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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi
Eunice Evelyn Pramesty Utomo
Politeknik Negeri Samarinda
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Analisis Sentimen Warga X (Twitter) Terhadap Ibu Kota Nusantara Indonesia Menggunakan Metode Support Vector Machine Eunice Evelyn Pramesty Utomo; Farindika Metandi; Subhan Hartanto
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9235

Abstract

Abstrak - Pemindahan Ibu Kota Negara (IKN) ke Kalimantan Timur memunculkan berbagai opini publik, terutama di media sosial X (Twitter). Namun, belum ada analisis yang mendalam terkait sentimen publik terhadap proyek ini, yang berpotensi memengaruhi persepsi dan keputusan strategis pemerintah. Penelitian ini bertujuan untuk mengklasifikasikan sentiment (positif, negatif, netral) pada tweet-tweet terkait IKN menggunakan metode Support Vector Machine (SVM). Data yang digunakan sebanyak 2.500 tweet yang diperoleh melalui proses crawling dan diolah melalui tahapan preprocessing. Model SVM dilatih menggunakan dataset yang telah dilabeli dan diuji performanya dengan metrik akurasi, precision, recall, dan f1-score. Hasil penelitian menunjukkan model mampu mengklasifikasikan sentiment dengan akurasi 88% dengan performa terbaik pada kelas negatif dan netral. Hal baru dari penelitian ini adalah pendekatan komprehensif dalam menganalisis persepsi publik terhadap IKN melalui media sosial dengan metode SVM, yang dapat menjadi acuan dalam pengambilan kebijakan berbasis data. Kata Kunci: Analisis Sentimen; IKN; Support Vector Machine; Media Sosial; X (Twitter); Abstract — The relocation of Indonesia's capital city (IKN) to East Kalimantan has sparked various public opinions, particularly on the social media platform X (Twitter). However, there has been no in-depth analysis of public sentiment toward this project, which could influence public perception and strategic government decisions. This study aims to classify sentiments (positive, negative, neutral) in tweets related to IKN using the Support Vector Machine (SVM) method. The dataset consists of 2,500 tweets collected through a crawling process and processed through preprocessing stages. The SVM model was trained using a labeled dataset and its performance was evaluated using accuracy, precision, recall, and f1-score metrics. The results indicate that the model can classify sentiments with an accuracy of 88%, showing the best performance in the negative and neutral classes. The novelty of this research lies in its comprehensive approach to analyzing public perceptions of IKN through social media using the SVM method, which can serve as a reference for data-driven policymaking.Keywords: Sentiment Analysis; IKN; Support Vector Machine; Social Media; X (Twitter);
Co-Authors Farindika Metandi Subhan Hartanto