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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Yuda Permadi
Universitas Pamulang, Tangerang Selatan
Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Integrasi Metode AHP dan Entropy dalam Pengambilan Keputusan Seleksi Beasiswa Perguruan Tinggi Yuda Permadi; Saprudin Saprudin; Perani Rosyani
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.936

Abstract

Multi-criteria decision making (MCDM) is a complex process involving multiple criteria with different levels of importance. One of the most critical aspects of MCDM is the determination of criterion weights, as inappropriate weighting may lead to biased and unrepresentative decision outcomes. This study aims to apply an integrated weighting approach using the Analytic Hierarchy Process (AHP) and the Entropy method in multi-criteria decision making. The research employs a case study of scholarship recipient selection at University X, using decision data consisting of five student alternatives and four evaluation criteria, namely Grade Point Average (GPA), parents’ income, organizational activities, and essay assessment. The AHP method is used to derive subjective weights based on decision-makers’ preferences, while the Entropy method is applied to obtain objective weights based on data variability. Weight integration is performed using the arithmetic mean approach to produce final weights that balance subjective judgment and objective information. The results indicate that the integrated AHP–Entropy weights produce a more balanced distribution compared to single-method weighting. Weight stability is evaluated through comparative analysis of weight variations across methods, demonstrating that the integration reduces subjective dominance and enhances the representation of objective data characteristics. Therefore, the integration of AHP and Entropy is proven to be an effective weighting approach for multi-criteria decision making, particularly in the context of scholarship selection in higher education institutions.
Klasifikasi Batu Permata Berbasis Citra Menggunakan Convolutional Neural Network Perani Rosyani; Oke Hariansyah; Yuda Permadi; Muhamad Rosdiana; Nanang Nanang
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9101

Abstract

Manual gemstone identification still faces several limitations, such as subjective assessment and strong dependence on expert experience, which may lead to misclassification, particularly for gemstones with similar visual characteristics. This study aims to apply a Convolutional Neural Network (CNN) for automatic visual-based gemstone image classification using a limited dataset. The dataset consists of three gemstone classes, namely Alexandrite, Almandine, and Amazonite, with a balanced class distribution. Image preprocessing includes image resizing, pixel value normalization, and data augmentation to increase data variability. The proposed CNN model is a custom architecture composed of three convolutional layers with ReLU activation, followed by max pooling, a fully connected layer with dropout, and a Softmax output layer. Model performance is evaluated using a confusion matrix and classification metrics, including accuracy, precision, recall, and F1-score. Experimental results show that the CNN model achieves a testing accuracy of 93.33% on the limited test dataset with relatively balanced performance across classes. However, analysis of the training and validation curves indicates the presence of overfitting, suggesting that the model’s generalization capability to unseen data remains limited. These findings highlight that the achieved accuracy is conditional on the specific and constrained dataset used. Therefore, future work is recommended to expand dataset size and diversity, apply more comprehensive data augmentation strategies, and explore transfer learning approaches to improve model stability and generalization performance.
Co-Authors Muhamad Rosdiana Nanang Nanang Oke Hariansyah Perani Rosyani Saprudin Saprudin