Article Per Year (5 Year)
p-Index From 2021 - 2026
2.795
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) JURIKOM (Jurnal Riset Komputer) JE (Journal of Empowerment) Abdimasku : Jurnal Pengabdian Masyarakat DEDIKASI PKM Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Journal of Multiscale Materials Informatics
Prabowo, Wahyu Aji Eko
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Klasifikasi Penyakit Daun Mangga Menggunakan CNN Berbasis Transfer Learning Dengan Model Arsitektur VGG16, DenseNet121, dan InceptionV3 Purnomo, Zaky Dwi; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9504

Abstract

Mango is one of the important fruits in Indonesia, but its production is often disrupted by leaf diseases and pests that are difficult to detect early. Manual disease recognition methods usually depend on observers and are not always accurate. This study aims to create an automated system to classify mango leaf diseases, using deep learning techniques based on the Convolutional Neural Network (CNN) algorithm. This study also compares three models, namely VGG16, DenseNet121, and InceptionV3, by applying the transfer learning method. The dataset used consists of 4,000 images divided evenly into 8 categories, consisting of 7 types of diseases (Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mold) and 1 category of healthy plants. Evaluation was carried out using the 5-Fold Cross-Validation method to ensure valid results. The results show that all three models are able to provide an accuracy of more than 90%. The VGG16 model showed the best and most stable performance, with an accuracy of 93.25%, a Precision of 0.93, a Recall of 0.93, an F1-Score of 0.93, and an AUC-ROC of 0.98. Meanwhile, InceptionV3 achieved an accuracy of 92.38% and DenseNet121 reached 91.25%. Therefore, VGG16 is recommended as the primary model due to its better ability to extract texture features and accurately recognize mango leaf diseases. VGG16 architecture is able to outperform complex models in efficiently extracting mango leaf texture features, making it very potential to be used as a basis for real-time plant disease diagnosis applications for farmers
Co-Authors Achmad Wahid Kurniawan Adi, Ilham Arif Kuncoro Adriansyah, Vicky Puja Ajisaputra, Faris Prasetya Akrom, Muhamad Almasah, Muhammad Rakha Ayasy, Ahmad Yahya Ayu Pertiwi Budi, Setyo Harun Al Azies Herowati, Wise Hidayat, Novianto Nur Hidayatulloh, Ryan Maulana Wahyu Ibrahim Maulana, Isyeh Rafi Moch Anjas Aprihartha Muhammad Naufal Nadia Itona Siregar, Nadia Itona Noor Ageng Setiyanto, Noor Ageng Nugraini, Siti Hadiati Purnomo, Zaky Dwi Putrawan, Zulhandi Riadi, Denny Fajar Safitri, Aprilyani Nur Sijabat, Glen Fierre Sitohang, Bramudya Toguando Sudibyo, Usman Trisnapradika, Gustina Alfa Wibawa, Tangkas Surya Wibawa, Vincentius Hadianta Zulfiningrum, Rahmawati