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All Journal Arcitech: Journal of Computer Science and Artificial Intelligence Applied Information Technology and Computer Science (AICOMS)
Md, Ramanda
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Computer Science & IT

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Opini Publik terhadap Isu Pengoplosan Pertamax di Youtube Menggunakan Metode Naive Bayes Adikara Alif Nurrahman; Moza , Earlando; Md, Ramanda; Rizvi Roshan , Muhamad; Rizky , Ahmad; Irsyad , Hafiz
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 Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak md, Ramanda; Hartati, Ery
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.v5i2.15122

Abstract

Early detection of Alzheimer's disease is crucial for effective treatment, and the use of brain MRI images has become a common method for diagnosis. However, many previous studies have faced challenges in addressing class imbalance in their datasets, leading to lower accuracy for minority classes. This study aims to address this issue by using a pretrained CNN architecture, VGG19, combined with the SMOTE method to address class integration and improve classification accuracy. This study contributes by introducing SMOTE to the Alzheimer's MRI image dataset to achieve a more balanced class distribution, which has not been fully explored in previous studies. The evaluation results show that the classification accuracy reaches 95%, higher than previous studies using VGG-19 with an accuracy of 77.66%. These results confirm that the use of VGG19 with SMOTE produces better performance, especially in addressing class representation, which is a key contribution of this study. This research has the potential to be applied in more efficient and accurate automated image-based detection systems, especially for the early diagnosis of Alzheimer's disease.
Co-Authors Adikara Alif Nurrahman Hartati, Ery Irsyad , Hafiz Moza , Earlando Rizky , Ahmad Rizvi Roshan , Muhamad