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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Felicia Marvela Evanita
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pengelompokan Toko E-commerce Shopee berdasarkan Reputasi Toko menggunakan Metode Clustering K-Medoids Felicia Marvela Evanita; Imam Cholissodin; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The growth of internet encouraged the creation of e-commerce or electronic commerce. E-commerce with the most visitors in Indonesia is Shopee with more than 72 million visitors each month at the end of 2019. Although e-commerce has a lot of good impact, users are still faced with the risks from using e-commerce. Users must be more careful in choosing a store to trust in order to avoid these risks. Users are faced with many choices while looking for products and users must consider which store should they choose. Store clustering on Shopee e-commerce based on store reputation with K-Medoids clustering could solve this problem. The data that used in this study were taken from 100 store in Shopee e-commerce by web scraping. Steps that taken were preprocessing the data, normalization, finding the distance for each data, clustering with K-Medoids, and evaluate using Silhouette Coefficient. In this study, the number of cluster and data were tested. From these tests, it was found that the best Silhouette Coefficient average was 0,317681 while using 2 clusters and 100 data.
Co-Authors Adinugroho, Sigit Imam Cholissodin