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All Journal Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Indonesian Journal of Electrical Engineering and Computer Science PAKAR Pendidikan Journal of Applied Data Sciences Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) Journal of Computer Scine and Information Technology Jurnal Riset dan Pengabdian Masyarakat Jurnal Ipteks Terapan : research of applied science and education Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo
Silfia Andini
Universitas Putra Indonesia YPTK Padang
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Refining CNN-Based Models for Multi-Class Corn Leaf Disease Classification Anjar Wanto; Poningsih Poningsih; Achmad Daengs GS; Silfia Andini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7400

Abstract

Corn leaf disease significantly impacts agricultural productivity and national food security, particularly in regions with high dependence on maize as a staple commodity. Manual disease identification remains challenging due to the need for expert agronomists, inconsistent environmental conditions, and visual similarities among disease patterns, often resulting in delayed decision-making and inaccurate control measures. Deep learning-based image classification has emerged as an effective solution for plant disease identification; however, existing models often face limitations regarding overfitting, poor generalization, and insufficient performance when applied to multi-class agricultural image datasets. Therefore, this research aims to develop an Improved EfficientNetB0 model for the multi-class classification of maize leaf diseases comprising Healthy, Leaf Blight, Leaf Rust, and Leaf Spot categories. A dataset of 4,000 images was used and processed through resizing, normalization, and augmentation techniques. Five CNN backbones; EfficientNetB0, MobileNetV2, ResNet50, DenseNet121, and InceptionV3—were initially evaluated, and EfficientNetB0 demonstrated the highest baseline performance. The model was subsequently enhanced through fine-tuning, regularization (dropout and batch normalization), and cosine learning rate scheduling. Experimental results show that the Improved EfficientNetB0 achieved superior performance with an accuracy of 0.9671, macro precision of 0.9665, macro recall of 0.9666, and macro F1-score of 0.9661, exceeding all baseline models. These findings demonstrate that the proposed framework effectively improves maize disease classification accuracy and contributes a robust solution for smart agriculture applications. Future work may integrate real-time deployment and mobile-based decision support for field-level monitoring.
Image-Based Detection of Reduced Security Features in Indonesian Banknotes Using U-Net Architecture Silfia Andini; Tukino Tukino
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1087

Abstract

The circulation of fake currency banknotes in Indonesia continues to rise alongside rapid technological advancements, while conventional verification systems remain limited and often ineffective in detecting subtle authenticity cues. The main objective of this study is to develop an image-based fake currency detection system using the U-Net deep learning architecture and its modified version, T-Net, to enhance feature extraction and classification accuracy. The key contribution of this research lies in combining convolutional architectures with a practical, web-based interface that enables real-time image analysis, thus bridging the gap between model performance and user accessibility. A quantitative experimental method was employed, involving model development in Python using TensorFlow and Keras, and implementation of a Flask-based web application for real-time classification. The research utilized a dataset of 2,141 Indonesian rupiah banknote images, consisting of 1,015 genuine and 1,126 fake currency samples synthetically generated through digital modification of security features such as watermarks and color-shifting ink. Image preprocessing included resizing, normalization, and augmentation techniques such as random flipping and brightness adjustment to enhance data quality. Three convolutional architectures U-Net, ResNet-50, and the modified T-Net were trained and compared using identical hyperparameters. The T-Net model achieved the best performance, with 97.8% training accuracy, 82.6% validation accuracy, precision of 0.83, recall of 0.80, and an F1-score of 0.81. Despite the performance gap indicating overfitting, the model effectively distinguishes genuine from fake currency notes. The Flask-based interface allows users to upload images and receive classification results from all three models within 0.3–1.8 seconds per image. The findings demonstrate the feasibility and efficiency of U-Net based architectures for image-driven fake currency detection and provide a foundation for developing advanced, reliable, and real-time financial authentication systems that can strengthen digital security infrastructures in future applications.
Co-Authors Abu Kasim Aditya Franatiaga Ahmad Rivaldi Anggi Muhammad Fajri Aulia Fitrul Hadi Aulia Hadi Deval Gusrion Dinul Akhiyar Edo Septiawan Enggari, Sofika Erdisna Erlinda Erlinda Erlinda Erlinda Eva Rianti Fajrul Islami Gema, Rima Liana GS , Achmad Daengs Halifia Hendri Harkamsyah Andrianof Hendri, Hallifia Iskandar Fitri, Iskandar Jamhur, Annisak Izzaty Lita Gusrian Lukman Ibrahim Majid, Mazlina Abdul Marfalino, Hari Muhammad Akhif Muhammad Fikri Arif Ni Luh Wiwik Sri Rahayu Ginantra Poningsih Poningsih Ragil Ardiansyah Ramadani Sabela Ramadhanu, Agung - Rani Anggraini Retno Devita Ridho Perwira Niza Ridho Perwira Niza Riky Hermansyah Rizkia, Eva Silky Safira Sofika Enggari Sri Rahmawati Sri Restu Ningsih Sumijan Sumijan Surmayanti, Surmayanti Suryana, Febriyanno Thesalonica Claudia Tri Oktaharyadi Putra Tukino Tukino Veni Amirah Putri Veri, Jhon Wahyuni Sutari Hsb Wanto, Anjar Winda Putri Afandi Yenni Desnelita Yuhandri Yuhandri, Yuhandri Zia Farhan Afra