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Journal : building of informatics technology and science

Implementasi Arsitektur MobileNetV2 Berbasis Citra untuk Deteksi Penyakit Dropsy dan Popeye pada Ikan Cupang Fadhilah Rafi Musyaffa; Adzhal Arwani Mahfudh; Moh Hadi Subowo
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 Lutfi Najib; Adzhal Arwani Mahfudh; Syaiful Bakhri
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.
Integrasi Model Hibrida TOPSIS-BORDA untuk Penentuan Prioritas Strategi Digitalisasi Pesantren yang Berkelanjutan Shofi Putri Lathifah; Adzhal Arwani Mahfud; Wenty Dwi Yuniarti; Khotibul Umam
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.v7i4.9439

Abstract

Digital transformation in Islamic boarding schools (pesantren) holds a specific urgency as these institutions face the challenge of integrating administrative governance modernization with the preservation of salaf traditions, a dilemma rarely found in general formal education. Resource limitations and preference differences among leaders and administrators often trigger strategic deadlocks. This study aims to determine sustainable digitalization strategies at Pondok Pesantren YPMI Al-Firdaus Semarang by integrating the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Borda Count. The TOPSIS model is utilized to objectively evaluate the technical feasibility of seven criteria, while BORDA facilitates quantitative deliberation to accommodate the preferences of five decision-makers. The analysis results indicate that alternative A1 (Digitalization of Administrative Management) becomes the main priority with a relative closeness value of 0.738 and a BORDA consensus score of 8.7664. This figure significantly outperforms other alternatives, proving that the improvement of basic administration is the most urgent and mutually agreed-upon foundation before the pesantren advances to more complex digitalization. Although this study is on a local scale, the integration of these methods proves effective in mapping social and technical compromises, and can be adapted by other pesantren with similar characteristics.