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All Journal JOIV : International Journal on Informatics Visualization Jurnal Komunikasi Dialogis
Hairul Umam, Akhmad
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Computer Science & IT Languange, Linguistic, Communication & Media Social Sciences

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Comparison of Salp Swarm Algorithm and Particle Swarm Optimization as Feature Selection Techniques for Recession Sentiment Analysis in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Irmawati, Irmawati; Ekachandra, Kristian; Suhali, Jason; Hairul Umam, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3102

Abstract

Amidst global economic uncertainty, this study focuses on Twitter sentiment during the global recession issue on social media, especially in Indonesia. By utilizing sentiment analysis, this study uses machine learning algorithms such as Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) which are still less than optimal on high-dimensional Twitter data. The purpose of this study is to improve the accuracy of conventional machine learning using basic metaheuristic algorithms, namely the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) as feature selection. From January to May 2023, this study captures the evolving sentiment in response to economic conditions. Data preprocessing, including labeling through the TextBlob and VADER libraries, sets the stage for the analysis. Performance is compared based on labeling techniques, feature selection, and classification algorithms. Specifically, when applied to VADER labeled data without feature selection, the SVM model achieves an outstanding accuracy of 83% and an F1 score of 67%—notably, the application of SSA and PSO results in a reduction in model accuracy by 1%. However, the application of SSA and PSO slightly reduced the model accuracy performance by 1%. On the TextBlob labeled data, SVM showed an outstanding performance (80% accuracy, 77% F1 score). Interestingly, PSO on TextBlob data with SVM significantly decreased the model's performance. These findings contribute significantly to understanding the intricacies of sentiment dynamics during economic uncertainty on social media platforms, with SVM emerging as a strong choice for practical sentiment analysis.
Praktik Translingual dalam Komunikasi Politik Prabowo: Identitas dan Ideologi dalam Diskursus Pemerintahan Hairul Umam, Akhmad; Aryani Harijono, Kartika
Jurnal Komunikasi Dialogis Vol. 1 No. 1 (2025): Jurnal Komunikasi Dialogis
Publisher : Ilmu Komunikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/jkd.v1i1.954

Abstract

Fenomena translingual dalam komunikasi politik Indonesia semakin menonjol, terutama sejak terpilihnya Prabowo Subianto sebagai Presiden Republik Indonesia pada tahun 2024. Penelitian ini menjadi penting karena memotret bagaimana praktik pencampuran bahasa—Indonesia, Inggris, Arab, dan bahasa lokal—digunakan sebagai strategi komunikasi yang tidak hanya menyampaikan pesan, tetapi juga merepresentasikan identitas politik dan ideologi pemerintahan. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi kasus. Data dikumpulkan dari pidato resmi, unggahan media sosial, dan kutipan berita daring dari Desember 2024 hingga Agustus 2025. Analisis dilakukan melalui kerangka Critical Discourse Analysis (CDA) dan teori translanguaging. Hasil menunjukkan bahwa translingual practices dalam pidato Prabowo secara strategis membentuk identitas politik yang multivokal—nasionalis, religius, dan global.
Co-Authors Aryani Harijono, Kartika Dinar Ajeng Kristiyanti, Dinar Ajeng Ekachandra, Kristian Irmawati Irmawati Sanjaya, Samuel Ady Suhali, Jason