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All Journal Arcitech: Journal of Computer Science and Artificial Intelligence Applied Information Technology and Computer Science (AICOMS)
Muhamad Rizvi Roshan
Universitas Multi Data Palembang
Computer Science & IT

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Opini Publik terhadap Isu Pengoplosan Pertamax di Youtube Menggunakan Metode Naive Bayes Adikara Alif Nurrahman; Earlando Moza; Ramanda Md; Muhamad Rizvi Roshan; Ahmad Rizky; Hafiz Irsyad
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1990

Abstract

This study aims to explore public perceptions regarding the issue of Pertamax fuel adulteration, a topic that has sparked widespread discussion on YouTube, by employing sentiment analysis techniques based on the Naive Bayes algorithm. This issue has attracted significant public attention and become a trending topic on social media, particularly on the YouTube platform. The data analyzed in this research consist of user comments responding to the issue. The Naive Bayes algorithm is used to classify sentiments in the comments into three categories: positive, negative, and neutral. To address the imbalanced distribution of data, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. The results show that before applying SMOTE, the model achieved an accuracy of only 48%, with a precision of 0.48, recall of 0.36, and an F1-score of 0.41 for the negative category, as well as a precision of 0.48, recall of 0.56, and an F1-score of 0.52 for the positive category. After implementing SMOTE, the model's accuracy increased significantly to 88%, with a precision of 0.91, recall of 0.93, and an F1-score of 0.92 for the negative category. For the positive category, precision improved to 0.80, although recall decreased to 0.75, yielding an F1-score of 0.77. The average precision, recall, and F1-score (macro average) after applying SMOTE reached 0.85, 0.84, and 0.85, respectively, representing a substantial improvement compared to the results before SMOTE. This study highlights the importance of using SMOTE to enhance sentiment analysis accuracy, particularly in addressing class imbalance issues within the dataset.
Klasifikasi Kerusakan Uang Rupiah Menggunakan CNN Dengan Arsitektur VGG16 Muhamad Rizvi Roshan; Hafiz Irsyad
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15125

Abstract

This study developed a deep learning model using a Convolutional Neural Network (CNN) architecture with VGG16 to classify the level of damage to rupiah banknotes. Previous studies have focused more on recognizing denominations and detecting counterfeit money using CNN and transfer learning, while the classification of physical damage to rupiah banknotes is still limited, both locally and internationally, and often relies on special acquisition devices or template registration. The dataset used consists of images of rupiah banknotes grouped into three damage categories: >20%, >40%, and >50%. This dataset is divided into 80% for training data (537 images) and 20% for test data (135 images). To enrich the data variety, this study applied on-the-fly data augmentation techniques with rotation, zoom, and flipping during the training process. The experimental results show that this model achieves an accuracy of 93.33%, with excellent precision, recall, and F1-score values, especially in the >50% damage category. The use of the ADAM optimizer with a learning rate of 1e-3 proved to provide more stable and efficient training. Overall, this study shows that the application of CNN with the VGG16 architecture is effective in classifying rupiah currency damage and can contribute to the development of image processing technology, particularly for evaluating currency feasibility in real-world scenarios.
Co-Authors Adikara Alif Nurrahman Ahmad Rizky Earlando Moza Hafiz Irsyad Ramanda Md