Classification of Timely Student Graduation Rates Based on Academic Data Using Naïve Bayes
Klasifikasi Tingkat Kelulusan Mahasiswa Tepat Waktu Berdasarkan Data Akademik Menggunakan Naïve Bayes
DOI:
https://doi.org/10.33050/sensi.v12i2.4531Keywords:
Data Mining, Naïve Bayes, Classification, Student Graduation, RapidMinerAbstract
Timely student graduation is a key indicator of higher education quality; however, many students still experience delays in completing their studies. This research aims to apply the Naïve Bayes algorithm to classify students' timely graduation status based on academic data and to evaluate the resulting model's performance. The dataset consists of 1,687 student records with five attributes: ip1, ip2, ip3, ip4, and tepat as the class label. The research stages include data collection, preprocessing (data cleaning, selection, transformation, and labeling), an 80:20
training-testing split, classification using RapidMiner, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results show an accuracy of 88.72%, precision of 93.59%, recall of 94.19%, and F1-score of 93.89%, with a confusion matrix of 292 True Positives, 7 True Negatives, 20 False Negatives, and 18 False Positives. The high performance on the “Yes” class indicates that the model is very effective at identifying students who graduate on time, although performance on the “No” class remains relatively low due to class imbalance. It is concluded that the Naïve Bayes algorithm performs well and is suitable as a decision-support method for predicting timely student graduation.
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