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All Journal TEKNIK INFORMATIKA
Nurhayati Buslim
(SCOPUS ID : 55516191400, h-index: 4) Universitas Islam Negeri Syarif Hidayatullah
Computer Science & IT

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Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis. Nurhayati Buslim; Lee Kyung Oh; Muhammad Hugo Athallah Hardy; Yusuf Wijaya
JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.29302

Abstract

Text classification is a process of categorizing a text into the correct label. Text classification in natural language processing is a challenging task that requires accuracy to get the correct results, manual text classification tends to be inefficient because it requires a lot of time and also experts. The utilization of machine learning for automatic text classification can be a solution to this problem. KNN, Naive Bayes, and SVM are known as some of the most algorithms to solve classification problems, especially text classification. In this study, we are trying to compare the KNN, Naive Bayes, and SVM algorithms for text classification with the problem of classifying movie genres based on a synopsis using datasets obtained from Kaggle.com and IMDB Dataset. The results of this study indicate that of the 12 experiments, Support Vector Machine (SVM) is the bestperforming algorithm with an accuracy of 90%, 93%, 65%, and 63%. It is hoped that this research can help to determine the best algorithm in the text classification process. 
Co-Authors Lee Kyung Oh Muhammad Hugo Athallah Hardy Yusuf Wijaya