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All Journal International Journal of Multidisciplinary Research of Higher Education (IJMURHICA) Journal of Hypermedia & Technology-Enhanced Learning
Syamsi, Alkindi
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Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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The Implementation of the Gale-Shapley Algorithm in School Admission Preferences: An Analysis of Matching Efficiency and Allocation Equity Sandra, Randi Proska; Syamsi, Alkindi; Azmi, Arafil; Febriani, Natasya; Apriliyanti, Resti; Nerurkar, Pranav
International Journal of Multidisciplinary Research of Higher Education Vol 8 No 4 (2025): (October) Theme Education, Religion Studies, Social Sciences, STEM, Economic, Tou
Publisher : Islamic Studies and Development Center in Collaboration With Students' Research Center Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijmurhica.v8i4.409

Abstract

In today’s educational landscape, integrating algorithmic approaches into school admission systems is crucial to ensure fairness, transparency, and efficiency. This study investigates the application of the Gale-Shapley algorithm to address the challenges of student-school matching, which often result in mismatches and inequities. This study aims to explain how the Gale-Shapley algorithm can ensure stable student placement, where no pair of students prefers each other over the post-assignment. Employing a mixed-methods approach, we combined a literature review with a simulation-based implementation using Python. A test case involving four students and four schools was used to validate the algorithm’s performance. The preferences of both students and schools were modeled, and the Gale-Shapley algorithm was applied to generate stable matchings. Authors analysis focused on evaluating the stability, fairness, and efficiency of the outcomes. The results demonstrate that the algorithm consistently produces optimal and conflict-free placements aligned with participant preferences. These findings highlight the algorithm’s potential to enhance the equity and effectiveness of school admission processes, particularly when applied to real-world educational settings. The implications of the discussion show that it supports trust in the admission system, because the stability and transparency of the process increase legitimacy and acceptance by all parties, including students, schools, and educational authorities.
Web-Based Inventory Management System for Educational Training: Integrating EOQ and ARIMA for Data-Driven Learning Syamsi, Alkindi; Irfan, Dedy; Novaliendry, Dony; Sandra, Randi Proska
Journal of Hypermedia & Technology-Enhanced Learning Vol. 4 No. 1 (2026): Journal of Hypermedia & Technology-Enhanced Learning—Future Education
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.218

Abstract

Manual inventory recording and heuristic ordering practices remain common among Micro, Small, and Medium Enterprises (MSMEs), often leading to inaccurate demand estimation, excessive holding costs, and stockouts. This study develops and evaluates a web-based inventory information system that integrates Autoregressive Integrated Moving Average (ARIMA) forecasting with the Economic Order Quantity (EOQ) model to improve decision accuracy and cost efficiency. The system uses CodeIgniter 3 and MySQL and incorporates a Python-based time-series forecasting engine. Historical sales data were modeled using ARIMA, and the optimal specification was selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The ARIMA(1,1,1) model achieved a Mean Absolute Percentage Error (MAPE) of 8.47%, indicating high forecasting accuracy for operational planning. The forecasted annual demand was integrated into the EOQ framework to determine the optimal order quantity, Reorder Point (ROP), and probabilistic Safety Stock. A one-year cost simulation demonstrated that the EOQ-based policy reduced total inventory costs by 22.73% compared with the existing approach. Functional validation through Black-Box testing confirmed full compliance with specified requirements. These findings demonstrate that integrating predictive analytics with classical inventory optimization enhances operational efficiency and reduces total inventory cost. The system provides a practical, data-driven inventory management framework for MSMEs undergoing digital transformation.
Co-Authors Apriliyanti, Resti Azmi, Arafil Dedy Irfan Dony Novaliendry Febriani, Natasya Nerurkar, Pranav Sandra, Randi Proska