Educational Data Mining: Comparison of Models for Predicting Non-Academic Students - Aldin Febriansyah; Ihsan Fauzi (Southeast Asian Journal on Open and Distance Learning, 2023)
Passing the national assessment test in Bahasa Indonesia for Paket C students is still a challenge, especially for non-academic students. This is caused by various factors, such as educational background, economic conditions, and learning motivation. This research aims to develop a prediction model for Package C students passing the national assessment exam in Indonesian using four machine learning algorithms, namely neural network, logistic regression, support vector machine, and naive bayes. The data used in this study are data on equivalency education exam tryout scores, equivalency education exam scores, schools, and regional information from 1,240 non-academic students who took the Equivalency Education Exam in the Indonesian language subject in the 2020-2021 academic year. This research uses machine learning methods in the context of Educational Data Mining. The preliminary analysis results show that the four machine learning algorithms can be used to predict the graduation of Paket C students with a fairly high accuracy. The neural network algorithm shows the best performance with 57.5% accuracy. SVM, LR, and NB algorithms achieved 56.2%, 54.8%, and 48.4% accuracy, respectively. The results of this study have the potential to increase the pass rate of Package C students on the Indonesian language national assessment test. The prediction model developed can be used to identify students at risk of not graduating, so that appropriate educational interventions can be provided.