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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik ICIT (Innovative Creative and Information Technology) Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Decode: Jurnal Pendidikan Teknologi Informasi Eksplorasi Teknologi Enterprise & Sistem Informasi (EKSTENSI) Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Witta Listiya Ningrum
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Mobile Application for Plant Leaf Disease Classification Using Convolutional Neural Networks Fajar, Luqman Nur; Ningrum, Witta Listiya; Kurniawati, Ana
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 3: NOVEMBER 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i3.1676

Abstract

Indonesia, as an agrarian country, faces serious challenges in agriculture, particularly plant leaf diseases that may lead to crop failure. Early detection is crucial to prevent their spread. This study aims to develop a leaf disease classification model using a Convolutional Neural Network (CNN) and implement it in an Android application called Leaf Lives. The model was developed using the EfficientNetB0 architecture and optimized with Stochastic Gradient Descent (SGD), trained for 30 epochs and fine-tuned for an additional 10 epochs. The dataset consisted of 44,030 leaf images covering 11 plant species and 60 disease or leaf-condition classes, obtained from public datasets available through platforms such as Kaggle and Mendeley Data. Four data split scenarios were evaluated (75:15:10, 70:20:10, 65:25:10, and 60:30:10). The best performance was achieved with the 70:20:10 split, yielding 92.03% accuracy, 91.12% precision, 89.89% recall, and 89.96% F1-score. The resulting model was then integrated into an Android application that classifies leaf images captured using the camera or selected from the gallery and stores classification history offline. The developed application is expected to assist farmers in identifying potential leaf diseases quickly and independently; however, further validation using real-field images and user-based evaluation is required to assess its practical effectiveness.
Co-Authors Ahmad Hidayat Ahmad Hidayat Ana Kurniawati Ananda Putri K Fajar, Luqman Nur Farida Amalya Gibran, Naufal Halim Ike Putri Kusumawijaya Irawati, Dyah Cita Irfan Humaini Kemal Ade Sekarwati Laksono Trisnantoro M., Lulu Chaerani Miftahul Jannah Muhammad Ivan Hidayatullah Naeli Umniati Pralega, Muhammad Wesya Rani Puspita WIDIASTUTI -