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All Journal Journal of Educational Management and Learning Grimsa Journal of Science Engineering and Technology
Trina Ekawati Tallei
Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado 95115, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Education Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Other

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Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Aga Maulana; Ghazi Mauer Idroes; Pati Kemala; Nur Balqis Maulydia; Novi Reandy Sasmita; Trina Ekawati Tallei; Hizir Sofyan; Asep Rusyana
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.
GC-MS Analysis Reveals Unique Chemical Composition of Blumea balsamifera (L.) DC in Ie-Jue Geothermal Area Nur Balqis Maulydia; Khairan Khairan; Trina Ekawati Tallei; Ethiene Castellucci Estevam; Mohsina Patwekar; Fazlin Mohd Fauzi; Rinaldi Idroes
Grimsa Journal of Science Engineering and Technology Vol. 1 No. 1 (2023): October 2023
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v1i1.6

Abstract

Blumea balsamifera (L.) DC. or Sembung is a flowering plant belonging to the genus Blumea of the family Asteraceae. Many pharmacological activities of this plant show potential in human therapy. In this study, an investigation was conducted on the ethanolic extract of B. balsamifera collected from a geothermal area known as Ie-Jue, in Aceh Province, Indonesia. The results showed that the ethanolic extract of B. balsamifera contained secondary metabolites of flavonoids and tannins. Chemical constituents of ethanolic extracts B. balsamifera further analysis using gas chromatography-mass spectrometry (GC-MS) show that active compounds from this plant was Proximadiol (C15H28O2) with relative area 41.76%. This research underscores the compelling potential of the Ie-Jue geothermal area as a promising reservoir of flora owing to the plant's adaptability to geothermal extremities.
Co-Authors Aga Maulana Asep Rusyana Ethiene Castellucci Estevam Fazlin Mohd Fauzi Ghazi Mauer Idroes Hizir Sofyan Khairan Khairan Mohsina Patwekar Novi Reandy Sasmita Nur Balqis Maulydia Pati Kemala Rinaldi Idroes