The Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering
Keywords:Data Mining, K-Means, Clustering, Cluster, Online Customer Reviews
Technological developments have made changes in people's lifestyles, namely changes in the behavior of people who had shopped directly or offline to online. Many benefits are obtained from shopping online, namely the many conveniences offered by shopping online, besides that there are also many disadvantages of shopping online, namely the many risks in using e-commerce facilities, namely the problem of product or service quality, safety in payments, fraud. This research aims to mine review data on one of the e-commerce sites which ultimately produces clusters using the K-Means Clustering algorithm that can help potential customers to make a decision before deciding to buy a product or service
I. U. Yoviriska, and Wahjoedi. “Trend keputusan Belanja Online Mahasiswa Fakultas Ekonomi UM Angkatan 2014”, Jurnal Pendidikan Ekonomi, vol.11, no. 1, Mar. 2018.
S. Sidharta, and B. Suzanto, “Pengaruh kepuasan transaksi online shopping dan kepercayaan konsumen terhadap sikap serta perilaku konsumen pada e-commerce”, Jurnal computech & Bisnis, vol. 9, pp. 23-26, no.1, Jun. 2018.
V. Carlo. Business Intelligence: Data Mining and Optimization for Decision Making, West Sussex, United Kingdom: John Wiley & Sons Ltd, 2009.
R. Nainggolan and E. Purba, “The Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering”, Data Science: J. of Computing and Appl. Informatics, vol. 4, no. 2, Jul. 2020.
Y. Ganjisaffar. (2013). Open Source Web Crawler for Java. [Online]. Accessed: 13 May 2017. Available: http://code.google.com/p/crawler4j/.
L. Kumar, and P. K. Bhatia, “Text Mining: Concepts, Process and Applications”, Journal of Global Research in Computer Science, vol. 4, pp. 36-39, 2013.
T. Mardiana , T. B. Adji, , and I. Hidayah, “Stemming Influence on Similarity Detection of Abstract Written in Indonesia”, TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 14, no. 1, pp. 219-227, 2016.
F.Z. Tala, “A Study of stemming effects on information retrieval in Bahasa Indonesia”, Master of Logic Project, Institute for Logic, Language and Computation, Univ. van Amsterdam, Netherlands, 2013.
M. Adriani, J. Asian, B. A. A. Nazief, S. M. M. Tahaghoghi, and H. E. Williams, “Stemming Indonesian: A confix-stripping approach”, ACM Transactions on Asian Language Information Processing, vol. 6, no. 4, pp. 1-33, 2007.
How to Cite
Copyright (c) 2020 Data Science: Journal of Computing and Applied Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Data Science: Journal of Informatics Technology and Computer Science (JoCAI) and Faculty of Computer Science and Information Technology as well as TALENTA Publisher Universitas Sumatera Utara as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, will be allowed only with a written permission fromData Science: Journal of Informatics Technology and Computer Science (JoCAI).