Comparative Analysis of the Fuzzy Time Series Chen and Rungge Kutta Felhberg Methods for Forecasting the Number of HIV/AIDS in the Province of North Sumatra - Wahono, Sielve Suwanda; Rakhmawati, Fibri (Journal of Computer Networks, Architecture and High Performance Computing, 2025)
The increase in HIV/AIDS cases in North Sumatra Province requires accurate forecasting methods to support prevention and control programs. Accurate predictions of the number of cases will help stakeholders design more targeted interventions and allocate resources effectively. This study aims to compare the performance of the Chen Fuzzy Time Series method and the Runge-Kutta Fehlberg numerical method in forecasting the number of HIV/AIDS cases in North Sumatra Province. The data used are monthly HIV/AIDS case data obtained from the North Sumatra Provincial Health Office. The Chen Fuzzy Time Series method is applied to capture patterns in data that are uncertain and ambiguous, while the RKF method is used to solve the logistic growth model that represents the development of cases. Forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results showed that the RKF method produced a lower MAPE value compared to the Chen Fuzzy Time Series method, indicating higher prediction accuracy. The RKF method provides more stable predictions for the next three months and is closer to the actual trend, while the Chen Fuzzy Time Series method shows slightly larger deviations but remains useful for imprecise data. In conclusion, both methods can be used for HIV/AIDS case forecasting, but the RKF method is proven to be superior in accuracy for the data used in this study.