Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify

Ayu Sri Rahayu (Universitas Buana Perjuangan Karawang, Karawang)
Ahmad Fauzi (Universitas Buana Perjuangan Karawang, Karawang)
Rahmat Rahmat (Universitas Buana Perjuangan Karawang, Karawang)

Jurnal Sistem Komputer dan Informatika (JSON)
Vol 4, No 2 (2022): Desember 2022

The Spotify app is a subject of interest to social networking communities with significant disagreements or sentiments. Sentiment Analysis is a solution to automatically categorize opinions or ratings into negative or positive opinions. The techniques used in this research are Support Vector Machines (SVM) and Naïve Baye. The advantages of Naïve Bayes are simple, fast and high accuracy. SVM, on the other hand, can identify different hyperplanes that maximize the margin between two different classes. The classification results of this study have two category labels, namely negative and positive. The resulting accuracy value indicates the best test model for sentiment classification cases. Accuracy is measured by the confusion matrix and the results show that the accuracy value of the SVM algorithm is 84% while the accuracy value of the Naïve Bayes algorithm is higher than SVM which is 86.4%.