SENTIMENT ANALYSIS OF THE SAMBARA APPLICATION USING THE SUPPORT VECTOR MACHINE ALGORITHM - Firdaus, Thoriq Janati; Indra, Jamaludin; Lestari, Santi Arum Puspita; Hikmayanti, Hanny (Jurnal Teknik Informatika (Jutif), 2024)
Rapid technological developments have opened up new opportunities for public services by utilizing digital application innovations. One example is the West Java Samsat Mobile (SAMBARA) designed by the West Java Provincial Revenue Agency (BAPENDA). The SAMBARA application is expected to accelerate annual vehicle tax payment obligations, but several reviews on the Playstore show user dissatisfaction with SAMBARA's performance. This study aims to conduct a sentiment analysis of SAMBARA application reviews using the Support Vector Machine algorithm. SAMBARA user review data on Google Playstore was collected using the python programming language google play scraper library on google colabolatory resulting in 1620 data on January 2, 2024. The data pre-processing stage involves various steps such as data cleaning, lowercase conversion, tokenization, stemming, stop words removal, normalization, and the use of the TF-IDF method. The data is then labeled positive and negative, positive for reviews with scores of 4 and 5 and negative labels for reviews with scores of 1 to 3. The Support Vector Machine (SVM) algorithm is used for classification, a well-known method for accurate classification. Model evaluation was conducted using a confusion matrix to calculate the precision, recall, and F1-Score values. The evaluation results provide an overview of the performance of the classification algorithm in grouping user reviews into positive and negative categories. The evaluation results show that the SVM algorithm provides quite good performance with an accuracy value of 88.75%, precision 87.51%, recall 81.25%, and F1-Score 83.71% which can be the basis for improving the quality of service of the SAMBARA application. Because the Sambara application has a negative sentiment of 73.4%, it can be concluded that it still gets a bad rating in terms of use.