Prediksi Inflasi Menggunakan Deep Learning dengan Integrasi Variabel Makroekonomi dan Data Narasi Digital sebagai Variabel Eksogen
Main Article Content
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.
Almosova, A., & Andresen, N. (2023). Nonlinear inflation forecasting with recurrent neural networks. Journal of Forecasting, 42(2), 240–259. https://doi.org/10.1002/for.2901
Arif, E., Herlinawati, E., Devianto, D., Yollanda, M., & Permana, D. (2024). Hybridization of long short-term memory neural network in fractional time series modeling of inflation. Frontiers in Big Data, 6, 1282541. https://doi.org/10.3389/fdata.2023.1282541
Damayanti, R., Irhamah, & Widhianingsih, T. D. A. (2026). Deep Learning-Based Inflation Forecasting Using Macroeconomic Indicators and Digital Narrative Variables. In Proceedings of the International Conference on Applied Mathematics, Statistics, and Data Science (AMLDS).
Das, P. K., & Das, P. K. (2024). Forecasting and analyzing predictors of inflation rate: Using machine learning approach. Journal of Quantitative Economics, 22(2), 493–517. https://doi.org/10.1007/s40953-024-00384-z
Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. https://doi.org/10.1080/07350015.1995.10524599
Eugster, P., & Uhl, M. W. (2024). Forecasting inflation using sentiment. Economics Letters, 236, 111575. https://doi.org/10.1016/j.econlet.2024.111575
Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang, N., Qi, Y., & Xia, J. (2024). China’s inflation forecasting in a data-rich environment: Based on machine learning algorithms. Applied Economics, 57(17), 1995–2020. https://doi.org/10.1080/00036846.2024.2322572
Indonesia, B. (2024). Survei Konsumen: Metodologi dan Indikator. Bank Indonesia. https://www.bi.go.id
Joseph, A., Potjagailo, G., Chakraborty, C., & Kapetanios, G. (2024). Forecasting UK inflation bottom up. International Journal of Forecasting, 40(4), 1521–1538. https://doi.org/10.1016/j.ijforecast.2024.01.001
Statistik, B. P. (2024). Indeks Harga Konsumen dan Inflasi Bulanan Indonesia. Badan Pusat Statistik. https://www.bps.go.id
Stock, J. H., & Watson, M. W. (2003). Forecasting output and inflation: The role of asset prices. Journal of Economic Literature, 41(3), 788–829. https://doi.org/10.1257/jel.41.3.788
Zahara, S., & Sugianto. (2021). Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 24–30. https://doi.org/10.29207/resti.v5i1.2562
