Implementation of Data Mining for Prediction of Student Waiting Time for Students Industrial Engineering Undergraduate Study Program at Telkom University Using Naive Bayes
Abstract
Telkom University, a leading institution in Indonesia, aims to produce competitive graduates. The Industrial Engineering program at the university is focused on maintaining a low average waiting time for graduates to preserve accreditation. In 2023, the Faculty of Industrial Engineering (FRI) saw an increase in the average waiting time to 3.89 months, compared to 3.72 months in 2022. For the Industrial Engineering program, the waiting time rose to 4.16 months. Factors contributing to this increase include poor English proficiency, extended study duration, and involvement in non-academic activities. To address this issue, the final project aims to develop a prediction model using the Naïve Bayes Algorithm to forecast student waiting times. The model employs data mining techniques, utilizing attributes such as gender, study duration, GPA, English Proficiency (EPrT) Score, and Student Activity Transcript (TAK) points. Data from the tracer study of 2016-2018 alumni were used, split into 80% for training and 20% for testing. The model achieved an accuracy of 65.95%, with precision and recall rates of 65% and 97%, respectively. A predictive dashboard was developed, allowing manual input and Excel data uploads. This tool helps the Head of the Industrial Engineering Program monitor and predict waiting times, aiding in decision-making and strategy development for improved academic management.
Keyword: Dashboard, Data Mining, Naïve Bayes Classifier, Prediction, Waiting Time
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