ANALISIS SENTIMEN APLIKASI PEMINJAMAN ONLINE BERDASARKAN ULASAN PADA PLAY STORE MENGGUNAKAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (STUDI KASUS : ADAKAMI DAN EASYCASH) - La Ode Muhammad Hafidz Abdillah Sam Mongkito; Natalis Ransi; La Surimi; Andi Tenriawaru; Gunawan Gunawan; Budi Wijaya Rauf (AnoaTIK: Jurnal Teknologi Informasi dan Komputer, 2024)
This research aims to analyze the sentiment of online lending applications based on reviews on the Google Play Store using the Naïve Bayes and Support Vector Machine methods and determine which online lending applications are more trustworthy. AdaKami is an online lending application under the auspices of PT Pemfinaan Digital Indonesia. EasyCash is an online lending application which is a financial technology company owned by PT. Indonesia Fintopia Technology which provides a digital financial service portal, especially online lending. However, to determine whether this online lending application is reliable or trustworthy, it requires a collection of information that comes from previous user experience. The Naïve Bayes and Support Vector Machine methods are used to analyze loan application sentiment based on relevant review data which is processed using the Python programming language with Google Colabs as a tool for carrying out the research stage. The research results show that the Naïve Bayes and Support Vector Machine methods can be applied in analyzing the sentiment of online lending applications and based on the results of application analysis using the Naïve Bayes Adakami method, it is more trusted by previous users because it produces 95% positive review data and the Easycash application produces positive review data of 95%. 93% and the results using the Adakami Support Vector Machine method produced positive review data of 91% and the Easycash application produced positive review data of 83%.review data while the Easycash application produces 93% positive review data.