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Sagala, M. Idris
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Computer Science & IT Control & Systems Engineering

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PENERAPAN DATA MINING UNTUK SEGMENTASI MENU KOPI BERDASARKAN KARAKTERISTIK PEMINAT MENGGUNAKAN ALGORITMA K-MEANS Habibah, Rizki; Siregar, Hikmah Aldinar; Hasibuan, Ade Lestari; Listia, Yolanda; Rahmansyah, Aldi; Sagala, M. Idris
Jurnal Teknologi Informasi dan Komunikasi Vol 19 No 1 (2026): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v19i1.360

Abstract

The growth of coffee menu variations requires business owners to understand consumer interest characteristics in a structured manner, while menu data and consumer preference information are often not fully utilized in decision making. This study aims to segment coffee menus based on consumer-interest characteristics using a data-mining approach. The method applied is clustering using the K-Means algorithm implemented in the Orange Data Mining software, with two main attributes: price and interest category. The analysis process includes data preprocessing, determining the optimal number of clusters, and evaluating cluster quality using the silhouette coefficient. The results show that the K-Means algorithm successfully groups coffee menus into three clusters with distinct price ranges and consumer-interest characteristics. The evaluation yields a silhouette coefficient of 0.725, indicating a strong cluster structure with clear separation between groups. Visualization of the clustering results reveals three main segments: an economical cluster characterized by low prices and high consumer interest, a middle cluster with moderate prices and varying levels of interest, and a premium cluster with high prices and consistently strong consumer interest. These segmentation results provide a clear representation of consumer preference patterns and support decision making in product planning and pricing strategies for coffee businesses.
Co-Authors Habibah, Rizki Hasibuan, Ade Lestari Listia, Yolanda Rahmansyah, Aldi Siregar, Hikmah Aldinar