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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Alwis Nazir
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : bulletin of computer science research

 1 
Implementasi Data Mining K-Means Clustering Untuk Pengelompokan Produk Keramik Berdasarkan Frekuensi, Volume, dan Jangkauan Penjualan Ferdian Arya Dinata; Alwis Nazir; Fadhilah Syafria; Teddie Darmizal; Eka Pandu Cynthia
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1195

Abstract

Ceramic inventory management at CV. Makmur Bersama has generally relied on intuition or partial sales data, without accounting for purchasing behavior patterns as a whole. This approach simultaneously creates two major risks: overstocking of slow-moving products, which burdens working capital and storage space, and stockouts of high-demand products, which can result in lost sales opportunities. This problem is further compounded by the limitation of stock data, which typically contains only a single quantitative variable such as the number of units sold and is therefore unable to comprehensively capture product demand characteristics, such as how frequently a product is purchased or how broad its customer base is. As a result, restocking decisions and promotional strategies are often poorly targeted. This research applies the K-Means algorithm to cluster ceramic products based on historical sales patterns as a solution to this limitation. Historical sales data from CV. Makmur Bersama for the 2025 period, consisting of 6,328 transactions, was processed into 417 unique products through a feature engineering approach using Frequency, Monetary, and Reach (FMR) namely transaction count, total quantity sold, and unique customer count per product. After outlier detection using the Interquartile Range (IQR) method, 381 products remained for the clustering process. The optimal number of clusters was determined using the Elbow Method, resulting in k=4 as the best cluster count. Evaluation using the Davies-Bouldin Index (DBI) produced a value of 0.8954, categorized as good, and stability testing across five iterations with different random states showed consistent results (DBI standard deviation of 0.0034). The clustering results produced Cluster 1 (190 products, 49.9%) as slow-moving products, Cluster 2 (34 products, 8.9%) as top-performing products with an average transaction frequency of 30.8 times, Cluster 3 (93 products, 24.4%) as potential products, and Cluster 4 (64 products, 16.8%) as products with limited demand. This research provides practical contributions for companies in determining restocking priorities, promotional strategies, and working capital efficiency based on actual sales patterns. This research contributes methodologically through the adaptation of the RFM framework into FMR to better suit real-world data constraints, as well as the integration of the Elbow Method, Davies-Bouldin Index, and stability testing as a comprehensive validation mechanism. Practically, the segmentation results can be directly utilized by the company as a basis for restocking priorities, promotional strategies, and working capital allocation efficiency based on actual sales patterns.
Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means Wenny Tarisa Oktaviany; Fitri Insani; Alwis Nazir; Pizaini Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.608

Abstract

Floods are one of the natural disasters that often occur in Indonesia, especially during the rainy season. This disaster is caused by various factors, both natural and caused by human activities, such as high rainfall, poor drainage systems, land conversion, and suboptimal spatial planning. The impact of floods is very detrimental, both physically and psychologically, including loss of life and damage to property. Therefore, a method is needed to group areas based on their level of vulnerability to flooding. This study aims to group flood disaster areas in Indonesia using the K-Means algorithm. The data used comes from the BNPB Geoportal covering flood events from January 2020 to December 2024, with a total of 7,487 events from 498 areas. Based on the test results obtained using the Silhouette Coefficient, it shows that 2 clusters were selected as the best number of clusters with a Silhouette Coefficient value of 0.8461 which is included in the strong clustering structure. Of the 2 clusters obtained, cluster 1 is a high-risk category consisting of 35 areas, while cluster 2 is a low-risk category consisting of 463 areas. The results of this study can provide information for related parties to improve the efficiency of flood disaster management.
Co-Authors Adrian Maulana Agung Surya Maulana Ana Komaria Baskara Beni Basuki Eka Pandu Cynthia Elin Haerani Elvia Budianita Fadhilah Syafria Ferdian Arya Dinata Fitri Insani Gusti, Siska Kurnia Ihda Syurfi Iis Afrianty Iwan Iskandar Lestari Handayani Lili Rahmawati Lola Oktavia Muhammad Irsyad Muhammad Rifaldo Al Magribi Nining Nur Habibah Nur Iza Nurafni Syahfitri Pizaini Pizaini Qurrata A'yuni Reski Mai Candra Reski Mei Candra Said Nurfan Hidayad Tillah Sephia Pratista Suwanto Sanjaya Syahbudin Hamwar Teddie Darmizal Trisia Intan Berliana Wahyu Cahyadi Wenny Tarisa Oktaviany Yusra Yusra Yusril Hidayat