Peningkatan Kinerja SVM pada Klasifikasi Sentimen Ulasan iPusnas Berbasis Imbalance Handling - Akmal Mustafa; Rudi Kurniawan; Bani Nurhakim; Puji Pramudya Marta; Khaerul Anam (Universitas Harapan Medan, 2026)
This study investigates sentiment classification of user reviews on the iPusnas digital library application to provide an objective overview of service quality and user experience. Numerous complaints related to login failures, application crashes, and issues in accessing digital books indicate the need for a computational approach capable of processing large volumes of user feedback. The proposed method integrates Natural Language Processing (NLP) techniques with the Support Vector Machine (SVM) algorithm. The workflow consists of collecting 2,000 reviews, applying text cleaning and normalization, tokenization, stopword removal, stemming, rating-based sentiment annotation, and feature extraction using TF-IDF. The dataset was divided using a train–test split for model training and evaluation. Experimental results show that the SVM model achieves 90.1% accuracy, demonstrating strong performance in detecting negative sentiments and moderate performance for positive sentiments due to class imbalance. These findings highlight the effectiveness of NLP and SVM for extracting user perceptions and indicate the potential of this model as a decision-support tool for improving iPusnas application services. Overall, the study contributes to the advancement of digital service innovation in Indonesia.