Work place: Universitas Insan Pembangunan Indonesia, Jl. Raya Serang KM 10, Tangerang, Indonesia
E-mail: nurasiah8@ipem.ac.id
Website: https://orcid.org/0009-0004-2447-2720
Research Interests:
Biography
Nurasiah The author was born in Tangerang on July 9, 1979, is a Permanent Lecturer in the Information Systems Study Program, Faculty of Computer Science, Universitas Insan Pembangunan Indonesia (UNIPI), the author completed his undergraduate education at Gunadarma University Jakarta, majoring in Information Systems and completed his Masters in Information Systems Management (MMSI) with a concentration in Software Engineering at Gunadarma University, the author is also currently completing a Doctoral Program in Computer Science (S3) at Gunadarma University, majoring in Information Technology.
By Giri Reksa Guritno Winanti Beby Tiara Andi Rukmana Nurasiah
DOI: https://doi.org/10.5815/ijieeb.2025.05.02, Pub. Date: 8 Oct. 2025
Many study programs at universities face issues, including students experiencing delays in graduation, which hinders the completion of their studies on time. These delays in student graduation contribute to a decrease in the accreditation score of the Information Systems program. One solution to address this issue is to develop a data-mining-based system to monitor and utilize student progress data by predicting their graduation status using the C4.5 Decision Tree algorithm. This research process involves several stages: problem analysis, data and system design, coding, testing, and finally, maintenance. The outcome of this research is the implementation of the C4.5 algorithm to predict students' timely and delayed graduation. The data used includes records of students who graduated in 2021 and 2022. The acceptance rate, calculated using a confusion matrix, demonstrates an accuracy level of 92.16%, based on a dataset of 119 training data points and 51 testing data points, or 70% training to 30% testing ratio. The results of this research and testing indicate that the C4.5 Decision Tree algorithm is highly suitable for predicting student graduation outcomes.
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