ARIMA and LSTM Comparison for Forecasting Healthcare Service Costs in Bogor - Rizal Rizal Ashari Nampira; Jenal Mutakin Sambas; Ika Nur Laily Fitriana; Liyu Adhi Kasari Sulung (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
The National Health Insurance (JKN) program, managed by BPJS Kesehatan, has experienced a significant increase in healthcare service costs, particularly at Advanced Referral Healthcare Facilities (FKRTL). This study aims to compare the forecasting accuracy of ARIMA and Long Short-Term Memory (LSTM) methods in predicting healthcare service costs in FKRTL Bogor from January 2014 to October 2024. The data, sourced from BPJS Kesehatan Branch Bogor, were analyzed using time series approaches. Model evaluation was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results show that for 80% of training data, LSTM produced a MAPE of 8.85% and RMSE of IDR 6.98 billion, slightly outperforming ARIMA (0,1,1) with MAPE of 10.28% and RMSE of IDR 6.67 billion. For the 20% testing data, LSTM demonstrated significantly better accuracy, with an MAPE of 12.97% and RMSE of IDR 15.52 billion, compared to ARIMA’s MAPE of 24.22% and RMSE of IDR 30.76 billion. Therefore, LSTM is considered more effective for short- to medium-term forecasting of JKN healthcare costs, particularly when dealing with complex and non-linear patterns.