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All Journal INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Ahmad Rifqi
Universitas Nasional, Indonesia
Religion Humanities Social Sciences

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COMPARISON OF DECISION TREE AND RANDOM FOREST ALGORITHMS IN PREDICTING STUDENT GRADUATION BASED ON ACADEMIC DATA Marlan Marlan; Ahmad Rifqi; Agus Iskandar
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 3 No. 3 (2025): MARCH
Publisher : Adisam Publisher

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Abstract

This research aims to compare the performance of the Decision Tree and Random Forest algorithms in predicting student graduation based on academic data. By utilizing data such as Grade Point Average (GPA), the number of credit hours, and course grades, this study focuses on analyzing the accuracy of both algorithms in predicting students who are at risk of not graduating on time. The results of the study indicate that the Random Forest algorithm achieves higher accuracy compared to the Decision Tree, particularly in terms of recall and precision. While Decision Tree is simpler and easier to interpret, it tends to have overfitting issues that can affect prediction results. In contrast, Random Forest overcomes these issues by producing more stable predictions through an ensemble process. This study is expected to contribute to the development of student graduation prediction systems in educational institutions. As such, institutions can use these findings as a foundation for designing intervention strategies for students at risk of not graduating on time.
DECISION SUPPORT SYSTEM WITH MABAC METHOD TO DETERMINE THE BEST STUDENTS Rima Tamara Aldisa; Ahmad Rifqi
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 1 No. 6 (2024): INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Publisher : Adisam Publisher

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This research aims to apply the Multi Attributive Border Approximation Area Comparison (MABAC) method in the process of selecting the best students in the educational environment. MABAC, as a multi-criteria decision making method, provides a systematic and objective approach to evaluating and comparing various attributes possessed by students. In this study, the criteria considered include academic, extracurricular, personality and social skills aspects. Data was collected from academic records, teacher assessments, and observations of student involvement in school activities. The MABAC method is used to calculate the relative weight of each criterion and determine a comprehensive score for each student. The research results show that the MABAC method is effective in providing a holistic and multifaceted assessment of student performance. In addition, this method also helps in identifying strengths and areas of development for each student, thereby providing valuable insight for teachers and educators in the coaching process. This research concludes that the application of MABAC can be a useful tool in the educational evaluation process, especially in determining the best students with a more objective and structured approach.
Co-Authors Agus Iskandar Marlan Marlan Rima Tamara Aldisa