Statistical Characteristics For Identification Defect of Solar Panel with Naive Bayes

Energy that comes from the sun is energy without limits and never runs out. This energy is alternative energy that can be converted into electrical energy, namely by using solar cells. But people who live in remote areas will have difficulty getting electricity. Solar panels are an alternative power source. Solar panels are an alternative way to produce electricity. The production of good solar panels is an important thing that must be done to produce the desired electrical energy. The uncontrolled production process causes various types of defects that appear in solar panels. This study applies the Bayes theorem to classify data by estimating the probability that tuple X is in a class. Using thirty samples consisting of fifteen images of undamaged solar panels and fifteen images. The level of accuracy of image processing for identification of flawed solar panel textures by the Naive Bayesian Classifier method or Simple Bayesian Classifier is around eighty three percent. The results of this study are expected to be used as a reference for the initial detection system of damage that occurs on the surface of the Solar Panel.