Journal of Applied Informatics Science
Aim The Journal of Applied Informatics Science is dedicated to advancing the discipline of applied informatics by publishing high-quality, peer-reviewed research that integrates theoretical foundations with practical solutions. The journal seeks to promote scientific excellence, foster technological innovation, and support interdisciplinary collaboration within the global informatics community. Its primary objective is to provide an authoritative platform for researchers, academicians, and industry professionals to disseminate original contributions that address emerging challenges, opportunities, and transformations in intelligent and secure computing. Scope The journal welcomes submissions that explore concepts, models, technologies, and applications across a wide spectrum of applied informatics. Areas of interest include, but are not limited to: Intelligent Systems and Artificial Intelligence: machine learning, deep learning, expert systems, natural language processing, computer vision, robotics, autonomous systems, and intelligent agents. Software Engineering: software development methodologies, agile and DevOps approaches, software testing and quality assurance, software architecture, cloud-native development, and distributed systems. Computing Systems: high-performance computing, embedded and real-time systems, parallel computing, Internet of Things (IoT), sensor networks, edge and fog computing, and cyber-physical system architectures. Cybersecurity and Cryptography: secure communication protocols, network security, intrusion detection and prevention systems, cryptographic techniques, cyber threat modeling, blockchain security, and privacy-preserving technologies. Big Data and Data Analytics: scalable data processing frameworks, data mining, predictive analytics, real-time analytics, data streams, visualization techniques, and analytical dashboards. Business Intelligence and Knowledge Management: decision support systems, enterprise data warehousing, knowledge discovery, digital transformation strategies, and AI-driven business process optimization. The journal accepts original research articles, review papers, technical notes, and case studies that contribute to scientific understanding, technological development, policy insight, or practical implementation of informatics solutions. All submissions undergo a rigorous peer-review process to ensure academic integrity, relevance, and quality. The Journal of Applied Informatics Science encourages interdisciplinary research that connects informatics with domains such as healthcare, education, business, environment, industry, and public services.
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CornLeafNet: Disease-Area-Based Corn Leaf Disease Classification Using Convolutional Neural Networks
Herfandi Herfandi;
Eri Sasmita Susanto;
Fahri Hamdani;
Jonathan Afriliansyah
Journal of Applied Informatics Science Volume 2 Issue 2 (2026)
Publisher : GWS Tech Solution
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DOI: 10.65897/jais.v2.i2.96
Corn leaf diseases can reduce crop productivity by disrupting photosynthesis and plant growth. Manual identification in large-scale fields remains limited due to its dependence on observer expertise, visual similarity among disease symptoms, and variations in field conditions. This study proposes CornLeafNet, a Custom Convolutional Neural Network model for disease-area-based corn leaf disease classification. The dataset consists of XML-annotated corn leaf images, from which disease-affected regions were extracted through annotation parsing and bounding box-based cropping to focus the model on symptomatic leaf areas. CornLeafNet was developed to classify three disease categories: Grey Leaf Spot, Corn Rust, and Leaf Blight. The model achieved a validation accuracy of 97.66% and a testing accuracy of 98.60%, with precision, recall, and F1-score values of 0.9860, respectively. The best-performing model was converted into ONNX format and deployed in a web-based prototype for image- and video-based classification. The testing results showed that all core system functions operated as expected, indicating that CornLeafNet has potential as an automatic and practical support model for early corn leaf disease identification.
Implementasi Sistem Informasi Supply Chain Management (SCM) Berbasis Web pada Franchise Ayam Super Fried Chicken
Raafi Muhammad Raafi Juliyanto;
M. Teguh Prihandoyo;
Yerry Febrian Sabanise
Journal of Applied Informatics Science Volume 2 Issue 2 (2026)
Publisher : GWS Tech Solution
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DOI: 10.65897/jais.v2.i2.103
The Ayam Super Fried Chicken franchise previously relied on manual, message-based communication to order raw materials from branch partners to the central warehouse, which frequently caused inaccurate stock records, delayed payment verification, and limited real-time visibility for the business owner. This study aims to design and implement a web-based Supply Chain Management (SCM) information system that automates supply-chain coordination and payment validation for the franchise. The system was built using the Laravel framework, a MySQL database, and Tailwind CSS for the interface, and it integrates the Midtrans payment gateway to automatically verify partner payments. The Waterfall model guided the development process, covering requirement analysis, system design using the Unified Modeling Language (UML), implementation, testing, and maintenance, while Black Box Testing was used to validate system functionality. Testing across four functional modules-authentication, transactions and payment, shipment management, and reporting and security-showed that all eleven tested scenarios ran as expected. The resulting system streamlines raw-material procurement between the central office and branch partners, secures financial transactions through automatic payment verification, and provides real-time operational transparency for franchise management through an analytics dashboard and an activity audit log.