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All Journal Jurnal Teknologi Informasi dan Pendidikan International Journal of Multidisciplinary Research of Higher Education (IJMURHICA)
Apriliyanti, Resti
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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Klasifikasi Warna Objek secara Real-Time Menggunakan Optimasi Model CNN MobileNetV2 Apriliyanti, Resti; Kurniadi, Denny; Novaliendry, Dony; Rahmadika, Sandi; Farhan, Muhammad
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.969

Abstract

This research aimed to develop a Convolutional Neural Network (CNN) model for automatic object color classification using MobileNetV2. To determine the optimal configuration, the training process adjusted several hyperparameters, with particular focus on identifying the most suitable learning rate. The dataset consisted of 3,212 images grouped into five color categories: red, green, blue, random (including yellow, orange, and brown), and none (no object detected). Data augmentation techniques were applied to enhance the variety and robustness of the dataset. The model was trained using the Adam optimizer alongside the categorical crossentropy loss function, with various learning rate settings tested during training. Evaluation results showed that the model worked best with a learning rate of 0.0001 and a batch size of 32, with an average accuracy of 94%. To display prediction results in real time, the top-performing model was integrated into a graphical user interface (GUI). These findings demonstrate the effectiveness of the MobileNetV2-based CNN model in recognizing object colors and highlight its suitability for integration into real-time industrial sorting applications
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) Education, Religion Studies, Social Sciences, STEM, Economic, Tourism,
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.
Co-Authors Azmi, Arafil Dony Novaliendry Febriani, Natasya Kurniadi, Denny Muhammad Farhan Nerurkar, Pranav Rahmadika, Sandi Sandra, Randi Proska Syamsi, Alkindi