Prediction Analysis of Package C Student Graduation at the Bollo DMansel Community Learning Activity Center (PKBM) with the Naïve Bayes Algorithm Method

Abstract

Education plays a crucial role in improving the quality of human resources and is the key to a nation's progress. Bollo DMansel Community Learning Activity Center (PKBM) in West Papua provides a Paket C Equivalency Education program to help those who are underserved by formal education. The main challenge in this program is to increase student graduation rates. This research aims to analyze and predict the graduation of Paket C students at PKBM Bollo DMansel using the Naive Bayes algorithm method. The data used includes historical student data from 2021 to 2023, with a total of 128 students. The research steps include data collection, data pre-processing, Naive Bayes algorithm application, and prediction model evaluation. The results show that the Naive Bayes algorithm can provide graduation prediction with fairly high accuracy. The factors that most influence student graduation were identified, including attendance, test scores, and participation in activities. This research makes a real contribution to improving the quality of education at PKBM Bollo DMansel by providing a prediction tool to identify students at risk of not graduating so that timely intervention can be provided.