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Untoro, Meida Cahyo
Teknik Informatika, Institut Teknologi Sumatera
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Journal : Scientific Journal of Informatics

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Sentiment Analysis of Public Opinion on BAWASLU Using Random Forest and Particle Swarm Optimization Untoro, Meida Cahyo; Farhan, Muhammad
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22234

Abstract

Purpose: Sentiment analysis, commonly referred to as opinion mining, involves the study of people's opinions, emotions, and attitudes toward various subjects. While the Random Forest algorithm is frequently employed in sentiment classification tasks, its integration with Particle Swarm Optimization (PSO) for feature selection remains relatively underexplored. This study investigates whether PSO-based feature selection can enhance the predictive performance of Random Forest by optimizing the selection of relevant textual features, ultimately leading to more accurate sentiment classification. Methods: The research adopts a structured text preprocessing approach that includes data cleansing, case folding, normalization, stop-word removal, and stemming to refine the input text. Term Frequency-Inverse Document Frequency (TF-IDF) is applied to extract features, followed by PSO-driven feature selection to refine the input set for the Random Forest classifier. The proposed model is evaluated using a Twitter sentiment dataset related to “Bawaslu”, with performance measured based on Out-of-Bag (OOB) error and accuracy metrics. Result: Empirical results demonstrate that incorporating PSO-based feature selection into the Random Forest model substantially lowers the OOB error to 20.42%, compared to 28.72% in the baseline Random Forest model. Furthermore, the optimized model achieves an accuracy of 78.35%, outperforming the standard approach. However, the introduction of PSO-based feature selection increases computational demands, indicating a trade-off between classification accuracy and processing efficiency. Novelty: This study introduces the novel integration of PSO-driven feature selection with Random Forest classification for sentiment analysis, addressing challenges in imbalanced text data. By optimizing feature selection through a metaheuristic approach, it enhances model accuracy and efficiency. The novelty lies in applying PSO to refine feature selection in text classification, offering new insights into improving machine learning models for imbalanced datasets. Future research could explore reducing computational overhead and investigating hybrid selection techniques to further enhance scalability and performance.
Co-Authors Afriansyah, Aidil Ahmad Agung Zefi Syahputra Aidil Afriasnyah Algifari, Muhammad Habib Amrulloh, Iqbal Anastasia Puteri Dewi Andika Setiawan Andika Setiawan, Andika Andini, Maria Anggraini, Leslie Annisa Dwi Atika Anugerah Perdana Aprilia Purwanto Aprilianda, Mohamad Meazza Arre Pangestu Athalla, Muhammad Nadhif Bagaskara, Radhinka Bangun, Natasya Ate Malem Ba’its, Alfian Kafilah Buliali, Joko Lianto Dani Al Mahkya Desi Budiarti Dharmawan, Benedictus Budhi Dian Anggraini Drantantiyas, Nike Dwi Grevika Eka Nur'azmi Yunira Eko Dwi Nugroho Eri Yuni Nilasari Faisal, Amir Faza Nur Fuadina Febrianto, Andre Feri Fahrianto Fery Widhiatmoko Fitrawan, Mhd. Kadar Gunawan, Rayhan Fatih Harmiansyah Hidayah, Fathan Rizki Ibn, Ferreyla Setara Ilham Firman Ashari Irawati, Febri Dwi Jerhi Wahyu Fernanda Kesuma, Alvin Kurniawansyah, Apri Laisya, Nashwa Putri Leo Viranda Millennium Leonard Rizta Listiani, Amalia M. Syamsuddin Wisnubroto Mahdia Nisrina Maharani M Mandiri, Tobyanto Putra Marbun, Rustian Afencius Maria Oktarise Natania Gultom Mastuti Widianingsih, Mastuti Muhammad Adam Aslamsyah Muhammad Affandi Muhammad Alfarizi Tazkia Muhammad Farhan Muhammad Muttaqin Muhammad Nadhif Athalla Muhammad Yusuf Muhammad Zulfarhan Najie, Muhammad Nasrulloh, M. Anas Nazla Andintya Wijaya Nestiawan Ferdiyanto Nur'azmi, Eka Nurul Fajrin Ariyani Oktaviana Rinda Sari Perdana, Agung Mahadi Putra Prabandari, Pungki Resti Praramadhana, Daffa Praseptiawan, Mugi Pungki Resti Prabandari Raidah Hanifah Raidah Hanifah Retnosari, Hesti Revangga, Dwi Arthur Riyanarto Sarno Samsu Bahri Sianturi, Elsa Elisa Yohana Sidabutar, Ribka Julyasih Sinaga, Nydia Renli Siregar, Abu Bakar Siddiq Sofian, Ahmad Alif Sophia Nouriska Suranta, Akmal Fauzan Tirta Setiawan Verdiana, Miranti Winda Yulita Wisnubroto, M. Syamsuddin Yulita, Winda Yunira, Eka Nur'azmi Yusuf, Muhammad Asyroful Nur Maulana