Naive Bayes Algorithm Classification for Predicting Graduation Rate - Pradani Ayu Widya Purnama; Nurmaliana Pohan; Adiddo Restiady (JURNAL TEKNOLOGI DAN OPEN SOURCE, 2024)
Classification refers to the process of identifying a model or function that clarifies or differentiates concepts or categories of data, with the goal of predicting the class of an object. Naïve Bayes is a machine learning technique that employs probability computations. In this case study, various algorithms are used for modeling classification, and the naïve bayes algorithm is applied to examine the graduation rate. By utilizing this method, accuracy is assessed, which allows for an analysis based on criteria such as School Major, First Choice of College, Second Choice of College, Average Graduation Value, and Graduation Information. The outcome of the computation utilizing the Naïve Bayes Algorithm (Information Systems | Option 1) > (Information Engineering | Option 2) is 53.32% > 0%, which allows us to infer that the First Option of Information Systems and the Second Option of Informatics Engineering yield an Average Score of 75.00, resulting in a Graduation Information status of PASS, thus, Information Pass (Option 1-Information Systems).