Article Per Year (5 Year)
p-Index From 2021 - 2026
3.422
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Mechatronics, Electrical Power, and Vehicular Technology Jurnal Ketahanan Nasional Sinergi JOURNAL OF MECHANICAL ENGINEERING, MANUFACTURES, MATERIALS AND ENERGY VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Informatika Teknologi dan Sains (Jinteks) Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Journal of Educational Management and Learning Grimsa Journal of Science Engineering and Technology Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada
Erkata Yandri
Universitas Darma Persada
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Educational Management and Learning

 1 
Optimizing University Admissions: A Machine Learning Perspective Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Paristiowati, Maria; Suhendra, Rivansyah; Yandri, Erkata; Satrio, Justinus; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.46

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach Noviandy, Teuku Rizky; Zahriah, Zahriah; Yandri, Erkata; Jalil, Zulkarnain; Yusuf, Muhammad; Mohamed Yusof, Nur Intan Saidaah; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i1.191

Abstract

Education is important for societal advancement and individual empowerment, providing opportunities, developing essential skills, and breaking cycles of poverty. Nonetheless, the path to educational success is marred by challenges such as achieving academic excellence and preventing student dropouts. Early identification of students at risk of dropping out or those likely to excel academically can significantly enhance educational outcomes through tailored interventions. Traditional methods often fall short in precision and foresight for effective early detection. While previous studies have utilized machine learning to predict student performance, the potential for more sophisticated ensemble methods, such as stacked classifiers, remains largely untapped in educational contexts. This study develops a stacked classifier integrating the predictive strengths of LightGBM, Random Forest, and logistic regression. The model achieved an accuracy of 80.23%, with precision, recall, and F1-score of 79.09%, 80.23%, and 79.20%, respectively, surpassing the performance of the individual models tested. These results underscore the stacked classifier's enhanced predictive capability and transformative potential in educational settings. By accurately identifying students at risk and those likely to achieve academic excellence early, educational institutions can better allocate resources and design targeted interventions. This approach optimizes educational outcomes and supports informed policymaking, fostering environments conducive to student success.
Co-Authors Aep Saepul Uyun Amaral, Clizardo Ariati, Ratna Aryati, Ratna Bahri, Ridzky Aulia Bangun Novianto Candra, Arief Faizin, Muhammad Ihsan Nur Fitriani Fitriani Gede Arya Rachman Ghazi Mauer Idroes Hamja, Nasrullah Heriyanto, Hernawan Hilmi, Erik Ibrahim, Riki Firmandha Irvanizam, Irvanizam Isandono, Hafit Kukuh Priyo Pramono Lala, Andi Lodewijk, Dewi Putriani Yogosara Ludji, Omrie Maria Paristiowati Maulana, Aga Mohamed Yusof, Nur Intan Saidaah Muhammad Syukri Nur Muhammad Yanis Muhammad Yusuf Murdiyansah, Novan Murdiyansyah, Novan Naoto Hagino Nizamuddin Nizamuddin Pramono, Kukuh Priyo Ratna Ariati Ratna Ariati Rifki Saiful Riki Firmandha Ibrahim Rinaldi Idroes Riyanto, Andry Rizal F. Hariadi Saiful, Rifki Sasmita, Novi Reandy Satrio, Justinus Sidharta, Rendy Sihombing, Very Sparisoma Viridi Suhendra, Rivansyah Teuku Rizky Noviandy Timba, Ayub Tirta, Andy Uhanto, Uhanto Utami, Mei Budi Viendyasari, Mila Zahriah, Zahriah Zulkarnain Jalil