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All Journal Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Prosiding Seminar Nasional Indonesia
Razpa Arya Wardana
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Humanities Industrial & Manufacturing Engineering Social Sciences Other

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Metode System Literature Review Untuk Analisis Penggunaan TIK Sebagai Media Pembelajaran Shafira Ramadiani Herliza; Faradiba Aurel Yasmin; Nanda Salma Zhafira; Razpa Arya Wardana
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 3 No. 2 (2023): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v3i2.499

Abstract

Information and communication technology or what is often called ICT is a device that can process and produce data or information. ICT can also be a tool and means to disseminate or publish such data and information. This study aims to determine the effectiveness and also the impact of using information and communication technology (ICT) as a medium in learning for students and staff to improve the quality of education in Indonesia. The method used in this study is the SLR or System Literature Review method. This SLR obtains data by collecting, analyzing, and also synthesizing previous literature that has the same research topic. This method can allow researchers to gain a comprehensive understanding where the data or information obtained includes evidence that is relevant and in accordance with the research topic. The results obtained from the formulation of the problem in this study are that ICT is an important use in improving teaching and learning activities in Indonesia. This increase in learning is certainly balanced by collaboration and good interaction between educators and students to use ICT in the process.
KLASIFIKASI TINGKAT NILAI MATEMATIKA SISWA MENGGUNAKAN METODE MACHINE LEARNING BERBASIS FAKTOR SOSIAL DAN PERILAKU Razpa Arya Wardana
Prosiding Seminar Nasional Indonesia Vol. 3 No. 3 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The use of data in education is increasingly important to support data-driven decision making, particularly in monitoring students’ learning progress in mathematics. This study aims to classify students’ mathematics achievement levels based on social, behavioral, and academic factors using machine learning methods. The dataset used is the Student Performance Data Set from the UCI Machine Learning Repository, specifically the student-mat.csv file, which contains 395 student records with 33 attributes. The final mathematics grade (G3) is grouped into three categories: low, medium, and high. The research methodology follows the CRISP-DM approach, which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. Model development is carried out using Logistic Regression, Random Forest, and XGBoost algorithms. The evaluation results show that the Random Forest algorithm achieves the best performance, with an accuracy of 0.5570 and an F1-score of 0.4975, outperforming the other algorithms. Feature analysis indicates that prior academic failures, school support, social activities, and students’ absenteeism levels have a significant influence on mathematics achievement. This study is expected to help schools identify at-risk students earlier and support the planning of more targeted learning interventions.
Co-Authors Faradiba Aurel Yasmin Nanda Salma Zhafira Shafira Ramadiani Herliza