Klasterisasi Data Penjualan Menggunakan Algoritma K-Mean Dengan RapidMiner

Tiodora Priska Panjaitan; Asmaul  Dwi Akbar; Sabrina Nur Rahmah; Stefani Cinthia Ernadi; Mochammad Akmal Fatoni; Fatkhul Inayah; Uli Vicilia Sitorus

Klasterisasi Data Penjualan Menggunakan Algoritma K-Mean Dengan RapidMiner

Authors

Tiodora Priska Panjaitan Institut Informatika dan Bisnis Darmajaya
Asmaul Dwi Akbar Institut Informatika dan Bisnis Darmajaya
Sabrina Nur Rahmah Institut Informatika dan Bisnis Darmajaya
Stefani Cinthia Ernadi Institut Informatika dan Bisnis Darmajaya
Mochammad Akmal Fatoni Institut Informatika dan Bisnis Darmajaya
Fatkhul Inayah Institut Informatika dan Bisnis Darmajaya
Uli Vicilia Sitorus Institut Informatika dan Bisnis Darmajaya

Keywords:

Clustering, K-Means, Elbow Method, RapidMiner, Data Mining

Abstract

ABSTRACT
This research aims to identify the optimal number of clusters in the dataset using the K-Means algorithm and the Elbow method in Rapidminer software. The method used is K-Means to cluster data and the Elbow method to determine the optimal number of clusters. The results of research using the K-Means algorithm have obtained the optimal number of clusters. From the results of processing test data with the number of clusters (k= 2 – 5), it was found that cluster 2 had the highest number of domestic chicken egg sales compared to cluster 1, namely 41 purchases.

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Published

2025-04-26

How to Cite

Panjaitan, T. P., Asmaul Dwi Akbar, Sabrina Nur Rahmah, Stefani Cinthia Ernadi, Mochammad Akmal Fatoni, Fatkhul Inayah, & Uli Vicilia Sitorus. (2025). Klasterisasi Data Penjualan Menggunakan Algoritma K-Mean Dengan RapidMiner. JoDMApps (Journal of Data Science Methods and Applications), 1(1), 25–32. Retrieved from https://journal.darmajaya.ac.id/index.php/JoDMApps/article/view/889