PENERAPAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) UNTUK ANALISIS SENTIMEN TERHADAP DATA ULASAN APLIKASI E-COMMERCE LAZADA PADA GOOGLE PLAYSTORE

Rais, Zulkifli
Muhammad Kasim Aidid
Asti Dewi Putri

VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Vol. 7 No. 2 (2025)

Classification is the process of grouping objects based on their characteristics. Various classification methods have been employed, ranging from manual grouping to using technology as an aid in the process. One commonly used classification method is the K-Nearest Neighbor (K-NN) algorithm. K-NN predicts the class of data based on the majority class of its nearest neighbors. The novelty of this research lies in using the K-NN method on the case of Lazada application user sentiment on the Google Playstore. In this study, the review classification used is positive and negative labels. Additionally, three accuracy comparisons between training and testing data were used: 80% : 20%, 70% : 30%, and 60% : 40%. Based on the research results from the classification process of Lazada application user reviews on the Google Playstore, an accuracy of 87.00% was obtained for the training and testing data comparison of 80% : 20%.