Implementasi Sistem Informasi Kluster Penjualan Beras Menggunakan Algoritma K-Means

Authors

  • Syahrul Adi Saputra Universitas Semarang, Indonesia
  • Galet Guntoro Setiaji Universitas Semarang, Indonesia
  • Ahmad Rifa’i Universitas Semarang, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v6i1.17056

Keywords:

Information System, Data Mining, K-Means Clustering, Davies Bouldin Index, Rice Sales

Abstract

Manual processing of rice sales transaction data results in data being archived and cannot be used optimally in decision-making. Therefore, business owners find it difficult to manage stock in sales planning. The K-Means Clustering algorithm was implemented in a web-based information system to create a recommendation feature for the best-selling rice. UD Maju Mapan, located in Demak Regency, was the location for the sales transaction data collection process for the sales period from October 2025 to March 2026. Data was processed using the Min-Max Normalization method and the K-Means algorithm with 3 clusters: premium, standard, and economy. The grouping will automatically appear in the dashboard display of the web-based information system, providing information for decision-making. The results show that the standard cluster has the largest amount of data compared to the premium and economy clusters. A value of 0.5361 is the result of the evaluation process using the Davies Bouldin Index method, which can be interpreted as quite good cluster quality. The rice sales information system is capable of managing and determining sales strategies and stock procurement based on the results of real transaction data analysis.

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Published

11-06-2026

How to Cite

Syahrul Adi Saputra, Galet Guntoro Setiaji, & Ahmad Rifa’i. (2026). Implementasi Sistem Informasi Kluster Penjualan Beras Menggunakan Algoritma K-Means. Arcitech: Journal of Computer Science and Artificial Intelligence, 6(1), 145–162. https://doi.org/10.29240/arcitech.v6i1.17056

Issue

Vol. 6 No. 1 (2026): June 2026

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