kontribusi empiris bagi pengembangan model peramalan harga pangan dan perumusan kebijakan stabilisasi harga di tingkat kota.
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