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All Journal Computatio : Journal of Computer Science and Information Systems JUTIK : Jurnal Teknologi Informasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas JUTIM (Jurnal Teknik Informatika Musirawas) Kurawal - Jurnal Teknologi, Informasi dan Industri Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Teknomatika (Jurnal Teknologi dan Informatika) Syntax: Journal of Software Engineering, Computer Science and Information Technology JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Ilmu Komputer dan Informatika Bulletin of Information Technology (BIT) Brilliance: Research of Artificial Intelligence Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Agrivet: Jurnal Ilmu-ilmu Pertanian dan Peternakan DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Insand Comtech : Information Science and Computer Technology Journal Buletin Ilmiah Informatika Teknologi JOINTECOMS (Journal of Information Technology and Computer Science) MDP Student Conference Software Development Digital Business Intelligence and Computer Engineering Journal Information & Computer (JICOM) Jurnal Software Engineering and Computational Intelligence Applied Information Technology and Computer Science (AICOMS) JISCOMP (Journal of Information System and Computer) Journal of Informatics and Computer Engineering Research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Hafiz Irsyad
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : JUTIK : Jurnal Teknologi Informasi dan Komputer

 1 
OPINI PUBLIK TERHADAP ISU KEASLIAN IJAZAH PADA PLATFORM YOUTUBE DENGAN NAÏVE BAYES, KNN, DAN SMOTE Agnes Anastasia Putri; Christian Bautista; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3892

Abstract

In the digital era, public opinion spreads massively and instantly through various social media platforms. One issue that has sparked widespread attention and debate in the digital space is regarding the authenticity of President Joko Widodo's diploma. This issue has provoked various reactions, both support, criticism, and neutral attitudes from netizens, especially through the comments column on YouTube. This study analyzes public sentiment towards the issue using a machine learning approach with the Naïve Bayes and K-Nearest Neighbors (KNN) algorithms, as well as the SMOTE data balancing technique. A total of 1,000 comments were analyzed and classified into three sentiment categories, namely positive, negative, and neutral. Four test scenarios were carried out, namely: KNN, KNN with SMOTE, Naïve Bayes, and Naïve Bayes with SMOTE with a performance comparison tested to see the effectiveness of each in classifying digital opinion. The test results showed that the combination of Naïve Bayes and SMOTE provided the best performance with accuracy, precision, recall, and F1-score of 73%. In contrast, the worst performing model is KNN with SMOTE, which only achieves 27% accuracy, 53% precision, 34% recall, and 15% F1-score. This study emphasizes the importance of algorithm selection and data handling strategies in digital opinion classification, and can be the basis for developing a reliable sentiment analysis system in the future.
OPINI MASYARAKAT TERHADAP BONUS DEMOGRAFI PADA KANAL YOUTUBE DENGAN METODE TF-IDF, NAÏVE BAYES DAN SMOTE Samuel Effendi Pratama; Jolyn Lucretia; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3902

Abstract

This study examines public opinion on the demographic bonus issue expressed through comments on YouTube channels using the TF-IDF, Naïve Bayes, and SMOTE methods. The data used consists of 870 comments that have been manually labeled into positive and negative sentiments. The research stages include data pre-processing in the form of case folding, removal of non-alphabetic characters, stopword removal, and stemming, then feature extraction using TF-IDF to convert text into numeric representations that can be processed by the algorithm. This study compares the performance of the Naïve Bayes sentiment classification model in two scenarios, namely without and with the application of SMOTE. The SMOTE technique is used to overcome data imbalance between sentiment classes so that the classification results are more balanced and unbiased. The evaluation results show that the model without SMOTE produces an accuracy of 70% but has a very low recall in the positive class. After applying SMOTE, the accuracy increased to 77%, with the highest precision of 0.89 in the negative class and the highest recall of 0.92 in the positive class. The word cloud visualization shows the dominant words that reflect the pattern of public opinion regarding the demographic bonus clearly and informatively. The results of this study can provide a quantitative picture of public perception and be a consideration for policy makers. In the future, this method can be further developed with other algorithms and data from various social media platforms to improve the accuracy and representativeness of sentiment analysis.
Co-Authors Abdul Rahman Adrian Suparto Agnes Anastasia Putri Ahmad Farisi Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Andreas Andreas Antony, Felix Arta Tri Narta Arta Tri Narta Aurelia, Reni Busdin, Rusdie Candra candra Chandra Wijaya Chandra, Kelvin William Christian Bautista Christy, Christy Cindy Meilani Daniel Wijaya Derry Alamsyah Devella, Siska dewa Dicko David K Dina Mariana Dwifa_Sophian, Muhammad Agus Edward Pratama Eka Puji Widiyanto Fareza, Ivan Farisi, Ahmad Farisi, Ahmad Fariz Prasetya Ferdi Jiranda Sinaga Fernando Sugianto Putra Franko, Billy Fujianto Graciela, Michelle Hansen, Hansen Hartati, Ery Hendra Nata Niko P Hidayat, Muhammad Syahrizal Ibnusina, Fedri Ivander Destian Luis Jeason Lie Jocelyn, Jennifer Jolyn Lucretia jonathan stanly Jonathan Wijaya Juliana Nasution Kamilah, Nyimas Nisrinaa Kelly, Angel Kevin Kevin Kevin kevin Kotan, Jendraja Husein Kurniawan, Calvin Laksana, Jovansa Putra Leonardo Leonardo Lestari, Yehezekiel Gian levid, Jonathan Felix Lin, Jimmi M Ezar Al Rivan Meiriyama, Meiriyama Michael Joy Clement Molavi Arman Muhammad Bemby Putra Mansyah Muhammad Rizky Pribadi Mutia, Silvi Narta, Arta Tri Novan Wijaya Novan Wijaya Novan Wijaya Novan Wijaya Pribadi, M Rizky Putra Darmansius, Albertus Dwi Andhika Renaldo, Florence Reynald Dwika Prameswara Rikky, Rikky Rizki Ambarwati RR. Ella Evrita Hestiandari Russel Wijaya Samuel Effendi pratama Santoti, Jennifer Velensia Sanu, Intan Saputra, M Reynaldi Shela, Shela Silfia Taqwiym, Akhsani Taqwiym, Akhsani Taqwiym, Akhsani Tinaliah, Tinaliah Triana Elizabeth, Triana Verrino Adityya Virginia, Callista Wati, Retiana Krisna Wati, Risha Ambar Wijang Widhiarso Wijaya, Christian Richie Willyanto, Aldo Wilyanto, Nicholas Wiwik Handayani Wong, Jeovanni Yohannes Yohannes Yunarto Yunarto, Yunarto