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All Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Teknik Informatika Unika Santo Thomas (JTIUST)
Muhammad Rafi Haidar Arsyad
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Computer Science & IT Decision Sciences, Operations Research & Management

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Klusterisasi Data Review Pengguna Aplikasi Marketplace blibli.com dengan Algoritma K-Means dan K-Medoids Muhammad Rafi Haidar Arsyad; Sulastri, Sulastri
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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Abstract

The marketplace serves as a platform for consumers to engage in online shopping. With the increasing use of marketplaces, the role of application reviews becomes increasingly crucial. Reviews provided by application users are a significant source of information for assessing customer satisfaction with the application. This research aims to categorize review data from the Blibli marketplace application using the K-Means Clustering and K-Medoids Clustering methods. The dataset used consists of customer reviews from the Blibli application spanning from 2022 to 2023, totaling 17,255 reviews. The research results indicate that both methods yield four optimal clusters in the review data. Frequently occurring words such as 'application,' 'goods,' and 'shopping' are visualized in a word cloud, and the clustering results are presented in a cluster plot. The obtained findings aim to enhance the service quality of the Blibli application.
Klusterisasi Data Review Pengguna Aplikasi Marketplace blibli.com dengan Algoritma K-Means dan K-Medoids Muhammad Rafi Haidar Arsyad; Sulastri, Sulastri
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The marketplace serves as a platform for consumers to engage in online shopping. With the increasing use of marketplaces, the role of application reviews becomes increasingly crucial. Reviews provided by application users are a significant source of information for assessing customer satisfaction with the application. This research aims to categorize review data from the Blibli marketplace application using the K-Means Clustering and K-Medoids Clustering methods. The dataset used consists of customer reviews from the Blibli application spanning from 2022 to 2023, totaling 17,255 reviews. The research results indicate that both methods yield four optimal clusters in the review data. Frequently occurring words such as 'application,' 'goods,' and 'shopping' are visualized in a word cloud, and the clustering results are presented in a cluster plot. The obtained findings aim to enhance the service quality of the Blibli application.
Co-Authors Sulastri Sulastri