Model Hibrida ARIMA-Neural Network untuk Peramalan Kasus Tuberkulosis - Agung Setyabudi, Arriza; Etik Zukhronah; Isnandar Slamet (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Tuberculosis (TB) remains a significant public health challenge in Surakarta City, necessitating accurate forecasting methods to support effective and planned control strategies. This study aims to evaluate the performance of the Autoregressive Integrated Moving Average-Neural Network (ARIMA-NN) hybrid model in forecasting monthly TB cases in the Surakarta region. The performance of this hybrid model is further compared with the ARIMA model. The research data used consists of monthly TB case data from January 2019 to September 2024 obtained from the Surakarta City Health Department. The data is divided into two sets: training data from January 2019 to December 2023 and testing data from January 2024 to September 2024. The ARIMA(0,1,1) model was identified as the best model for capturing the linear component of the data, yielding a Mean Absolute Percentage Error (MAPE) of 14.52% on the training data and 16.55% on the testing data. The residuals from the ARIMA(0,1,1) model were then further modeled using a Neural Network with 5 hidden neuron architecture, period lookback 6, and a learning rate of 0.1, to capture the remaining non-linear patterns. The developed ARIMA(0,1,1)-NN hybrid model showed better forecasting performance, with a MAPE value of 14.34% on the training data and 14.48% on the testing data. These results indicate that the ARIMA-NN hybrid approach offers the potential for improved accuracy compared to the ARIMA model in the context of TB case forecasting in Surakarta.