<![CDATA[Ground-based solar radiation measurements help solar energy projects and applications. Various models have been developed to estimate solar radiation. Then, several additional models were created using improved machine learning. Currently, estimating solar radiation with the help of hybrid models is more efficient. In this research, the basic concepts of modeling procedures for hybrid between the Autoregressive Integrated Moving Average (ARIMA) and the Multilayer Perceptron (MLP) are used to improve the performance of the ARIMA and MLP models in estimating solar radiation data from a pyranometer sensor installed on the automatic weather station (AWS) at Stasiun Klimatologi Daerah Istimewa Yogyakarta. The test results of the estimation model based on the coefficient of determination (R2) value and root mean square error (RMSE) show that the ARIMA model can provide a high coefficient of determination value in each data splitting scenario. The MLP estimation model shows a coefficient of determination value that is lower than the ARIMA model. On the other hand, MLP is able to improve the RMSE value in the ARIMA model in 70:30 and 90:10 splitting data. Furthermore, the ARIMA-MLP hybrid estimation model is able to improve the RMSE value of the ARIMA and MLP models even though the coefficient of determination value is not as good as the ARIMA model. This research shows that the ARIMA-MLP hybrid model is able to contribute to increasing the accuracy value in RMSE compared to the ARIMA and MLP models in estimating solar radiation sensor data.]]>
