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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE
Afdhaluddin, Muhamad
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Computer Science & IT

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Decision Support System For Submitting and Evaluating Web-Based Scholarships Using The Topsis Method at SMP Kusuma Raya Afdhaluddin, Muhamad; Rahmi, Lailatur; Amalia, Tasya
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4452

Abstract

This research aims to develop a web-based decision support system for the scholarship application and assessment process. The system is designed to improve efficiency, transparency, and objectivity of selection. The TOPSIS method is used to evaluate potential recipients based on a number of relevant criteria. The system was developed using PHP, MySQL, Visual Studio Code, and XAMPP as the development platform. System testing shows that this application is able to provide accurate scholarship recipient recommendations, so that it can help decision makers in determining recipients more fairly and systematically. With this approach, it is expected that the scholarship selection process will become more structured and reduce the potential for subjectivity in assessment. The results of this research contribute to the application of information technology in supporting a better selection system in the academic environment and scholarship granting institutions.
Development of a Machine Learning-Based Predictive Model for Forecasting Spare Part Requirements at the Warehouse of PT. Setia Karya Transport (Great Giant Foods) Way Lunik Afdhaluddin, Muhamad; Rahmi, Lailatur; Agresty, Elsa Dwi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.5025

Abstract

Efficient spare part management plays a crucial role in supporting operational continuity at PT. Setia Karya Transport (Great Giant Foods). The current spare part forecasting process is still reactive and relies on periodic evaluations, resulting in potential inefficiencies in procurement planning. This study aims to develop a machine learning-based predictive model to forecast spare part requirements using historical transaction data from January to July 2025. The research applied three modeling scenarios: (1) a hybrid model combining Support Vector Regression (SVR), Random Forest, and Statistical methods; (2) pure statistical methods with zero-ratio classification; and (3) the XGBoost algorithm with zero-ratio classification. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. The results showed that the hybrid approach achieved the best performance with an MAE of 2.919 and an RMSE of 8.056, indicating higher prediction accuracy compared to other models. The findings demonstrate that integrating machine learning with statistical approaches can effectively enhance forecasting accuracy and support data-driven decision-making in warehouse management. Keywords : Machine Learning, Forecasting, Spare Part, Random Forest, Support Vector Regression
Co-Authors Agresty, Elsa Dwi Amalia, Tasya Lailatur Rahmi