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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics JUITA : Jurnal Informatika Bulletin of Electrical Engineering and Informatics Journal of Information Technology and Computer Science (JOINTECS) JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Informatika Universitas Pamulang JISKa (Jurnal Informatika Sunan Kalijaga)
Imam Riadi
Ahmad Dahlan University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis Abdul Fadlil; Imam Riadi; Fiki Andrianto
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.19798

Abstract

Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment.
Co-Authors Abdul Fadlil Ahmad Luthfie Anton Yudhana Fiki Andrianto Herman Herman Muh. Hajar Akbar Nasirudin Nasirudin Nur Hamida Siregar Nur Widiyasono Sahiruddin Sahiruddin Sukma Aji Sunardi, Sunardi W, Yunanri