Peramalan Volume Transaksi Uang Elektronik Menggunakan Hybrid SARIMA-LSTM dengan Integrasi Klasifikasi Berita IndoBERT
-
Nabila Sya’bani Wardana
*
Institut Teknologi Sepuluh Nopember, Surabaya,
Indonesia
- Wiwik Anggraeni Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia (*) Corresponding Author
Abstract
Amid the accelerating transformation of digital payments, the volume of electronic money transactions in Indonesia has grown rapidly, exhibiting volatile patterns and frequent structural breaks. These characteristics make forecasting particularly challenging, even though accurate projections are needed to support monitoring and planning. Existing approaches generally rely on univariate models or macroeconomic variables that suffer from publication lag, rendering them less responsive to rapidly evolving economic shocks. To address these limitations, this study proposes a hybrid SARIMA-LSTM framework that incorporates online news classification, generated using IndoBERT, as an exogenous variable providing more timely signals than macroeconomic indicators. Approximately 13,000 news headlines were collected through web scraping and classified into three impact categories pendorong (driving), penghambat (inhibiting), and informatif (informative) using a fine-tuned IndoBERT model. The classification results were aggregated monthly into exogenous features, with SARIMA capturing the linear trend and seasonal patterns while LSTM modeled the nonlinear residuals. The best-performing model, a hybrid SARIMA-LSTM with the informative news variable, achieved a MAPE of 14.42%, outperforming the hybrid model without exogenous variables (15.24%), the standalone SARIMA (17.77%), and the standalone LSTM (23.37%). The main contribution is to evaluate the hybrid SARIMA-LSTM architecture and asses whether integrating online news classification can serve as a complementary indicator, reducing reliance on lag-affected data. Although the improvement from the news variable was marginal and more pronounced during volatile periods, its consistent superiority indicates that informative news carries nonlinear signals beneficial for the forecasting model.
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References
M. Barroso and J. Laborda, “Digital transformation and the emergence of the Fintech sector: Systematic literature review,” Digital Business, vol. 2, no. 2, Jan. 2022, doi: 10.1016/j.digbus.2022.100028.
W. Widayat, I. Masudin, and N. R. Satiti, “E-Money payment: Customers’ adopting factors and the implication for open innovation,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 6, no. 3, Sep. 2020, doi: 10.3390/JOITMC6030057.
Bank Indonesia, Blueprint Sistem Pembayaran Indonesia 2030: Mengakselerasi Ekonomi Digital Nasional untuk Generasi Mendatang. Jakarta: Bank Indonesia, 2024. [Online]. Available: https://www.bi.go.id/id/publikasi/kajian/Documents/Blueprint-Sistem-Pembayaran-Indonesia-2030.pdf
P. Nailul, H. • Dyah, A. • Rani, S. E. Bhima, and Y. Adhinegara, “Outlook Ekonomi Digital 2025,” 2024. [Online]. Available: www.celios.co.id
A. Yuli Wijaya, I. Mukhlis, and L. Seprillina, “Analisis pengaruh E-money, volume transaksi elektronik dan suku bunga terhadap jumlah uang beredar di Indonesia pada masa sebelum dan sesudah pandemi COVID-19,” Jurnal Ekonomi, Bisnis dan Pendidikan, vol. 1, no. 2, pp. 135–145, Feb. 2021, doi: 10.17977/um066v1i22021p135-145.
I. Nengah Dharma Pradnyandita and A. A. Rohmawati, “Electronic Money Transactions Forecasting with Support Vector Regression (SVR) and Vector Autoregressive Moving Average (VARMA),” Intl. Journal on ICT, vol. 8, no. 1, pp. 69–85, 2022, doi: 10.21108/ijoict.v8i1.632.
U. Mahmudah, S. R. Anggita, and N. A. Suryaningtyas, "Forecasting electronic money trends in Indonesia using neural network models: A comparative analysis," Majalah Ilmiah Matematika dan Statistika (MIMS), vol. 25, no. 1, pp. 1–12, Mar. 2025, doi: 10.19184/mims.v25i1.53688.
C. Andreas, S. Sediono, E. Ana, S. Suliyanto, and M. F. F. Mardianto, “Penerapan Model ARIMAX-GARCH dalam Pemodelan dan Peramalan Volume Transaksi Uang Elektronik di Indonesia,” MUST: Journal of Mathematics Education, Science and Technology, vol. 6, no. 2, p. 241, Dec. 2021, doi: 10.30651/must.v6i2.11214.
C. Fakhri, F. Purwoko, P. N. Farida, S. N. Ramadhani, and V. M. Erson, "Modeling Indonesia's inflation based on e-money transaction volume with local polynomial estimator approach," Indonesian Journal of Statistics and Its Applications, vol. 1, no. 5, Feb. 2022, doi: 10.55324/iss.v1i5.120.
J. T. E. Chapman and A. Desai, “Macroeconomic Predictions Using Payments Data and Machine Learning,” Forecasting, vol. 5, no. 4, pp. 652–683, Dec. 2023, doi: 10.3390/forecast5040036.
M. D. Rizqi, U. Mahmudah, and R. D. Aulia, “Prediksi Tren Penggunaan Electronic Money (E-Money) di Indonesia menggunakan pendekatan Extreme Learning Machines,” Emerging Statistics and Data Science Journal, vol. 3, no. 3, pp. 640–649, Oct. 2025, doi: 10.20885/esds.vol3.iss.3.art17.
