Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Hasil Pengabdian Kepada Masyarakat Universitas Jember
Abiyyu, Ahmad Naufal
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENDAMPINGAN DAN EVALUASI KETERSEDIAAN AIR IRIGASI WILAYAH KELOMPOK TANI RAHARJO DESA GUMUKMAS, KECAMATAN GUMUKMAS, KABUPATEN JEMBER Pambudi, Akbar Setyo; Andriyani, Idah; Hita, Muhammad Arga; Karomah Hidayah, Darul Alfan; Abiyyu, Ahmad Naufal; Arif, Ahmad Zidan; Jones, Mochammad Roy
Jurnal Hasil Pengabdian kepada Masyarakat Universitas Jember Vol 4 No 1 (2025): Jurnal Hasil Pengabdian Kepada Masyarakat Universitas Jember
Publisher : LP2M Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Irrigation is an effort to provide water, manage water (management), and drainage for plant needs. Raharjo Farmers Group is one of the farmer groups in Gumukmas Village, Gumukmas District, Jember Regency, which is located close to the coastal area or can be said to be the most downstream area of ​​the flow. This research activity entitled "Assistance and Evaluation of Irrigation Water Availability in the Raharjo Farmers Group Area, Gumukmas Village, Gumukmas District, Jember Regency, was carried out with the aim of obtaining data and information related to water sources that irrigate agricultural land which are used as the basis for processing recommendation maps for assistance and evaluation of irrigation water availability. The first stage of this activity is carried out with direct survey and investigation activities in the field, at this stage existing field conditions will be obtained in the form of channel conditions, building conditions (if any), information on the Cropping Index, Cropping Patterns and so on. After the survey and investigation stages, the design planning stage continues, at this stage the data that has been collected will continue to the data processing stage according to the needs of the research activity. The final planned result is a thematic map containing information on points or recommendation points or channel problems. The thematic map is used as study material for the East Java Provincial Agriculture and Food Security Service as a reference for policy making for better development.
Comparison of Transfer learning Models MobileNetV3-Large and EfficientNet-B0 for Rice Leaf Disease Classification Abiyyu, Ahmad Naufal; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12033

Abstract

Rice productivity strongly depends on early detection of leaf diseases, while manual identification is often delayed and subjective. This study investigates the use of lightweight CNN architectures MobileNetV3-Large and EfficientNet-B0 based on transfer learning to classify six rice leaf disease classes, namely bacterial leaf blight, brown spot, healthy, leaf blast, leaf scald, and narrow brown spot. The dataset is obtained from Kaggle and consists of 2,628 images with a balanced class distribution, stratified into training, validation, and test sets with a ratio of 80%:10%:10%. The images are resized to 224×224 pixels and data augmentation was applied to the training set. Pretrained ImageNet weights are first used as frozen feature extractors, followed by partial fine-tuning of the last 30% backbone layers, with custom classification layers trained using the Adam optimizer with an early stopping mechanism. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices, while computational efficiency is assessed based on parameter count and inference speed measured in frames per second. The results show that under partial fine-tuning MobileNetV3-Large achieves 95.83% test accuracy and 95.80% macro F1-score with 3.12 million parameters, while EfficientNet-B0 obtains 93.18% accuracy and 93.02% macro F1-score with 4.21 million parameters. Both models achieve inference speeds above 50 frames per second, suggesting their potential suitability for deployment on resource-constrained devices. Bootstrap analysis suggests the performance gap is clear in the frozen stage but becomes less conclusive after partial fine-tuning. Overall, MobileNetV3-Large provides the best trade-off between accuracy and efficiency for rice leaf disease classification.
Co-Authors Akbar Setyo Pambudi Arif, Ahmad Zidan Hita, Muhammad Arga Idah Andriyani Jones, Mochammad Roy Karomah Hidayah, Darul Alfan Majid Rahardi