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All Journal Jurnal INSYPRO (Information System and Processing) Journal Software, Hardware and Information Technology Agents: Journal of Artificial Intelligence and Data Science
Muhammad Nur Akbar
Universitas Islam Negeri Alauddin Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Agents: Journal of Artificial Intelligence and Data Science

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Analisis Sentimen Komentar Pengguna Aplikasi Threads Pada Google Playstore Menggunakan Algoritma Multinominal Naive Bayes Classfier Muhammad Nur Akbar; Nirwana Samrin
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 2 (2023): Maret - Agustus
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v3i2.67

Abstract

The advancement of technology, especially internet and social media technology, has enabled people across the country to connect and interact with each other. In this context, Instagram has become a popular platform for content creators to share their work. In an effort to compete with other platforms, Instagram launched an integrated app called Threads, which shares some features similar to Twitter. Threads allows users to share text-based posts and provides various other features. To enhance the quality of this application, developers need to review user comments. However, the influx of comments is substantial, making manual review inefficient. Therefore, an automated application is required to categorize comments and analyze user sentiment. By utilizing text mining techniques for sentiment analysis, developers can easily sort comments into positive and negative categories. Multinomial Naive Bayes was chosen as it's specifically designed for data with frequency occurrences, such as in text analysis. It is expected that this application can assist developers in improving the quality of the generated app. From the results of this study, an accuracy of 76% was achieved, which is  relatively good and offers potential for further development to attain better results.
Co-Authors Darmatasia Darmatasia Mubarak Mubarak Muh Syahwal Mustikasari Mustikasari Nirwana Samrin Nur Annisa Safitri Yusuf Nur Salman Yoga Yuniadi Yulia Ardana