Perbandingan Metode Naive Bayes Classifier dan Support Vector Machine pada Analisis Sentimen Twitter Topik Lifestyle

Fadlila Nurwanda; Jelita Ratu Rizkiani

doi:10.5281/zenodo.10077023

Fadlila Nurwanda Universitas Sebelas Maret Surakarta
Jelita Ratu Rizkiani Universitas Sebelas Maret Surakarta

DOI: https://doi.org/10.5281/zenodo.10077023

Keywords: twitter, lifestyle, indonesia, naive bayes calssifer, support vector machine

Abstract

Technology is currently developing rapidly thanks to the widespread growth of the internet worldwide. This growth has triggered an increasing demand for diverse information, especially in textual form. One way to fulfill this information demand is through social media platforms, which enable communication and interaction among individuals. Twitter has become a popular social media platform in Indonesia, providing a space for people to express their opinions on various topics, including lifestyle. These opinions can range from positive to negative or even neutral. Sentiment analysis is needed to provide a general overview of the sentiment expressed by the Indonesian public regarding lifestyle topics. This research utilizes the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) classification methods to compare which method is most effective in analyzing sentiment towards lifestyle topics in Indonesian society. The study found that the SVM method achieved the highest accuracy of 61% and produced consistent prediction results.

References

Fhutuh, I. Analisis sentimen K-popers terhadap dunia hiburan tanah air dengan algoritma Convolutional Neural Network (CNN). Bandung: Doctoral dissertation, Universitas Islam Negri Sunan Gunung Djati, 2022.

Naufal, M. F., Arifin, T., & Wirjawan, H. Analisis Perbandingan Tingkat Performa Algoritma SVM, Random Forest, dan NaiveBayes untuk Klasifikasi Cyberbullying pada Media Sosial. Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 8(1), 82-90, 2023.

Amelia, R., Darmansah, D., Prastiwi, N. S., & Purbaya, M. E. Impementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Masyarakat Indonesia Mengenai Drama Korea Pada Twitter. JURIKOM (Jurnal Riset Komputer), 9(2), 338-343, 2022.

Larasati, M. A. Z., Winarsih, N. A. S., Rohman, M. S., & Saraswati, G. W. Penerapan Metode K-Means Clustering Dalam Menganalisis Sentimen Masyarakat Terhadap K-Popers Pada Twitter. Progresif: Jurnal Ilmiah Komputer, 18(2), 201-210, 2022.

Setiyawati, D., & Cahyono, N. Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia. Indonesian Journal of Computer Science, 12(1), 2023.

Fitri, E. Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine. Jurnal Transformatika, 18(1), 71-80, 2020.

Fitriana, F., Utami, E., & Al Fatta, H. Analisis Sentimen Opini Terhadap Vaksin Covid-19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes. Jurnal Komtika (Komputasi Dan Informatika), 5(1), 19-25, 2021.

Tuhuteru, H., & Iriani, A. Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier. Jurnal Informatika, 3(03), 2018.