PENERAPAN DATA MINING UNTUK SEGMENTASI MENU KOPI BERDASARKAN KARAKTERISTIK PEMINAT MENGGUNAKAN ALGORITMA K-MEANS

Authors

  • Rizki Habibah Universitas Labuhanbatu
  • Hikmah Aldinar Siregar Universitas Labuhanbatu
  • Ade Lestari Hasibuan Universitas Labuhanbatu
  • Yolanda Listia Universitas Labuhanbatu
  • Aldi Rahmansyah Universitas Labuhanbatu
  • M. Idris Sagala Universitas Labuhanbatu
https://doi.org/10.47561/jtik.v19i1.360
This Abstract has been read 43 times

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.



Keywords: data mining, segmentation, coffee menu, K-Means, clustering

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Author Biographies

Rizki Habibah, Universitas Labuhanbatu

Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu

Hikmah Aldinar Siregar, Universitas Labuhanbatu

Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu

Ade Lestari Hasibuan, Universitas Labuhanbatu

Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu

Yolanda Listia, Universitas Labuhanbatu

Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu

Aldi Rahmansyah, Universitas Labuhanbatu

Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu

M. Idris Sagala, Universitas Labuhanbatu

6Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu

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Published

2026-03-19

How to Cite

[1]
R. Habibah, H. A. Siregar, A. L. Hasibuan, Y. Listia, A. Rahmansyah, and M. I. Sagala, “PENERAPAN DATA MINING UNTUK SEGMENTASI MENU KOPI BERDASARKAN KARAKTERISTIK PEMINAT MENGGUNAKAN ALGORITMA K-MEANS”, JTIK, vol. 19, no. 1, pp. 11-20, Mar. 2026.

Issue

Vol. 19 No. 1 (2026): April

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Copyright (c) 2026 Rizki Habibah, Hikmah Aldinar Siregar, Ade Lestari Hasibuan, Yolanda Listia, Aldi Rahmansyah, M. Idris Sagala

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This work is licensed under a Creative Commons Attribution 4.0 International License.