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Nafi’iyah, Nur
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Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

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Implementasi MobileNetV2 Untuk Pengenalan Presisi Penyakit Daun Kopi Berbasis Citra Anggraini, Selvi Fitria; Nafi’iyah, Nur; Qomariyah N, Nur
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9876

Abstract

ABSTRACT Coffee is one of Indonesia’s leading commodities, playing a vital role in the national economy, providing employment opportunities, and serving as a primary source of income for many farmers. However, coffee productivity is often reduced due to pest and disease attacks, particularly on th leaves, such as coffee leaf rust and red spider mites. These diseases can disrupt photosynthesis, lowe plant quality, and even cause plant death if not addressed promptly. Manual identification at the farmer level is often challanging due to limited knowledge and the similarity of visual symptoms betwen diseases. This study aims to develop an image classification system for detecting healthy leaves, leaf rust, and red spider mite infestations on coffee plants automatically. The method employed is machine learning based on a Convolutional Neural Network architecture using MobileNetV2 and transfer learning. The dataset consists of 501 images of coffee leaves, divided into 456 training data and 45 testing data. The model was trained to distinguish between the three classes, achieving a training accuracy of 64% and a testing accuracy of 56%. The resulting model was then integrated into a web-based application using Streamlit, enabling easy access for farmers and the general public. This system is expected to facilitate early detection of coffee leaf diseases in a faster, more practical, and affordable way, allowing farmers to take timely action before damage spreads. In the long term, this technology is anticipated to support improved coffee plantation productivity in Indonesia. Keywords: image classification, coffee leaf, MobileNetV2, transfer learning.
Implementasi Implementasi Cnn Berbasis Deep Learning Untuk Klasifikasi Penyakit Daun Jagung Askan, Valentiena Prastika Putrie; Nafi’iyah, Nur
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9885

Abstract

This study aims to develop and evaluate a convolutional neural network (CNN)-based model for classifying corn leaf diseases using a simple yet effective architecture. Four disease classes were considered: healthy, gray leaf spot, leaf blight, and common rust. A dataset comprising 13,136 images was obtained from the open-source PlantVillage Dataset and processed using class balancing techniques to mitigate prediction bias. Each image was resized to 256×256 pixels, normalized, and split into training (80%) and testing (20%) sets. The proposed CNN architecture consists of four convolutional layers with progressively increasing filters (16, 32, 64, 128), followed by max pooling, dropout, and two fully connected layers. The model was trained for 50 epochs using the Adam optimizer with categorical cross-entropy as the loss function. Performance evaluation, based on accuracy, precision, recall, and F1-score, achieved an accuracy of 97.18% with consistently high metrics across all classes. The results were further visualized using a confusion matrix and classification report. Finally, the trained model was deployed in a Flask-based web application, enabling users to upload corn leaf images and receive automated detection results. These findings demonstrate that a simple CNN architecture can achieve high accuracy in classifying corn leaf diseases and holds significant potential for integration into digital plant disease monitoring systems.
Co-Authors Anggraini, Selvi Fitria Askan, Valentiena Prastika Putrie Qomariyah N, Nur