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All Journal Jurnal Nasional Teknologi Komputer Arcitech: Journal of Computer Science and Artificial Intelligence
Irsyad, Hafidz
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Computer Science & IT

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Analisis Sentimen di Youtube Terhadap Kenaikan UKT Menggunakan Metode Support Vector Machine Wahyuni, Nur Aisyah; Ayu, Dinda Putri; Irsyad, Hafidz
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 1 (2024): June 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i1.10829

Abstract

Students and the general public usually have different responses to the increase in Single Tuition Fees (UKT) at universities. Protests, complaints, and support for this increase may be expressed through various social media platforms, such as YouTube. Using the Support Vector Machine (SVM) method, this study analyzes comments on the YouTube platform related to the increase in UKT. Comment data is divided into three categories: positive, negative, and neutral. The evaluation results show that the SVM model achieves an accuracy of 0.88; it also demonstrates good ability to recognize negative sentiment with a precision of 0.83, recall of 0.90, and f1-score of 0.86. For neutral sentiment, the model shows a precision of 0.86, recall of 0.75, and f1-score of 0.80. Nevertheless, the SVM model achieves perfect scores for precision, recall, and f1-score of 1.00 for positive sentiment. Although the SVM model has proven effective in analyzing sentiment towards the increase in UKT on YouTube, further improvements are needed to enhance accuracy in identifying neutral sentiment.
Analisis Sentimen Opini Publik Terhadap Cyberbullying Pada Komentar Instagram Menggunakan Multinomial Naive Bayes Stephanie; Irsyad, Hafidz
Jurnal Nasional Teknologi Komputer Vol 4 No 4 (2024): Oktober 2024
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v4i4.157

Abstract

This study aims to evaluate the effectiveness of a sentiment classification model using the Naïve Bayes algorithm on Instagram comment data. The main focus of this research is to measure the performance of the model in terms of accuracy, precision, recall, and F1-score. The data used in this study includes 400 Instagram comments that have been labeled with negative and positive sentiments. Data pre-processing involved case folding, tokenization, stopword removal, and stemming, followed by TF-IDF weighting to measure the importance of each word. The data was divided into 80% for training and 20% for testing. The Naïve Bayes model was then applied to the test data to predict sentiment. The evaluation results show that the model achieved an accuracy of 86.25%, with a precision of 85.56%, recall of 86.46%, and F1-score of 85.88%. For the negative class, the precision reached 91%, recall 85%, and F1-score 88%, while for the positive class, the precision was 80%, recall 88%, and F1-score 84%. These findings show that the Naïve Bayes model is effective in classifying the sentiment of Instagram comments and provides useful insights in understanding public sentiment towards the issue of cyberbullying
Co-Authors Ayu, Dinda Putri Stephanie Wahyuni, Nur Aisyah