Article Per Year (5 Year)
p-Index From 2020 - 2025
2.601
P-Index
This Author published in this journals
All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Applied Data Sciences Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Journal of Computing Theories and Applications Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology
Sasmita, Novi Reandy
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Other

Published : 18 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.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.
Co-Authors Adha, Zuhra Adila, Wulan Farisa Ahmad Watsiq Maula Aklya, Zatul Apriliansyah, Feby Arif Saputra, Arif Arifin, Mauzatul Asep Rusyana Asshiddiqi, M. Ischaq Nabil Aziza, Zahra Ifma Chongsuvivatwong, Virasakdi Dahlawy, Arriz Dimas Chaerul Ekty Saputra Earlia, Nanda Fikri, Mumtaz Kemal Ghazi Mauer Idroes Hardi, Irsan Hilal, Iin Shabrina Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Iffaty, Athiya Irvanizam, Irvanizam Kamal, Saiful Kemala, Pati Khairul, Mhd Khairul, Moh Khairun Nisa Kruba, Rumaisa Kusumo, Fitranto La Ode Reskiaddin Malfirah, Malfirah Mardalena, Selvi Maria Paristiowati Maulana, Aga Maulydia, Nur Balqis Muhammad Farid Muhammad Subianto Muhammad Yusuf Muliadi Muslem Muslem, Muslem Nazila, Syifa Niode, Nurdjannah Jane Nuzullah, Teuku Muhammad Faiz Putri, Ulayya Rafiqhi, Adis Aufa Rahayu, Latifa RAHAYU, LATIFAH Ramadeska, Siti Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Salsabila, Adinda Saragih, Novita Sari Satrio, Justinus Sofyan, Rahmi Suhendra, Rivansyah Suryadi Suryadi Syakir, Fakhrus Syarafina, Risky Haezah Teuku Rizky Noviandy TRINA EKAWATI TALLEI Ulfa, Elvitra Mutia Ulhaq , Muhammad Zia Ulhaq, Muhammad Zia Utami, Reksi Yandri, Erkata Yarmaliza Zahriah, Zahriah Zurnila Marli Kesuma