M. Khairul Anam, Tri Putri Lestari, Lusiana Efrizoni, Nadya Satya Handayani, Imam Andhika
This study targets improved sentiment classification by combining the strengths of multiple SVM kernels within an ensemble framework. We introduce SVM Porlis, which fuses Linear, RBF, Polynomial, and Sigmoid kernels using both hard and soft voting to boost performance on skewed data. The task is binary sentiment recognition (positive vs. negative). A corpus of 2,248 tweets concerning the debate over the naturalization of Indonesia’s national football players was gathered via the official X/Twitter API, with a marked dominance of negative tweets. The preprocessing pipeline encompassed cleaning, labeling, tokenization, stopword removal, stemming, and TF-IDF feature extraction. To counter the imbalance, SMOTE was applied to synthesize additional minority-class samples. Each kernel was first trained and assessed independently, then aggregated into the SVM Porlis ensemble. Evaluation used accuracy, precision, recall, F1-score, and confusion-matrix analysis. The softvoting SVM Porlis model achieved the best results—98% for accuracy, precision, recall, and F1—outperforming single-kernel baselines and other ensembles such as SVM + Chi-Square and SVM + PSO. These outcomes indicate that integrating diverse kernels effectively captures both linear and nonlinear patterns, yielding a robust and adaptive approach for sentiment analysis on real-world, imbalanced datasets. © 2025, Ikatan Ahli Informatika Indonesia. All rights reserved.
Department of Informatics, Faculty of Science and Technology, Universitas Samudra, Langsa, Indonesia; Department of Business Digital, Faculty of Education and Social Sciences, Universitas Indraprasta PGRI, Jakarta, Indonesia; Department of Informatics Engineering, Universitas Sains dan Teknologi Indonesia, Pekanbaru, Indonesia; Department of Informatics Engineering, Politeknik Negeri Batam, Batam, Indonesia; Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
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