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All Journal Informatics and Enginering Dedication INOVTEK Polbeng - Seri Informatika
Fernando Feliansyah
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Computer Science & IT Other

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Enhancing Generalization of Tomato Leaf Disease Classification via TDR Model and Field-Conditioned Data Augmentation Fernando Feliansyah; Ery Hartati
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tvgfx074

Abstract

Tomato leaf diseases significantly affect agricultural productivity, particularly when detection systems are deployed under real-field conditions characterized by illumination variation, background clutter, and image noise. Although deep learning-based models have achieved high accuracy on laboratory datasets such as PlantVillage, their generalization performance often degrades when applied to real-world environments. This study proposes a lightweight CNN-based tomato leaf disease recognition model, referred to as the TDR-Model, combined with field-conditioned data augmentation strategies. The proposed model integrates MobileNetV3 with Convolutional Block Attention Module (CBAM) and Omni-Dimensional Dynamic Convolution (ODC) to enhance feature representation while maintaining computational efficiency. Field-conditioned augmentation using the Albumentations library to simulate real-world visual variations during training. The model is evaluated on the real-world tomato set consisting of 10 classes and 885 leaf images. Experimental results show that the proposed model achieves an overall test accuracy of 82.94%, with precision, recall, and F1-score of 85.06%, 83.04%, and 83.03%, respectively. Furthermore, the model requires only 3.47 million parameters, 0.23 GFLOPs, and an average inference time of 5.15 ms, making it suitable for real-time and resource-constrained agricultural applications. These results indicate that the proposed approach effectively balances accuracy and efficiency for practical tomato leaf disease detection.
PENGENALAN TEKNOLOGI RFID DALAM SISTEM ABSENSI OTOMATIS BERBASIS KARTU FLAZZ DI SMA XAVERIUS 3 PALEMBANG Adrian Suparto; Michael Joy Clement; Jovansa Putra Laksana; Brilliant Chandra Pratama; Fernando Feliansyah; Muhammad Rizky Pribadi; Eka Puji Widiyanto
FORDICATE Vol 5 No 1 (2025): November 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i3.11704

Abstract

Abstrak: Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkenalkan konsep dan implementasi sistem absensi otomatis berbasis teknologi RFID di lingkungan sekolah menengah. Tujuan utama kegiatan adalah memberikan pemahaman praktis kepada siswa mengenai cara kerja sistem absensi tanpa kontak dan manfaatnya dalam meningkatkan efisiensi administrasi. Metode yang digunakan meliputi sosialisasi langsung, penyampaian materi visual, serta demonstrasi aplikasi prototipe yang dikembangkan menggunakan antarmuka berbasis web dan pemindai kartu RFID. Hasil kegiatan menunjukkan respons positif dari siswa terhadap penggunaan teknologi tersebut. Demonstrasi berhasil memperlihatkan proses pencatatan kehadiran secara otomatis menggunakan kartu RFID dan bagaimana data disimpan dalam basis data lokal. Meskipun sistem belum diadopsi oleh pihak sekolah, aplikasi ini menunjukkan potensi sebagai solusi digital yang dapat diimplementasikan di masa mendatang. Kegiatan ini memberikan manfaat edukatif sekaligus mendorong kesadaran akan pentingnya transformasi digital dalam tata kelola sekolah.
Co-Authors Adrian Suparto Brilliant Chandra Pratama Eka Puji Widiyanto Hartati, Ery Jovansa Putra Laksana Michael Joy Clement Muhammad Rizky Pribadi