Prediksi Inflasi Menggunakan Deep Learning dengan Integrasi Variabel Makroekonomi dan Data Narasi Digital sebagai Variabel Eksogen
DOI:
https://doi.org/10.58344/locus.v5i7.5792Keywords:
Deep Learning, Dummy Structural Break, LSTM, Naratif Digital, Ablation StudyAbstract
Penelitian ini memperluas studi pendahuluan penulis ablation study enam model LSTM untuk peramalan inflasi Indonesia (manuscript submitted ke AMLDS 2026, under review) dengan mengevaluasi apakah variabel dummy structural break (Lebaran, COVID-19, kenaikan BBM) meningkatkan performa model yang mengintegrasikan variabel makroekonomi dan naratif digital. Menggunakan dataset yang sama (Januari 2005-Desember 2024, 240 observasi bulanan), penelitian ini memperkenalkan M3_Naratif_Dummy sebagai model baru yang mengombinasikan Google Trends Index dan IEH Bank Indonesia dengan tiga dummy structural break. Tujuh model dibandingkan: ARIMA, VAR, M1 LSTM Baseline, M2 LSTM Numerik, M3 LSTM Naratif, M3 LSTM Naratif+Dummy, dan M4 LSTM Full (N=10 replikasi, T*=6). Hasil menunjukkan M3 LSTM Naratif tetap menjadi model terbaik (RMSE=0,7823, sMAPE=17,72%, std=0,53%), sementara M3_Naratif_Dummy mencapai sMAPE=22,98% dengan varian 8,4 kali lebih tinggi (std=4,43%). Pada lookback optimal T=6, kedua model konvergen ke performa hampir setara (sMAPE 17,37% vs 17,57%), dan uji Diebold-Mariano mengonfirmasi tidak ada perbedaan signifikan (p=0,845). Seluruh model LSTM secara signifikan mengungguli VAR (p<0,05). Temuan utama: variabel naratif digital secara implisit menangkap informasi structural break, sehingga penambahan dummy eksplisit bersifat redundan dan mendestabilisasi model LSTM peramalan inflasi Indonesia.
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