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All Journal Multica Science and Technology Jurnal Ilmu Komputer Aceh
Riyadhul Fajri
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Computer Science & IT Energy Library & Information Science Physics

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Implementation of Ward AHC for Material Clustering Based on Mechanical Parameters Yusuf, Edy; Bakhtiar; Syukriah; Burhanuddin; Riyadhul Fajri
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.977

Abstract

This study aims to implement the Ward Agglomerative Hierarchical Clustering (Ward AHC) algorithm to classify materials based on mechanical parameters, including tensile strength (Su), yield strength (Sy), elastic modulus (E), shear modulus (G), Poisson's ratio (?), and density (?). The clustering results reveal that the data is divided into three main groups with the following distributions: Cluster 1 (321 data points), Cluster 2 (403 data points), and Cluster 3 (828 data points). Each cluster exhibits unique characteristics: Cluster 1 is dominated by materials with low Su and Sy values, moderate E and G values, and light ?. Cluster 2 features materials with very high E values, while Su, Sy, and G values vary. Cluster 3 is characterized by moderate Su values, low Sy values, high E and G values, and light ?. An evaluation using the Silhouette Score yielded a value of 0.492, indicating that the clustering quality is reasonably good, though there is evidence that some data points may lie near the boundaries between clusters.
Klasifikasi Spesies Ikan Koi Berdasarkan Citra Menggunakan Metode YOLOv3-Tiny Dan OpenCV Rauzi Saputra; Imam Muslem; Riyadhul Fajri
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.52

Abstract

Identification of koi fish (Cyprinus carpio) varieties in aquaculture and ornamental fish industries is commonly performed manually through visual observation, making the process subjective, inconsistent, and inefficient, particularly at large production scales. This study aims to develop an automated image-based detection and classification system for koi varieties using the YOLOv3-Tiny algorithm integrated with OpenCV, capable of operating in real-time conditions. The dataset consists of 3,154 images of six koi varieties—Asagi, Bekko, Hikarimono, Kohaku, Sanke, and Showa—which were expanded to 6,360 images through data augmentation techniques. Image labeling and annotation were conducted using Roboflow, while model training was implemented with the Darknet framework in a Google Colab environment supported by GPU acceleration. System performance was evaluated using mean Average Precision (mAP), loss function analysis, and both static image and real-time video testing. Experimental results demonstrate that the YOLOv3-Tiny model is capable of accurately detecting and classifying koi varieties with stable inference speed suitable for real-time applications. The proposed system enhances objectivity, consistency, and efficiency in koi variety identification and shows strong potential for practical implementation in technology-driven ornamental fish farming and trading industries
Co-Authors Bakhtiar Burhanuddin Muslem R, Imam Rauzi Saputra Syukriah Yusuf, Edy