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Maulana, Aga
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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.
Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Maulana, Aga; Irvanizam, Irvanizam; Jalil, Zulkarnain; Lensoni, Lensoni; Lala, Andi; Abas, Abdul Hawil; Tallei, Trina Ekawati; 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.v1i1.58

Abstract

Artificial intelligence (AI) has emerged as a powerful technology that has the potential to transform education. This study aims to comprehensively understand students' perspectives on using AI within educational settings to gain insights about the role of AI in education and investigate their perceptions regarding the advantages, challenges, and expectations associated with integrating AI into the learning process. We analyzed the student responses from a survey that targeted students from diverse academic backgrounds and educational levels. The results show that, in general, students have a positive perception of AI and believe AI is beneficial for education. However, they are still concerned about some of the drawbacks of using AI. Therefore, it is necessary to take steps to minimize the negative impact while continuing to take advantage of the advantages of AI in education.
Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia Idroes, Rinaldi; Subianto, Muhammad; Zahriah, Zahriah; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Mursyida, Waliam; Zhilalmuhana, Teuku; Idroes, Ghalieb Mutig; Maulana, Aga; Nurleila, Nurleila; Sufriani, Sufriani
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.v1i2.128

Abstract

This study examines the digital transformation in vocational education through the implementation of a Management Information System (MIS) in Banda Aceh, Indonesia. Focused on enhancing educational administration and decision-making, the study provides insightful analysis on the integration of MIS in State Vocational High School (SMK), specifically SMKN 1 and SMKN 3 in Banda Aceh. A purposive sampling method was employed for usability testing. The questionnaire-based usability test revealed high reliability and positive user responses across multiple indicators. Data analysis affirmed the system's high user satisfaction, effectiveness, and ease of use. Despite limitations, the study highlights the significant potential of well-designed MIS in improving operational efficiency and user satisfaction in educational settings. Future research directions include expanding the sample size, conducting longitudinal studies, incorporating qualitative methods, and exploring the impact on educational outcomes, to enhance the generalizability and depth of understanding regarding the role of MIS in education.
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.
Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Zahriah, Zahriah; Paristiowati, Maria; Emran, Talha Bin; Ilyas, Mukhlisuddin; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i2.233

Abstract

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and supporting skill development through adaptive content. It can also help researchers by automating tasks like data analysis and hypothesis generation, making research faster and more efficient. Moreover, generative AI can streamline administrative tasks, improving efficiency across institutions. However, using AI also raises concerns about privacy, bias, academic integrity, and equal access. To address these issues, institutions must establish clear ethical guidelines, ensure data security, and promote fairness in AI use. Training for faculty and AI literacy for students are essential to maximize benefits while minimizing risks. The paper suggests a strategic framework for integrating AI in higher education, focusing on infrastructure, ethical practices, and continuous learning. By adopting AI responsibly, higher education can become more inclusive, engaging, and practical, preparing students for the demands of a technology-driven world.
Co-Authors AA Sudharmawan, AA Abas, Abdul Hawil Abd Rahman, Sunarti Aprianto . Asep Rusyana Dharma, Dian Budi Diah, Muhammad Earlia, Nanda Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Faisal, Farassa Rani Ghazi Mauer Idroes Hardi, Irsan Hewindati, Yuni Tri Hizir Sofyan Husdayanti, Noviana Idroes, Ghadamfar Muflih Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Irvanizam, Irvanizam Kairupan, Tara S. Kemala, Pati Khairan Khairan Khairul, Mhd Khalijah Awang Kusumo, Fitranto Lala, Andi Maria Paristiowati Marwan Marwan Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Adam, Muhammad Muhammad Subianto Muhammad Yusuf Mukhlisuddin Ilyas Mursyida, Waliam Muslem Muslem, Muslem Musvira, Intan Niode, Nurdjannah Jane Nizamuddin Nizamuddin Noviandy, Teuku R. Nurdjannah J. Niode Nurleila, Nurleila Patwekar, Mohsina Prakoeswa, Cita RS. Rahman, Isra Farliadi Ramadeska, Siti Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa Sasmita, Novi Reandy Satrio, Justinus Souvia Rahimah sufriani, sufriani Sugara, Dimas Rendy Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Suryadi Suryadi Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar TRINA EKAWATI TALLEI Yandri, Erkata Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma