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All Journal Jurnas Nasional Teknologi dan Sistem Informasi IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Abdi Masyarakat Indonesia Community Impact and Society Empowerment Journal
Wahid, Arif Mu'amar
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
ENHANCING COLLABORATION DATA MANAGEMENT THROUGH DATA WAREHOUSE DESIGN: MEETING BAN-PT ACCREDITATION AND KERMA REPORTING REQUIREMENTS IN HIGHER EDUCATION Wahid, Arif Mu'amar; Afuan, Lasmedi; Utomo, Fandy Setyo
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.1747

Abstract

In higher education institutions, effective management of collaboration data is crucial for academic reporting and strategic planning. This study addresses the challenges of managing diverse data types and the necessity for streamlined data management to meet BAN-PT accreditation and Kerma reporting requirements. It aims to design and implement a data warehouse utilizing the star schema for improved accessibility and decision-making. Highlighting the development process, special emphasis is placed on the Extract, Transform, Load (ETL) process with Pentaho to assure data integrity and quality. The methodology involves a systematic approach to constructing the data warehouse, aimed at resolving identified challenges through efficient data organization and quality management. Results demonstrate significant enhancements in data accessibility, reporting efficiency, and quality, leading to reduced administrative efforts and improved decision-making. The research also considers the wider implications of such data management systems in academic administration, suggesting the potential of data warehouses in higher education as benchmarks for similar institutional challenges. Future research directions are recommended for optimizing data warehouse designs and adapting to evolving academic standards, underlining the critical role of advanced data management in meeting stringent accreditation and reporting needs, thus providing a model for technology-driven solutions in educational data management.
ANALYSIS OF FACTORS DETERMINING STUDENT SATISFACTION USING DECISION TREE, RANDOM FOREST, SVM, AND NEURAL NETWORKS: A COMPARATIVE STUDY Riyanto, Andi Dwi; Wahid, Arif Mu'amar; Pratiwi, Aniec Anafisah
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2188

Abstract

Student satisfaction is crucial in higher education, impacting student loyalty, retention rates, and institutional reputation. This study addresses the gap in applying advanced machine learning techniques to predict and understand key determinants of student satisfaction. The primary objective is to analyze and predict the factors determining student satisfaction using four machine learning models: Decision Tree, Random Forest, SVM, and Neural Networks. The dataset comprises 2527 entries with seven relevant features. Data preprocessing involved normalization and exploratory data analysis (EDA) to ensure accurate analysis. The Neural Network model achieved the highest accuracy with an MSE of 0.001399, RMSE of 0.037397, MAE of 0.030773, and R² of 0.998154, followed closely by the SVM model. These results suggest that advanced machine learning models, particularly Neural Networks and SVM, are effective in predicting student satisfaction and identifying key areas for improvement. This study contributes to understanding the determinants of student satisfaction using machine learning models, providing practical implications for educational administrators to develop targeted strategies to enhance student satisfaction by focusing on critical factors such as academic support and financial aid. The findings highlight the importance of using advanced predictive techniques to gain deeper insights into student satisfaction, thereby enabling institutions to implement more effective interventions. Future research should explore additional variables and more sophisticated model architectures to further improve predictive accuracy and expand the applicability of these models in educational settings.
Co-Authors Abdul Azis Abdul Jahir Agung Prasetyo Akto Hariawan, Akto Andi Dwi Riyanto Chyntia Raras Ajeng Widiawati Darmono Fandy Setyo Utomo Giat Karyono Haikal, Sultan Ananda Ika Maulita L'ga Tama, Teppei Jordy Lasmedi Afuan Nugroho, Khabib Adi Olivia, Desta Pratama, Satrya Fajri Pratiwi, Aniec Anafisah Prayoga Pribadi Putri, Khanivia Yuniska Sarmini Sarmini Sufranto, Tri Titis Taqwa Hariguna Titi Safitri Maharani Turino, Turino Widyaningsih, Dyah Ayu Yi, Ding Yuli Purwati