<![CDATA[The power generated by Solar Power Plants (Pembangkit Listrik Tenaga Surya/PLTS) from time to time is fluctuating due to the influence of weather and other external conditions. This study predicts the output power of PLTS Sumalata in North Gorontalo Regency with data analytics on datasets obtained from measurements at 2 plants in PLTS Sumalata. Data analytics to predict the output power of PLTS Sumalata is using a multiple linear regression approach, which is applied by implementing the Cross-industry standard for data mining (CRISP-DM) process model. The tools used are the Weka 3.0 application and Jupyter Notebook with the Python programming language. With data analytics using Weka 3.0 on datasets obtained from measurements at 2 plants in PLTS Sumalata, multiple linear regression equations were obtained as well as evaluation of prediction results using Correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). The equation formed from the prediction of the output power in Plant 1 is Y = -22216632810.1123 - 771640073.1888 X1 + 2349039057.8254 X2 -25796134709.3552 X3. While the equation formed from the prediction of the output power in Plant 2 is Y = -2784.107 + 300.0146 X1 – 173.7016 X2 + 21773.3845 X3. Based on the test, the correlation coefficient on the Plant 1 dataset is 0.52 and the Plant 2 dataset is 0.92. Those can be concluded that the irradiation data, module temperature, and ambient temperature have a significant effect of 52% on the output power generated in the PLTS system at Plant 1 and 92% on Plant 2. Then the MAE, RMSE, RAE, and RRSE values in the Plant 1 dataset are higher than Plant 2, while the relationship between the independent variables and the dependent variables in the Plant 2 dataset is stronger than the Plant 1 dataset. In order to improve the accuracy of the prediction that can be used for evaluating the performance of the PLTS system, measurement data with a minimum measurement duration of one year is needed to be able to represent seasonal conditions throughout the year, such as the dry season, rainy season, and extreme weather conditions.]]>
