Model Analisis Sentimen pada Ulasan Pengguna Mobil Banking Menggunakan Kombinasi K-Means dan Naive Bayes
Submitted : 2024-08-16, Published : 2025-05-08.
Abstract
Application Mobile Banking Sumsel Babel (BSB) faces the problem of low user satisfaction with a System Usability Scale (SUS) score of 31.46, which is included in the "Not Acceptable" category. The main purpose of this study is so that South Sumatra Babel can make improvements to the BSB application. The methods used include K-Means for clustering review data and Naïve Bayes for classifying user review sentiment. The study found four clusters of user reviews, of which sentiment in cluster 0 and cluster 3 were classified as negative, while cluster 1 and cluster 2 were classified as positive. Negative clusters indicate problems in access, account activation, failed transactions, and frequent application errors. In conclusion, users of the BSB application experienced various difficulties and frustrations related to the stability and reliability of the application, indicating an urgent need for improvement, especially in cluster 0 and cluster 3.
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