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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Walisongo Journal of Information Technology
Mahfudh, Adzhal Arwani
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Computer Science & IT Engineering

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Journal : Building of Informatics, Technology and Science

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Implementasi Arsitektur MobileNetV2 Berbasis Citra untuk Deteksi Penyakit Dropsy dan Popeye pada Ikan Cupang Musyaffa, Fadhilah Rafi; Mahfudh, Adzhal Arwani; Subowo, Moh Hadi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9379

Abstract

The identification of diseases in betta fish based on visual symptoms remains a challenge, particularly for beginners who lack experience in recognizing disease characteristics. This study aims to implement an image-based MobileNetV2 architecture as a diagnostic support system to detect dropsy and popeye diseases in betta fish that have already exhibited visual symptoms. The dataset used in this study consists of 600 betta fish images divided into three classes: healthy, dropsy, and popeye, with 200 images in each class, collected from the internet. Data preprocessing was conducted through image ratio adjustment, normalization, and data augmentation to increase data variability. A transfer learning approach was applied by freezing most layers of the MobileNetV2 feature extractor and fine-tuning several of the final layers. Model evaluation was performed using 5-Fold Cross Validation to ensure experimental stability and reproducibility. The best model from each fold was then combined using an ensemble method based on average probability to improve prediction performance on the test dataset. Experimental results show that the average 5-Fold Cross Validation accuracy reached 74.71% with a standard deviation of ±4.57%, while the Macro-F1 score achieved ±74.43%. The ensemble approach produced a test accuracy of 85.56% with balanced classification performance across all classes. Grad-CAM visualizations indicate that the model is able to focus on image regions relevant to disease symptoms. These findings demonstrate that the MobileNetV2 architecture is effective as an image-based diagnostic support tool for betta fish diseases.
Analisis Sentimen Persepsi Publik Terhadap Program MBG Pada Komentar YouTube Menggunakan Naïve Bayes dan Resampling Najib, Lutfi; Mahfudh, Adzhal Arwani; Bakhri, Syaiful
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9400

Abstract

The Free Nutritious Meal Program (MBG), launched by the Indonesian government in 2025, has generated diverse public responses on social media, particularly on YouTube as an open digital discussion space. This study aims to analyze public perception of the MBG program through sentiment classification of YouTube comments using the Multinomial Naïve Bayes algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The dataset consists of 1,082 comments categorized into three sentiment classes: negative, neutral, and positive. The data distribution reveals significant class imbalance, with negative sentiment dominating at 70.61%. The baseline model achieved an accuracy of 70.67% with a macro F1-score of 27.60%, indicating bias toward the majority class. To address this imbalance, Random Oversampling (ROS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied. Although overall accuracy decreased to approximately 51% after resampling, the macro F1-score improved to 36.24% (SMOTE) and 37.09% (ROS), indicating enhanced performance in detecting minority classes. In the context of public policy evaluation, improved sensitivity to minority sentiment is considered more representative than high but biased accuracy. These findings highlight the importance of handling class imbalance in social media–based sentiment analysis for public policy monitoring.
Co-Authors Alfaza, Ahilla Salma Faruq, Muhammad Kholfan Khothibul Umam Mokhamad Iklil Mustofa Mustofa, Hery Musyaffa, Fadhilah Rafi Najib, Lutfi Rizki, Favian Agung Subowo, Moh Hadi Syaiful Bakhri Zakiyah, Na'ilah Puti