ANALISIS SENTIMEN LAYANAN OJEK ONLINE MAXIM DENGAN MENGGUNAKAN SUPPORT VECTOR MACHINE

Abstrak

The rapid technological advancements in Indonesia, particularly in the field of telecommunication infrastructure and widespread internet access, have brought about significant changes in the social fabric of the community. This transformation is notably observed in the transportation sector, where traditional motorcycle taxis have evolved into online services like Maxim in Batam City. This research aims to leverage the Support Vector Machine (SVM) algorithm for sentiment analysis on user reviews of Maxim's online motorcycle taxi service. Through data mining techniques, the SVM algorithm, implemented via RapidMiner, classifies reviews into positive and negative sentiments to enhance service quality and user experience. The evaluation of the SVM model reveals a satisfactory performance with 64.65% accuracy, 67.41% recall, 75.82% precision, 62.44% F1-score, and an AUC of 0.67.