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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Dinamisia: Jurnal Pengabdian Kepada Masyarakat JUTIM (Jurnal Teknik Informatika Musirawas) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CSRID (Computer Science Research and Its Development Journal) IT (INFORMATIC TECHNIQUE) JOURNAL Information System Journal (INFOS) Journal of Applied Computer Science and Technology (JACOST) The Indonesian Journal of Computer Science Research JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) SWAGATI: Journal of Community Service
Anggita, Sharazita Dyah
Universitas AMIKOM Yogyakarta
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Social Sciences Other

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Journal : Journal of Applied Computer Science and Technology (JACOST)

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Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen Sharazita Dyah Anggita; Ferian Fauzi Abdulloh
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.524

Abstract

Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis.
Co-Authors -, Atik Nurmasani Abdulloh, Ferian Fauzi Acihmah Sidauruk Al Azfar, Naufal Anshor Azfar, Naufal Anshor Al Baita, Anna Dwi Hartanto , Anggit Eli Pujastuti Fahlevie, Reychandra Tristan Fauzi, Ahmad Firmansyah, Rohmatullah Batik Gita Cahyani Ika Asti Astuti Ikmah Ikmah Ikmah Ikmah Ikmah Nuri Cahyono Sangaji, Sangaji Wiwi Widayani Wulandari, Irma Rofni Yoga Pristyanto