Analisis Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Sentimen Ulasan Aplikasi E-Commerce
- R Immanuel Giovanni Italiano Pecaro Universitas Semarang, Semarang, Indonesia
- Galet Guntoro Setiaji Universitas Semarang, Semarang, Indonesia
-
Ahmad Rifa'i
*
Universitas Semarang, Semarang,
Indonesia
(*) Corresponding Author
Abstract
The growing digital technology has driven the rapid growth of E-Commerce applications, which is characterized by the number of similar applications available on the Google Play Store. This phenomenon has led to an increase in people's need for convenience in online shopping, as well as showing increasingly fierce competition in the digital marketplace. Reviews on E-Commerce applications in the Google Play Store often serve as a basis for users to decide whether to download an application. These reviews provide valuable insights, allowing users to assess whether the application is worth downloading. Shopee, one of the largest E-Commerce applications in Indonesia, currently has more than 15 million ratings and reviews on the Google Play Store. This study aims to compare the performance of the K-Means and K-Medoids algorithms in clustering numerical data from application reviews. Clustering was performed using the Clustering technique based on two numerical variables, namely score and thumbsupcount, to provide an initial overview of user opinion trends regarding the application. The dataset, consisting of 500 reviews, was collected from the Google Play Store in December 2024. The results Davies-Bouldin Index of the study indicate that K-Means outperforms K-Medoids, with a comparison score of 0.457 to 0.803.
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