Analysis of Profit Results From The Use of Plts (Solar Power Plant) in Lolo Wano Village Using the Naive – Bayes Classifier Method - Oskar Ana Rato; Gergorius Kopong Pati; Katarina Yunita Riti (Institut Teknologi dan Bisnis (ITB) Semarang, 2024)
The newest renewable energy source in the world and one of the most reasonably priced is solar energy. Because solar energy has so many benefits all year round, it can be a cost-effective energy source when used, especially since it is so abundant globally. to produce electricity by converting sun energy. The equator-based nation of Indonesia boasts an abundance of solar energy resources, with an average daily solar radiation intensity of about 4.8 kwh/m2. However, there is an abundance of solar-based energy sources that can be utilized. Especially in Lolo Wano Village, where the intensity of solar radiation is quite high, it is an option to develop a Solar Power Plant (PLTS) as a solution to electrical energy needs. In order to specifically identify the class of unknown object labels, classification techniques are employed since they are able to identify models that distinguish between different data classes or data ideas. In the meantime, the Naïve Bayes algorithm takes into account multiple factors that will influence a decision's final result in order to forecast future opportunities based on data that has already been collected. The information utilized comes from observations made by the LOLO WANO VILLAGE PLTS Community (PLTS). The data gathered from the satisfaction survey will be divided into two categories: training data and testing data. The testing data's accuracy will be evaluated using the output of the training data model. The classification findings demonstrate that, with the maximum level of accuracy at 87.50%, the Naïve Bayes algorithm is appropriate for gauging student satisfaction with online learning.