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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Ermanto Ermanto
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Analisis Klaster Penyebaran Berat Produk Mesin Sachet Menggunakan Metode Algoritma K-Means Ermanto Ermanto; Nurhadi Surojudin
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.766

Abstract

This study aims to analyze the distribution patterns of sachet machine product weights using the K-Means algorithm as a clustering technique. The dataset consists of 940 entries of primary production records, each containing ten weight measurement samples per production cycle. The data underwent a cleaning process to ensure the absence of missing values, duplicates, and outliers, followed by the selection of relevant attributes (product weight samples) and transformation using Min-Max normalization to scale all variables within the 0–1 range. The clustering process was performed iteratively by updating the centroids until convergence was achieved. The evaluation results indicate that the optimal number of clusters is three (k=3) with a Silhouette Coefficient of 0.55, reflecting a good balance between intra-cluster homogeneity and inter-cluster separation. Cluster 1 represents products with relatively low weights (8.00–8.18 grams), Cluster 2 includes medium-weight products (8.19–8.34 grams), and Cluster 3 consists of high-weight products (8.36–8.98 grams). Overall, the product weights tend to be stable with low variation, although some anomalies were observed in certain machines. These findings demonstrate that the K-Means algorithm can effectively classify product weight data, providing valuable insights for quality control, product variation identification, and minimizing risks of deviation from production standards.
Segmentasi Perilaku Pemustaka Menggunakan DBSCAN untuk Optimalisasi Layanan Perpustakaan Digital Candra Naya; Ermanto Ermanto; Unggul Prima Dhani
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10533

Abstract

The rapid growth of digital libraries has generated increasingly large borrowing transaction data, creating the need for analytical techniques to understand user behavior patterns and support data-driven library management. This study aims to cluster library users based on their borrowing behavior using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify user segments according to their borrowing activity. The study employed the Book-Crossing Dataset, consisting of 278,858 rating transactions, 271,379 user records, and 271,360 book records. The research methodology included Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature standardization using StandardScaler, ε parameter selection through the K-Distance Graph, DBSCAN clustering, cluster evaluation using the Silhouette Score, and visualization using Principal Component Analysis (PCA). The experimental results indicate that ε = 0.5 and MinPts = 5 produced three clusters, with 244 users identified as noise. The obtained Silhouette Score of 0.5996 demonstrates a reasonably good clustering quality. Furthermore, the resulting clusters successfully represent users with low, moderate, and very high borrowing activities, providing valuable insights for developing personalized library services, improving book recommendation systems, supporting collection development, and facilitating data-driven decision-making to enhance the overall quality of digital library services.
Co-Authors Candra Naya Nurhadi Surojudin Unggul Prima Dhani