Article Per Year (5 Year)
p-Index From 2021 - 2026
2.994
P-Index
This Author published in this journals
All Journal JBIO: jurnal biosains (the journal of biosciences) Abdimas JURNAL PENGABDIAN KEPADA MASYARAKAT JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Agrotekma: Jurnal Agroteknologi dan Ilmu Pertanian JMM (Jurnal Masyarakat Mandiri) Community Development Journal: Jurnal Pengabdian Masyarakat AGRISAINS: Jurnal Ilmiah Magister Agribisnis Pelita Masyarakat Jurnal Ilmiah Pertanian ( JIPERTA) Budapest International Research in Exact Sciences (BirEx Journal) Djtechno: Jurnal Teknologi Informasi Journal of Natural Sciences International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Warta Perkaretan Prioritas : Jurnal Pengabdian Kepada Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat
Asmah Indrawati
Universitas Medan Area
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 42 Documents
Search

 1   2   3   4   5 
A Hybrid Framework Combining U-Net, Ant Colony Optimization, and CNN for Rice Leaf Disease Classification under Class Imbalance Ongko, Erianto; Indrawati, Asmah; Sukiman, Sukiman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6910

Abstract

Accurate classification of rice plant diseases is essential for early intervention and precision agriculture. However, real-world datasets often suffer from complex backgrounds, high-dimensional features, and severe class imbalances, which compromise classification performance. This study proposes an integrated framework combining image segmentation using U-Net, feature selection via Ant Colony Optimization (ACO), hybrid sampling to handle class imbalance, and final classification using a Convolutional Neural Network (CNN). Segmentation isolates disease-affected areas, ACO optimizes feature subsets, and hybrid sampling balances class distribution using undersampling and SMOTE. The proposed method was tested on four rice leaf disease datasets—Brown Spot, Leaf Blast, Leaf Blight, and Leaf Scald—exhibiting significant class imbalance. Experimental results show that the proposed approach outperforms baseline models (SegNet, PspNet, and E-Net) across multiple metrics: Accuracy, IoU, Precision, and Recall. This indicates the framework’s robustness and potential for real-world deployment in precision agriculture. Future work will focus on model compression and real-time implementation in IoT systems.
Enhanced RegNetY-400MF for Fruit Fly Species Classification: Fine-Tuning Strategies and Data Balancing for Improved Accuracy Rahman, Sayuti; Indrawati, Asmah; Zen, Muhammad; Zealtiel, Billiam; Tanjung, Shabila Shaharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6973

Abstract

Fruit fly infestations pose a significant threat to agricultural productivity, especially in chili plantations, which can cause substantial yield losses. Accurate and rapid species classification is crucial for implementing targeted pest control strategies. This study developed a computationally efficient fruit fly species classification model using a deep learning approach that focused on improving accuracy with fine tuning and class balancing strategies. The dataset consists of 1049 images across 4 fruit fly species, captured in a natural plantation environment and available at www.inaturalist.org. The model evaluated several lightweight Convolutional Neural Network (CNN) architectures, including MobileNetV3-Small, RegNetY-400MF, and SqueezeNet among others, with RegNetY-400MF emerging as the best performing model, achieving a validation accuracy of 96.10% and a macro F1 score of 95.70%. The models tested in this study included several lightweight Convolutional Neural Network (CNN) architectures, including MobileNetV3-Small, RegNetY-400MF, and SqueezeNet, among others. RegNetY-400MF proved to be the best performing model, achieving a validation accuracy of 96.10% and a macro F1 score of 95.70%. Compared to other state-of-the-art models, RegNetY-400MF demonstrated higher accuracy while maintaining a lower number of parameters (8.3 million) and reduced computational complexity (0.41 GFLOPs). This makes the model highly suitable for real-time applications in resource-constrained agricultural environments. The model offers a practical solution for fruit fly species detection, enabling early and accurate identification of pest infestations in chili plantations, thereby reducing the risk of crop failure. By providing an efficient and scalable pest control tool, the model supports precision pest management, improves yield stability, and contributes to sustainable agriculture.
Co-Authors Abdul Rahman Afrinaldi Lubis Agung Syahputra Antahar, Diva Azwana Azwana Beby Masitoh Beby Masitoh Deddi Prima Putra Dian Noviandri Erianto Ongko Ernita Siahaan Fachru Yuzairi Gepin Sianipar Gusmeizal Gusmeizal Habib Satria Habibi, Khusnan Hartono Hartono Heru Kurniawan Ilham hidayat INDRA HERMAWAN Junaidi Junaidi Khafiz, Khafiz Khafiz, Khafiz M. Agung Saputra M Maemunah Maemunah Manurung, Dionikxon Mardiatul Hasanah Mitra Musika Lubis Moranain Mungkin Muhammad Khahfi Zuhanda Muhammad Usman Muhammad Usman, Muhammad Muhammad Yogie Ernanda Muhammad Zen, Muhammad Noer, Zoelherly Rahman, Sayuti Retna Astuti Rio Saputra Silaban Rizal Aziz Rizal Aziz Rizal Aziz, Rizal Roy Fernando Saipul Sihotang Sembiring, Arnes Setyadi, Rahmat Arief Siti Mardiana Sukiman Sukiman Sumihar Hutapea Suswati . Suswati Suswati Suswati Suswati suswati suswati Syahbudin Hasibuan Syahputra, Agung Syahputra, Agung Tanjung, Shabila Shaharani Tumpal HS Siregar Tumpal HS Siregar, Tumpal HS Zealtiel, Billiam Zulheri Noer