Analisis Kepuasan Pengguna Aplikasi Neobank Melalui Analisis Sentimen Dan Klasifikasi Multilabel Dengan Logistic Regression Pada Kerangka Eucs

Abstract

The rapid development of digital banking services in Indonesia has led to an increased adoption of mobile banking applications as the primary medium for financial transactions. Neobank, as one of the mobile banking applications with a high number of downloads, exhibits a discrepancy between usage intensity and user satisfaction, as reflected in its ratings and user reviews. Various user reviews indicate issues related to technical constraints, system accuracy, ease of use, and service timeliness. This condition highlights the importance of conducting a comprehensive evaluation of user satisfaction. This study aims to analyze user satisfaction with the Neobank application through the integration of sentiment analysis and multilabel classification based on the End-User Computing Satisfaction (EUCS) framework. The dataset consists of 10,392 user reviews collected from the Google Play Store and App Store. The research stages include text preprocessing, sentiment labeling using a pre-trained Indonesian RoBERTa Base Sentiment Classifier, and EUCS dimension labeling using a fuzzy string matching approach based on keywords. Subsequently, a multilabel classification model was developed using Logistic Regression with a One-vs-Rest (OVR) approach and TF-IDF features.The evaluation results on the test data demonstrate a precision of 0.940, a recall of 0.809, and an F1-score of 0.869 (macro average). The k-fold cross-validation results (k=5) indicate stable model performance, with the highest average F1-score achieved in the Ease of Use dimension (0.927). The model was then implemented into a web-based system to perform sentiment prediction, EUCS multilabel classification, and generate improvement recommendations using a Large Language Model (LLM).