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International Journal of Informatics and Computation
Published by Universitas Respati Yogyakarta
ISSN : 26858711     EISSN : 27145263     DOI : 10.35842/ijicom
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Computation (IJICOM) is an international, peer-reviewed, open-access journal, that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of Informatics includes technologies of information and communication as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJICOM aims to be an international platform to exchange novel research results in simulation-based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices. The scope of this journal includes IoT, 5G, Artificial Intelligence, sensor networks, and high-resolution imaging techniques. This new discipline in science combines computational thinking, modern computational methods, devices, and collateral technologies to address problems far beyond the scope of traditional numerical methods
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 63 Documents
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Application of Deep Learning with CNN Method in Identifying Corn Leaf Diseases [Preview] Lengga S. Sandy; Sarjon Defit; Gunadi Widi N.
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.109

Abstract

Diseases in maize plants are one of the main factors contributing to yield reduction, ultimately impacting food security and the sustainability of the agricultural sector. Technological advancements have enabled digital image processing to become a widely applied method in various fields, including object identification, pattern detection, and plant disease classification with the support of Artificial Intelligence (AI). This study utilizes maize leaf images as a dataset, which is then processed using the Convolutional Neural Network (CNN) method based on the ResNet-50 architecture. This architecture is known for its superior ability to extract deep visual features, thereby enhancing classification accuracy. The CNN model operates by identifying and analyzing key features in images, such as color, texture, and damage patterns on maize leaves, to detect the type of disease affecting the plants. The methodology in this research involves several crucial stages, starting from collecting maize leaf image datasets directly from agricultural fields, preprocessing data to improve image quality for optimal model training, and finally, training and evaluating the CNN model’s performance. The initial dataset consisted of 1,199 images, which, after image analysis, was reduced to 870 for processing. These were then split into 552 images for training, 87 for testing, and 261 for validation. The CNN model evaluation with 87 test images showed that the highest precision was achieved in the Rust Disease class (100%). Meanwhile, the highest recall was found in the Normal Leaf class (99%), indicating that the model correctly identified all normal leaf samples. However, the recall for the Fall Armyworm Disease class was 89%. This model accurately identifies damage patterns, making it a potential tool for automatic early disease detection in plants. Thus, this research can serve as a foundation for further developments in AI-based applications to enhance efficiency and accuracy in plant disease diagnosis, ultimately supporting agricultural productivity and sustainable food security.
Comparing Cat Disease Classification using CNN Architectures Ria Wahyusari; Bulkis Kanata; Sudi M Al Sasongko
International Journal of Informatics and Computation Vol. 8 No. 2 (2026): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v8i2.298

Abstract

Accurate classification of feline skin diseases can support early identification and improve access to preliminary veterinary information. This study evaluates three pretrained convolutional neural network (CNN) architectures, ConvNeXtV2 (Atto), MobileNetV4 (ConvSmall), and RegNet (Y-008), for classifying five categories of cat images: feline acne, flea allergy, scabies, healthy cats, and ear mites. We utilize a balanced dataset of 3,000 images, with 600 images per class, and apply image preprocessing and online data augmentation to improve model training. We evaluate the models using three-fold cross-validation and a separate test set of 300 images. The results show that ConvNeXtV2 achieves the highest mean cross-validation accuracy of 96.48%, followed by RegNet at 96.33% and MobileNetV4 at 92.30%. However, RegNet achieves the best test performance, with 89.67% accuracy, 92.61% macro precision, 89.67% macro recall, and an 89.81% macro F1-score. RegNet also achieves 70.00% recall for scabies, outperforming ConvNeXtV2 and MobileNetV4. All three models achieve 100% recall for healthy cats, while scabies remains the most challenging class due to frequent misclassification as feline acne and ear mites. The application rejects predictions with confidence below 50% and clarifies that its output does not replace a veterinary diagnosis. These findings demonstrate the potential of pretrained CNN architectures for feline skin disease classification and identify RegNet as the most effective model among the evaluated architectures.
Improving Performance of LoRa in Agricultural Land Temperature and Humidity Monitoring System Ardhika Rizky Ramadhan; Bulkis Kanata; A Sjamsjiar Rachman
International Journal of Informatics and Computation Vol. 8 No. 2 (2026): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v8i2.302

Abstract

Limited cellular communication infrastructure, complex topography, and dense vegetation in rural areas pose major challenges to the implementation of smart agriculture. This study aims to analyze the performance and stability of a 433 MHz LoRa wireless communication network under Non-Line-of-Sight (NLOS) conditions and evaluate the accuracy of temperature and soil moisture sensors in an automatic irrigation prototype. We conducted field tests in Bentenu Hamlet, Banyu Urip Village, West Lombok Regency, at distances of 100 m, 300 m, and 500 m under two propagation scenarios: dense-vegetation NLOS and hilly-terrain NLOS. The results showed that the dense-vegetation path at 100 m achieved excellent signal quality, with an average Received Signal Strength Indicator (RSSI) of −39.67 dBm and a Signal-to-Noise Ratio (SNR) of +12.75 dB. At the maximum distance of 500 m, signal quality degraded to −101.50 dBm RSSI and −0.62 dB SNR under dense-vegetation conditions, classified as fair, and to −105.30 dBm RSSI and −8.63 dB SNR under hilly-terrain conditions, classified as poor. Despite the declining signal quality and negative SNR, the recorded packet loss remained at 0.00% across all tested distances and propagation conditions. Regarding sensor accuracy, the capacitive soil moisture sensor V1.2 achieved a Mean Absolute Error (MAE) of 1.24% volumetric water content (VWC), corresponding to a reported accuracy of 98.76%. The DHT11 air temperature sensor achieved an MAE of 0.38 °C, with a reported accuracy of 99.62%, compared with the standard reference instrument. These findings demonstrate the potential of 433 MHz LoRa technology for supporting automatic irrigation in rural agricultural areas with challenging terrain.

Page 7 of 7 | Total Record : 63

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