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All Journal Jurnal Pengabdian Rekayasa Sistem Journal of Applied Informatics Science
Jonathan Afriliansyah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Other

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HIDUP DI DUNIA OTOMATIS: AI, IOT, DAN PERUBAHAN SOSIAL I Made Widiarta; Shinta Esabella; Jonathan Afriliansyah
Jurnal Pengabdian Rekayasa Sistem Vol 3 No 2 (2025): Edisi 6
Publisher : Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36761/jpres.v3i2.6084

Abstract

Teknologi Informasi adalah kombinasi dari teknologi komputer (perangkat keras dan perangkat lunak) dan teknologi komunikasi informasi untuk memproses, menyimpan, dan mendistribusikan informasi (Martin, E. et al, 2005). Kecerdasan buatan atau artificial intelligence (AI) merupakan salah satu bagian ilmu komputer yang membuat agar mesin (komputer) dapat melakukan pekerjaan seperti dan sebaik yang dilakukan oleh manusia. Sedangkan Internet of Things merupakan konsep yang menghubungkan perangkat fisik ke internet untuk berkomunikasi dan berbagi data secara babas. Adapaun peserta dalam webinar berjumlah 100 orang yang terdiri dari mahasiswa, dosen, Relawan TIK di Indonesia. Webinar ini menghadirkan Narasumber ahli dari perguruan tinggi dan juga Relawan TIK yang memaparkan sekaligus demonstrasi keilmuan sesuai dengan tema Webinar. Webinar ini diharapkan dapat membekali peserta dalam menghadapi perkembangan teknologi informasi dan dampak sosialnya di masyarakat serta beradaptasi dengan tools-tools yang tercipta karenanya.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v2.i2.96

Abstract

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.
Co-Authors Eri Sasmita Susanto Fahri Hamdani Herfandi Herfandi I Made Widiarta Shinta Esabella