ANALISIS SENTIMEN PADA ULASAN APLIKASI GOOGLE MAPS TERHADAP PELAYANAN BADAN PENYELENGGARA JAMINAN SOSIAL (BPJS) KESEHATAN SAMARINDA MENGGUNAKAN METODE K-NEAREST NEIGHBOR DENGAN FITUR EKSTRAKSI TF-IDF - Ikhsan, Ikhsan Nuttakwa Takbirata Ihram Nabawi; Rudiman, Rudiman; Fendy, Fendy Yulianto (Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, 2024)
This study aims to analyze public sentiment towards the services of BPJS Kesehatan Samarinda based on reviews on the Google Maps application. The method used in this research is K-Nearest Neighbor (KNN) with TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction. The data used consists of 500 Indonesian-language reviews collected through web scraping techniques. After the data collection process, the data was labeled by an expert, and then a pre-processing stage was carried out, including case folding, cleaning, tokenizing, stop word removal, and stemming. The data was then weighted using the TF-IDF method to identify important words. The testing was conducted using a training and testing data ratio of 70:30 and a k value of 5. The results showed that the KNN method was able to classify positive and negative sentiments with an accuracy rate of 93.3%. This analysis provides an overview of the service quality of BPJS Kesehatan in Samarinda and can be used as a basis for service improvements. Additionally, this research contributes to the use of KNN and TF-IDF for sentiment analysis, opening opportunities for further research in this field.