Analisis Intensitas Radiasi Matahari terhadap Efisiensi Panel Surya menggunakan Algoritma Support Vector Regression (SVR) dan Naïve Bayes - Ramadoni; Purwaningsih, Sri; Pebralia, Jesi (JFT: Jurnal Fisika dan Terapannya, 2025)
Solar radiation is an alternative energy in the form of heat from electromagnetic waves consisting of electric and magnetic fields. The utilization of solar energy to be converted into electrical energy can be done through solar panels with the photovoltaic effect mechanism. This research aims to analyze the effect of solar radiation intensity on solar panel efficiency using the Support Vector Regression (SVR) and Naïve Bayes algorithms. The research method used is a computational method with Machine Learning techniques. The algorithms used are the Support Vector Regression algorithm and Naïve Bayes. The data used comes from the Jambi Province Meteorology, Climatology, Geophysics (BMKG) Agency. The performance of each model was then evaluated using accuracy metrics to determine the accuracy comparison. The results showed a very strong and positive relationship between solar radiation intensity and solar panel efficiency by 97%. Based on the results, the Naïve Bayes algorithm achieved an accuracy of 96.71%, which shows the model is capable in capturing the relationship between radiation intensity and panel efficiency. Meanwhile, the Support Vector Regression algorithm obtained an accuracy of 80.00 %.