Support vector machine on two-class classification problem to determine an otaku - Husyen Ramadhan, Farhan; Lestari, Apri Dwi (Journal of Student Research Exploration, 2025)
Machine Learning has become a popular topic among academics and practitioners in recent years. This paper describes the use of SVM for otaku classification problem. The dataset used is a dummy dataset created with a python programme. In this research, SVM will be used as a model. The model aims to predict whether someone is an otaku or not, based on several attributes. The optimal parameters are obtained after several experiments. The parameters consist of kernel=‘poly’, C=0.1, gamma=‘auto’, degree=2, and attribute class_weight=None. The performance obtained by applying the above parameters is 100% accuracy.