Sentiment Analysis of User Reviews of Kitalulus Job Search App on Google Play Store Using Machine Learning - Astrid Ayuzi Putri Hendri Hariadi; Bunga Intan; Armanto (Bulletin of Information Technology (BIT), 2025)
This study seeks to assess the sentiment of user reviews for the "KitaLulus" job search app found on the Google Play Store, utilizing Machine Learning techniques. Given the intensifying competition within the job market, this application serves as a crucial resource for job seekers in Indonesia. The study employs a sentiment analysis method to categorize user reviews into three groups: positive, negative, and neutral. The dataset comprises 20,000 reviews in Indonesian gathered from the Google Play Store. The methodologies used in this study include K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression, and Naïve Bayes. The findings indicate that various algorithms demonstrate different levels of accuracy in sentiment classification. It is anticipated that the outcomes of this analysis will offer valuable insights to developers about the quality and effectiveness of the "KitaLulus" application, while also assisting users in making informed decisions prior to utilizing the app. Additionally, this research contributes to the domain of sentiment analysis, particularly concerning job search applications in Indonesia.