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All Journal M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Knowbase : International Journal of Knowledge in Database
Wizsa, Uqwatul Alma
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Computer Science & IT Education Mathematics

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Classification Of KIP-K Scholarship Using Logistic Regression, Classification Trees, and Boosting Based On Decision Support System Wizsa, Uqwatul Alma; Rahmi, Alya
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 10 No. 1 (2025): Mathline : Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v10i1.837

Abstract

This study addresses the challenge of accurately identifying eligible awardees of the KIP-K scholarship at UIN Sjech M. Djamil Bukittinggi, where scholarship aid requests exceed the allocated funds. The research aims to develop an integrated classification and decision-making model to optimize the selection process. From the 2022 and 2023 scholarship applicant data obtained through AKAMA, preprocessing was conducted, resulting in a final dataset comprising 2,144 records. The dataset includes 14 explanatory variables influencing scholarship eligibility. The study compares three classification methods—logistic regression, classification tree, and boosting—using the 2022 data for training and testing. The SMOTE resampling technique was applied to address class imbalance. The novelty of this research lies in integrating classification analysis with a decision-making system based on the Simple Additive Weighting (SAW) method, enhancing the ranking of applicants based on criteria. The results indicate that logistic regression delivered the best performance in terms of accuracy, sensitivity, and AUC-ROC scores during testing, despite a slight decline in performance when applied to the 2023 dataset. Moreover, integrating logistic regression with SAW significantly improved decision-making precision. The application of logistic regression combined with SAW on the 2023 dataset resulted in a final accuracy of 0.5734 and a balanced accuracy of 0.5820. This integrated framework provides a data-driven, fair, and efficient approach to scholarship allocation. The study highlights the importance of combining predictive models with decision-making systems to ensure equitable and targeted distribution of financial aid to deserving students.
Decision-Making System for KIP IAIN Bukittinggi Scholarship Recipients Using the SAW and TOPSIS Methods Wizsa, Uqwatul Alma; Yuspita, Yulifda Elin; Rahayu, Wikasanti Dwi
Knowbase : International Journal of Knowledge in Database Vol. 2 No. 1 (2022): June 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/ijokid.v2i1.5188

Abstract

The KIP scholarship is one of the scholarships available at IAIN Bukittinggi, and prospective recipients will be chosen based on the number of quotas available. Thus far, the selection process has been carried out by calculating the total value based on the sum of the percentages of each criterion arranged according to the level of importance. The procedure does not include a decision-making system for determining whether or not to accept the KIP scholarship. As a result, a decision support system is required to quickly and accurately determine which students are eligible for scholarships. In this research, the decision-making system compares the SAW and TOPSIS methods, with the latter using normalized weights in calculating the preference value as a determining value for alternative scholarship recipients to be selected. The SAW method was found to be more sensitive than the TOPSIS method in the data for the KIP scholarship 2020 recipients at IAIN Bukittinggi, with a sensitivity value of 96.87 compared to 81.96 for the TOPSIS method. Based on these findings, the SAW method can be recommended as a decision return system for KIP scholarship recipients to study at IAIN Bukittinggi the following year.
Co-Authors Rahayu, Wikasanti Dwi Rahmi, Alya Yulifda Elin Yuspita