Penerapan Metode Fuzzy Time Series Markov Chain Untuk Meramalkan Nilai Transaksi Belanja Menggunakan Uang Elektronik di Indonesia - Muhammad Irsadul Ibaad; Meiliyani Siringoringo; Ika Purnamasari; Desi Yuniarti; Suyitno Suyitno (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2025)
Forecasting is an analytical process used to predict future conditions based on historical and current data to minimize errors. The Fuzzy Time Series (FTS) Markov Chain method is effective for handling uncertain, nonlinear, and fluctuating data, making it suitable for forecasting electronic payment transactions in Indonesia. These transactions often show gradual trends, seasonality, and external influences such as policy changes and consumer behavior, leading to data uncertainty that traditional models struggle to capture. A key factor in the FTS method is the interval length, which affects the accuracy of fuzzy set formation. This study compared two interval determination methods: Sturges formula and automatic clustering, to forecast Indonesia’s electronic payment transaction value for September 2024. Results showed that Sturges produced a forecast of Rp52,836.07 billion with a MAPE of 8.51%, while automatic clustering yielded a forecast of Rp55,369.31 billion with a lower MAPE of 3.90%. The findings indicate that the hybrid FTS-Markov Chain approach, especially when combined with automatic clustering, offers better accuracy. It adapts more effectively to the natural structure of the data, making it a more reliable method for forecasting complex and uncertain transaction patterns.