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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Pranata, Ricky Albin
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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METODE PEMBOBOTAN TF-IDF UNTUK KLASIFIKASI TEKS QUICK COUNT PEMILIHAN WAKIL PRESIDEN INDONESIA 2024 PADA X TWITTER DENGAN METODE SVM Pranata, Ricky Albin; Rudiman, Rudiman; Azmi Verdikha, Naufal
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14934

Abstract

The 2024 Indonesian Vice Presidential Election Quick Count sparked diverse public reactions on X Twitter. The sheer volume and variety of expressed opinions complicate accurate sentiment identification and classification. This study aims to develop a text classification model using Support Vector Machine (SVM) to identify sentiment in election Quick Count-related tweets. Data was acquired through tweet collection, followed by pre-processing, word weighting using TF-IDF, and data splitting for model training and testing. Results indicated that the developed SVM model achieved 77.30% accuracy in tweet sentiment classification. The model's implementation is expected to aid in more effective information filtering and assist stakeholders in understanding public opinion more accurately.
Co-Authors Naufal Azmi Verdikha Rudiman, Rudiman