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All Journal Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Pilar Nusa Mandiri Indonesian Journal of Business Intelligence (IJUBI) Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknologi Informatika dan Komputer Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Abdimas Komunikasi dan Bahasa Journal of Accounting Information System AMMA : Jurnal Pengabdian Masyarakat
Veti Apriana, Veti
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Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Social Sciences

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Sistem Kecerdasan Buatan Untuk Deteksi Kondisi Daun Berbasis Metode Klasifikasi Fahrozi, Habil; Adiansyah, Rifky Ramadhan; Samit, Zaidan; Sujiliani, Sujiliani; Santoso, Rame; Apriana, Veti
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2315

Abstract

Plant diseases pose a significant threat to agricultural productivity. This study aims to develop and evaluate an artificial intelligence system capable of automatically detecting leaf health conditions and comparing the performance of two different deep learning architectures. Leaf image data obtained from the Kaggle dataset were processed and classified using Convolutional Neural Network (CNN) and MobileNetV2, while the YOLOv8 algorithm was applied to detect leaf objects within the images. The main evaluation metric used was classification accuracy to assess the model’s ability to identify whether a leaf is healthy or diseased. The results demonstrate the efficiency and comparative performance of both classification methods. The best-performing model was then implemented into a Python-based web application, enabling users to upload leaf images and obtain real-time health detection results. This implementation provides a practical contribution to the development of precision agriculture systems.
Co-Authors Adiansyah, Rifky Ramadhan Ardian Dwi Praba Christian , Ade Doni Andriansyah Erawati, Wati Erzab, Anggi Okliata Evy Priyanti Fahmi , Muhammad Fahrozi, Habil Fauziah , Sifa Gitisari, Deva Indradewa, Rhian Kuncoro, Dwiyanto Mutiah, Septiany Khoirum Nisrina, Restu Putri Putri, Nayla Natania Rame Santoso Riezky Pahlevi, Raden Mohammad Samit, Zaidan Sujiliani Heristian Sujiliani, Sujiliani Sumarna , Sumarna Supriyatna , Adi Syahriani Yani , Ahmad