Peramalan Volume Transaksi Uang Elektronik Menggunakan Hybrid SARIMA-LSTM dengan Integrasi Klasifikasi Berita IndoBERT - Nabila Sya’bani Wardana; Wiwik Anggraeni (Building of Informatics, Technology and Science (BITS), 2026)
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.