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All Journal Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Informatika Kaputama (JIK) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmiah Islamic Resources Jurnal Litbang Kota Pekalongan Simpatik: Jurnal sistem Informasi dan Informatika Academic Journal of Computer Science Research (AJCSR)
Eka Fitriani
Universitas Bina Sarana Informatika
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : Jurnal Informatika Kaputama (JIK)

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ANALISIS SENTIMEN REVIEW PADA APLIKASI MEDIA SOSIAL TIKTOK MENGGUNAKAN ALGORITMA K-NN DAN SVM BERBASIS PSO Dian Ardiansyah; Atang Saepudin; Riska Aryanti; Eka Fitriani; Royadi
Jurnal Informatika Kaputama (JIK) Vol 7 No 2 (2023): Volume 7, Nomor 2, Juli 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v7i2.148

Abstract

Review sentiment analysis on social media applications is one of the methods used to analyze opinions and feelings (sentiment) of social media users towards a particular product, service or topic. Tiktok social media users are the second most in the world. The Tiktok app is the leading social media platform and the ultimate destination for short-form videos. Music, dance, education, beauty, passion, or talent show. This research uses data from Tiktok application reviews based on positive and negative sentiments to compare the K-Nearest Neighbor (K-NN) and Particle Swarm Optimization (PSO)-based Support Vector Machine (SVM) algorithms. To test the results of the PSO-based K-NN and SVM algorithms using the Cross Validation method from the test results that the PSO optimization SVM algorithm has the best accuracy compared to the KNN algorithm. Where the accuracy value of SVM is 86.40% and AUC is 0.908. The PSO optimization SVM has an accuracy of 88.20% and an AUC of 0.91. While the K-NN algorithm has an accuracy of 83.40% and an AUC of 0.903 then the accuracy value of the K-NN optimization PSO gets an accuracy of 69.20% and an AUC of 0.77. This means that the use of the PSO optimization SVM algorithm has the highest level of accuracy.
Co-Authors Abdussomad Abdussomad Ardiana Priharwanti Atang Saepudin Baiti, Nurul Dahlia Dahlia Dede Firmansyah Saefudin Dede Saefudin Dian Ardiansyah Dila Delia Fadilah Duwi Cahya Putri Buani Hakim, Ahmad Ilham Kurniawan Muhammad Faittullah Akbar Negsi, Ratika Richardus Eko Indrajit Rifky Permana Riska Aryanti Rizal Amegia Saputra Royadi - SALMAN ALFARIZI Widya Apriliah