E. Chong, C. C. Ho, Z. F. Ong, and H. H. Ong, “Using news sentiment for economic forecasting: a Malaysian case study,” in Machine learning in central banking, vol. 57, Bank for International Settlements, 2022. Tersedia: https://www.bis.org/ifc/publ/ifcb57_17.pdf
J. J. C. Ying, C. C. Liu, V. S. Tseng, W. Zhang, and J. Zhang, “Forecasting leading economic indicators in the US from financial news using multi-task learning,” Soft comput., vol. 29, no. 7, pp. 3641–3657, Apr. 2025, doi: 10.1007/s00500-025-10629-5.
B. Wilie et al., "IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding," in Proc. 1st Conf. Asia-Pacific Chapter Assoc. Comput. Linguistics and 10th Int. Joint Conf. Natural Language Processing, Suzhou, China, Dec. 2020, pp. 843–857, doi: 10.18653/v1/2020.aacl-main.85.
B. Juarto and Yulianto, “Indonesian News Classification Using IndoBert,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 2, pp. 454–460, Feb. 2023. Tersedia: https://ijisae.org/index.php/IJISAE/article/view/2654
D. S. Katemba, F. Bisilisin, Heni, and Sumarlin, "Klasifikasi Judul Berita Online Menggunakan BERT dan IndoBERT," TeIKa, vol. 16, no. 1, pp. 80–87, 2026, doi: 10.36342/3sqzca39.
W. A. Pratiwi, I. M. Sumertajaya, and K. A. Notodiputro, “Stacking Ensemble RNN-LSTM Models for Forecasting the IDR/USD Exchange Rate with Nonlinear Volatility,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 4, pp. 2331–2347, Aug. 2025, doi: 10.52436/1.jutif.2025.6.4.5057.
M. Rizal and S. U. Zuliana, "Forecasting using SARIMA and Bayesian structural time series method for range seasonal time," in Proc. Int. Conf. Data Science and Official Statistics, vol. 2023, no. 1, pp. 382–391, 2023, doi: 10.34123/icdsos.v2023i1.402.
M. Baek and Y. Seo, “Hybrid forecasting of university electricity demand using time series and deep learning,” Energy Build., vol. 347, Nov. 2025, doi: 10.1016/j.enbuild.2025.116400.
X. Du et al., “Application and comparison of ARIMA, LSTM, and ARIMA-LSTM models for predicting foodborne diseases in Liaoning Province,” Front. Big Data, vol. 8, 2025, doi: 10.3389/fdata.2025.1666962.
A. Aiman Dzulfiqar, “Hybrid Sarima-LSTM Model for Predicting Erosion In Butterfly Valve,” Jurnal Ilmu dan Inovasi Fisika, vol. 9, no. 1, pp. 75–88, Feb. 2025, doi: 10.24198/jiif.v9i1.61007.
L. Barbaglia, S. Consoli, and S. Manzan, “Forecasting with Economic News,” Journal of Business and Economic Statistics, vol. 41, no. 3, pp. 708–719, 2023, doi: 10.1080/07350015.2022.2060988.
N. J. Salsabiila and W. Anggraeni, “CNN+BiGRU-Attention Classification and TiDE-PSO Forecasting Approach for Social Media-based Predictive Analysis of Dengue,” International Journal of Intelligent Engineering and Systems, vol. 18, no. 4, pp. 716–735, 2025, doi: 10.22266/ijies2025.0531.47.
H. Aldiah and S. V. V. Lena, “Analisis Faktor-Faktor Yang Mempengaruhi Minat Penggunaan Uang Elektronik Pada Generasi Z,” Jurnal Minfo Polgan, vol. 13, no. 1, pp. 794–803, Jul. 2024, doi: 10.33395/jmp.v13i1.13808.
G. Altansukh and D. R. Osborn, “Using structural break inference for forecasting time series,” Empir. Econ., vol. 63, no. 1, pp. 1–41, Jul. 2022, doi: 10.1007/s00181-021-02137-w.
R. Ningombam, C. Singh, S. Sreekumar, R. Bhakar, and S. Padmanaban, “Box–Cox integrated sARIMA model for day-ahead inertia forecasting,” Electrical Engineering, vol. 107, no. 7, pp. 9135–9153, Jul. 2025, doi: 10.1007/s00202-025-02965-4.
Z. Guo, “Research on the Augmented Dickey-Fuller Test for Predicting Stock Prices and Returns,” Advances in Economics, Management and Political Sciences, vol. 44, no. 1, pp. 101–106, Nov. 2023, doi: 10.54254/2754-1169/44/20232198.
N. A. Achsani, T. Irawan, A. Holis, and H. Hasanah, EKONOMETRIKA Dengan Aplikasi dalam Bidang Ekonomi dan Keuangan. Bogor: PT Penerbit IPB Press, 2023. Accessed: Jun. 15, 2026. [Online]. Available: https://www.researchgate.net/publication/396099756_EKONOMETRIKA_Dengan_Aplikasi_dalam_Bidang_Ekonomi_dan_Keuangan
H. Okut, “Deep Learning for Subtyping and Prediction of Diseases: Long-Short Term Memory,” in Deep Learning Applications, IntechOpen, 2021. doi: 10.5772/intechopen.96180.
V. Kotu and B. Deshpande, “Time Series Forecasting,” in Data Science, Elsevier, 2019, pp. 395–445. doi: 10.1016/B978-0-12-814761-0.00012-5.
H. Khoshvaght, R. R. Permala, A. Razmjou, and M. Khiadani, “A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction,” Dec. 01, 2025, Elsevier Ltd. doi: 10.1016/j.jece.2025.119675.
E. P. Wahyuddin, R. E. Caraka, R. Kurniawan, W. Caesarendra, P. U. Gio, and B. Pardamean, “Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, no. 1, Mar. 2025, doi: 10.1016/j.joitmc.2024.100458.
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