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All Journal Semantik Jurnal Simetris Jurnal Informatika Seminar Nasional Informatika (SEMNASIF) Jurnal Informatika Upgris SISFOTENIKA JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Tekinkom (Teknik Informasi dan Komputer) Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Prosiding Konferensi Nasional PKM-CSR Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Computer and Technology
Hendrik Fery Herdiyatmoko
Universitas Katolik Musi Charitas
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Nursing Public Health Social Sciences Other

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Journal : Jurnal Informatika

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Youtube Comment Sentiment Classification System With Naive Bayes TF-IDF Using Laravel IDX normawati, dwi; Rendy Saputra; Hendrik Fery Herdiatmoko
Jurnal Informatika Vol. 19 No. 1 (2025): January 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v19i1.a30441

Abstract

The development of technology has enhanced social interactions through social media platforms like YouTube, making user comments a vital data source for sentiment analysis. One emerging issue is the lack of understanding regarding consumer perceptions of smartphone brands in Indonesia, which can be explored further through YouTube comments. This study aims to build a sentiment classification system for YouTube comments related to smartphone brands in Indonesia in 2024 using the Naïve Bayes Classifier algorithm with TF-IDF weighting and FastText features. Data was collected using the YouTube Data API, followed by preprocessing, labeling, and feature extraction stages. The model was optimized through GridSearchCV and evaluated with a Confusion Matrix, achieving an accuracy of up to 97%. The system was implemented as a Laravel-based web application, providing an interface for dataset management, model training, and sentiment visualization. This research also includes the integration of IDX Projects with Laravel, enabling more efficient data management and interactive presentation of sentiment analysis results. The findings demonstrate the effectiveness of this method in classifying positive and negative sentiments, which can help users understand consumer preferences for various smartphone brands.
Co-Authors Achmad Alfian Adha , M. Fathir Adi Prananda, Patrik Galih Alvaro, Hadi Anselmus Agung Pramudito Arini, Diana Putri Cevin, Jery Devi, Natalia Regina Diana Putri Arini Dwi Normawati, Dwi Fransiska Soejono Gultom, David Hadi Alvaro Hendri Syaputra Herdiyanto, H.Y. Ary Indriasari, Bernadine Ajeng J.M.Sri Narhadi M. Fathir Adha Meylinda Mulyati Muhamad Septian Natalia Regina Devi Orlando, Nikolas Patrik Galih Adi Prananda Pratama, Yohanes Dicka Putiastanti, Stevani Fatrisia Rendy Saputra riyanto, agustinus Saputra, Laurensius Sirwanti Septian , Muhamad Stevani Fatrisia Putiastanti Theresia Sunarni wawan nurmansyah Windriani, H.Y. Wayan Windriani, Wayan Yohanes Dika Pratama