Sentiment; Clustering; K-Means Analysis Sentiment in Bukalapak Comments with K-Means Clustering Method

Authors

  • Rena Nainggolan Universitas Methodist Indonesia
  • Fenina Adline Twince Tobing
  • Eva J.G Harianja

DOI:

https://doi.org/10.31937/ijnmt.v9i2.2914

Abstract

Technological development are very fast ini this era of globalization, to facilitate the work of many aspevt that can be utilized, as well as for the flow of information. By applying computer technology in varios fields, such as educations, entertainment, healt, tourism, culinary and so on. Clustering id one of the Data Mining techniques. Clustering works by combining a number of data or objects into one cluster, with the aim that each data ini one cluster will have data that is a similar as possible and different from data or objects in other groups. K-Means Clustering has the ability to perform computations that are relatively fast and efficient in combining large amounts of data. In this research, there are 1407 comments which will training data and testing data.

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Published

2023-01-09

How to Cite

Nainggolan, R., Tobing, F. A. T., & Harianja, E. J. (2023). Sentiment; Clustering; K-Means Analysis Sentiment in Bukalapak Comments with K-Means Clustering Method. IJNMT (International Journal of New Media Technology), 9(2), 87–92. https://doi.org/10.31937/ijnmt.v9i2.2914

Issue

Vol. 9 No. 2 (2022): IJNMT : International Journal of New Media Technology)

Section

Articles

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