ANALISIS SENTIMEN PERKEMBANGAN MOTOR LISTRIK MENGGUNAKAN SUPPORT VECTOR MACHINE DAN OPTIMASI PARTICLE SWARM OPTIMIZATION - Ginabila, Ginabila; Fauzi, Ahmad; Pratiwi, Risca Lusiana; Fauziah, Siti; Alfianti, Zulia Imami (INTI Nusa Mandiri, 2024)
Innovation in electric motor technology such as increased range, speed, and battery endurance can attract interest from individuals fascinated by the latest advancements. Sentiment analysis enables a profound understanding of consumer perceptions towards electric motors. In this study, Support Vector Machine (SVM) is employed as a classification tool to evaluate opinions on current developments in electric motors. SVM seeks an optimal hyperplane that maximizes the distance between sentiment categories. The development of sentiment analysis methods utilizes SVM with Particle Swarm Optimization (PSO) to successfully achieve an accuracy of 80.33% and obtain a Good Classification category based on ROC Curve results. This research provides insights into consumer perceptions of electric motor technology, offering valuable feedback for manufacturers in the development of superior electric motor products. Leveraging sentiment analysis, manufacturers can enhance product improvements, increase quality, and expand functionality to meet the evolving market demands.