Article Per Year (5 Year)
p-Index From 2021 - 2026
2.177
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) IKRA-ITH Informatika : Jurnal Komputer dan Informatika IKRA-ITH ABDIMAS JISICOM (Journal of Information System, Infomatics and Computing) JURNAL TEKNOLOGI INFORMASI Jurnal Sistem Informasi dan Sains Teknologi Teknomatika: Jurnal Informatika dan Komputer Journal of Computer Science and Information Technology Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada
Mira Febriana Sesunan
Universitas Darma Persada
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Prediction of new student admissions to higher education using support vector machines Neni Purwati; Windya Harieska Pramujati; A. Aviv Mahmudi; Mira Febriana Sesunan; Yahya Yahya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2484-2493

Abstract

Higher education institutions across various regions operate using systems that generate large amounts of data. This data is stored and utilized for strategic decision-making, providing significant business value to these institutions. Support vector machine (SVM) has become popular due to its strong generalization capability, high prediction accuracy, and faster training speed. SVM employs kernels as tuning parameters. This study aims to enhance the accuracy of student admissions prediction in higher education institutions using the SVM classification model. The SVM model was applied to a dataset comprising 5,936 records with four attributes and was evaluated using the use training set, 10-fold cross-validation, and percentage splits of 70%–30% and 80%–20%. Initially, the SVM-kernel model achieved high accuracy but failed to identify any true positive instances, indicating its inability to detect the minority “not accepted” class due to severe class imbalance. After applying class balancing techniques, the model’s performance improved significantly in terms of area under the curve (AUC), F-measure, and Matthews correlation coefficient (MCC), reflecting a more balanced classification between majority and minority classes. The SVM with Pearson VII function-based universal kernel (PUK) and classifier version 4.5 (C4.5) models achieved the best performance, indicating that class balancing effectively enhances both sensitivity and fairness in predictive classification.
Co-Authors A. Aviv Mahmudi Abirahma, Elvina Afifa, Linda Nur Annisa Dita Rianti Astuti, Eka Yuni Aulia, Nabila Darsin Darsin DARSONO Eka Yuni Astuty Endang Ayu Susilawati Eva Novianti Hartini Herianto Husen Asbanu Iksan Nurdin Juan Pratama Neni Purwati Nur Hasanah Nur Syamsiyah Nur Syamsiyah Ramadhan, Mohammad Alfitra Resha Mukti Sari Wulanningsih Setio Windya Harieska Pramujati Yahya Yahya Yahya Yahya Yahya Yahya, Yahya -