FB Prophet Algorithm Based on Clustering for Stock Price Prediction - Meti Despasari; Rizka Pitri (Program Studi Statistika Fakultas MIPA UNM, 2026)
Extreme volatility in banking stocks like PT Bank Central Asia Tbk (BBCA) decreases single forecasting model accuracy due to high data heterogeneity. This study aims to analyze BBCA stock price prediction accuracy using the FB Prophet algorithm mediated by K-Means Clustering preprocessing. A quantitative time-series method was applied to monthly data from 2014–2025. Results show that K-Means integration (k=3) effectively resolves data heterogeneity. Globally, the FB-Prophet model yielded a Mean Absolute Percentage Error (MAPE) of 20.34%. However, cluster-based evaluation demonstrated superior accuracy during transition phases (MAPE 9.83%) and low-price phases (MAPE 10.13%), dropping the average cluster error to 16.22%. Accuracy decreased only during highly volatile peak price phases (MAPE 28.70%). The 12-month projection for 2026 indicates a stable, conservative linear growth trend, closing at Rp8,532.34. Conclusively, this hybrid Clustering-Forecasting approach provides a more comprehensive and accurate prediction mapping based on distinct market phases.