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Arwani Arwani
Politeknik Kesehatan Kemenkes Semarang
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Journal : building of informatics technology and science

 1 
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.
Komparasi CNN dan SVM untuk Klasifikasi Penyakit Daun Jagung Berbasis CLAHE dan Explainable AI Muhammad Azka Najih Al Wafa; Adzhal Arwani Mahfudh; Masy Ari Ulinuha
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11025

Abstract

Corn productivity losses from leaf diseases remain a serious problem, while manual observation is subjective and accurate models are hard to trust when their decisions cannot be traced. The contribution of this research lies in controlled testing that isolates the contribution of CLAHE from architectural influences, establishing preprocessing parameters based on empirical testing, and directly comparing both methods under identical data and treatment conditions. This study compares Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for corn leaf disease classification under equivalent conditions, using CLAHE preprocessing and LIME. Testing used 4,188 images in four classes. The CNN based on EfficientNet-B0 reached 94.98%, outperforming the SVM based on GLCM and color histogram features at 92.46%. This 2.52-point margin is narrower than assumed, and the additional SVM errors concentrate in one class. A hybrid combining EfficientNet-B0 features with an SVM classifier reached 95.33%, indicating the CNN advantage stems from its feature representation rather than its decision layer. The SVM also predicted one hundred sixty times faster without a graphics accelerator. An ablation test shows the CLAHE benefit is method-dependent: the SVM gained 1.19 points, the CNN did not. LIME shows most influential regions fall on lesion areas, though some appear in the background. These findings keep the SVM relevant when computational resources are limited.
Co-Authors Aat Ruchiat Nugraha Afifah Salsabila Afifah Salsabila Afikirtiani, Winda Ahilla Salma Alfaza Ahmad Baequny Ahmad Riadi Ahmad Riadi Alfaza, Ahilla Salma Anonim, Tri Ari Suwondo Arina Rihadatulaisy Sabila Arina Tina Salwa Bedjo Santoso Bedjo Santoso Kadri Budi Agus Cahyono Budi Widiyanto Cahaya Nugraheni Charis Kusnanto Chatarina Umbul Wahyuni Chyntia Haryana Dina Indrati Dyah Sulistyowati Diyah Fatmasari Dwi Ari Murti Widigdo Dwi Ari Widigdo DwiKurniadi, Septian Epf. Eko Yulipriyono Ermi Lilianda Alang Estuasih Dyah Pertiwi, Estuasih Dyah Fachry Abda El Rahman Fadhilah Rafi Musyaffa Fadhilah Rosyid Pradana Faruq, Muhammad Kholfan Fauzia, Fadhila Favian Agung Rizki Fitriyanti, Suci Abrelia Gusti Noorlitaria Achmad Hera Lusi Ayuandita Iis Sriningsih Ika Suciatmi Indar Widowati Irmalita Wigati Irmalita Wigati Irmawati Irmawati Irmawati Irmawati Jamhari Jamhari Jauhary, Ermilya Restiani Karlina, Leni Khairulisni Saniati Kharisma Anjar Nugraha Khothibul Umam Khotibul Umam Krisdiana Wijayanti Lestari, Shelvi Ovi Lucky Herawati Luh Komang Candra Dewi Lutfi Najib Mahendra Wekoadi, Galih Mardi Hartono Mardiyono Mardiyono Mardiyono, Mardiyono Marsum Marsum Martha Cintia Dewi Martha Cintia Dewi Masy Ari Ulinuha Melyana Nurul W Melyana Nurul Widyawati Mochammad Ali Ridho Fathoni Moh Hadi Subowo Mokhamad Iklil Mustofa Monika Herti Kurniasari Muhamad Jauhar Muhammad Azka Najih Al Wafa Mustofa, Hery Nabila Salsabila Nawang Setyoningrum Nina Indriyawati Nina Indriyawati Nina Indriyawati, MNS. Nisful Laila Novi Indriani Nuni Oktaviani Nur Cahyo Hendro Wibowo Nur Khasanah Nurul Huda Oktavia, Jinhan Nur Prabowo, Rully Hadi Pujiastuti, Rr Sri Endang Pujiastuti, Rr. Sri Endang Putra, Riskiyana Sukandhi Riskiyana Sukandhi Putra RISMAN ABDI RAPIUDDIN Rizki, Favian Agung Rodhi Hartono Roro Sri Endang Pujiastuti Rosmiati Saleh Rr. Sri Endang Pujiastuti Rumbrawer, Daud Yusuf Sahrul Ramadhani Sahrul Widyastuti Shelvi Ovi Lestari Shobirun Shobirun Shofi Putri Lathifah Silvia Ekasari Simanjuntak, Veronica Siti Nur Luthfiana Siti Nuraini Siti Rizki Amalia Siti Rochjani Siti Syarashinta Cahyaningsih Siti Wahyuni Sofyana, Yudha Sri Endang Windiarti Sri Endang Windiarti Sri Lestari Sri Sumarni Sri Wahyuni Sri Widiyati Sri Widiyati, Sri Sudiharto Sudiharto Sudirman Sudirman Sudirman Sugih Wijayanti Sugih Wijayati Sugih Wijayati, Sugih Suharsono Suharto Suharto Suharyo Hadisaputro Sunarto Sunarto Supardi Supardi Supardi Supardi Suprihatin Suprihatin Supriyadi Supriyadi Supriyana Supriyana Suryati Kumorowulan Suryati Kumorowulan Suryo Pratikwo Susanti, Maria Suwando, Ari Syaiful Bakhri Syaiful Bakhri Ta'adi Ta'adi Ta'adi . Tri Nova Aprianti Tri Wiji Lestari Umi Margi Rahayu Umi Toifah Valentina, Alina Veronika, Sindy Viola Violentina Wahyu Rizqy Saputra Warijan Warijan Wekoadi, Galih Mahendra Wenty Dwi Yuniarti Wien Soelistyo Adi Wien Soelistyo Adi Yuliartini, Henny Zaenal Amirudin Zakiyah, Na'ilah Puti Zulfa, Alfania