Klasifikasi Penyakit Tanaman Mangga Menggunakan Citra Daun Dengan Pendekatan Transfer Learning Efficientnet-B0
DOI:
https://doi.org/10.47065/jimat.v6i1.904Keywords:
Deep Learning; EfficientNet-B0; Disease Classification; Mango Leaves; Confusion MatrixAbstract
Mango plant disease is one of the factors that can reduce the quality and productivity of the harvest. Manual identification of mango leaf diseases still relies on visual observation, potentially requiring a long time and producing inconsistent diagnoses. This study aims to develop a mango plant disease classification system based on leaf images using a transfer learning approach with the EfficientNet-B0 architecture. The dataset used consists of eight classes, namely seven types of mango leaf diseases and one class of healthy leaves. EfficientNet-B0 is used as a feature extractor with pre-trained weights from ImageNet, then custom layers are added in the form of Batch Normalization, Dense, and Dropout to adjust to classification needs. The training process was carried out for 10 epochs by dividing the data into training, validation, and test data. The test results show that the model achieves maximum performance on the dataset used, indicated by very high accuracy, precision, recall, and f1-score values ??across all classes. Visualization of individual image predictions also shows a prediction confidence level of 0.91 for one of the disease classes. While these results demonstrate the potential of EfficientNet-B0 in mango leaf disease classification, the very high performance achieved in a limited number of epochs indicates the need for further evaluation of the model's generalization capabilities.
Downloads
References
E. A. Apriadi et al., KECERDASAN BUATAN Teori, Implementasi, dan Aplikasi di Era Digital. Eko Aziz Apriadi, 2025.
M. Munadi, “Penerapan Algoritma Deep Learning untuk Deteksi Dini Penyakit dari Citra Medis,” Journal of Computer Science and Information Technology, vol. 1, no. 2, pp. 53–59, 2025, doi: 10.70716/jocsit.v1i2.259.
H. Listiani, S. N. Asia, S. Sepriano, and L. Judijanto, Deep Learning: Konsep, Arsitektur, dan Implementasi. PT. Sonpedia Publishing Indonesia, 2025.
R. M. Ardiyanti, N. Maryana, and P. Pudjianto, “Keanekaragaman lalat buah (Diptera: Tephritidae) dan parasitoidnya di Taman Buah Mekarsari, Cileungsi, Bogor,” J. Entomol. Indones., vol. 16, no. 2, p. 65, 2019, doi: 10.5994/jei.16.2.65.
R. C. Simangunsong and M. Muhathir, “MobileNetV2 Analysis in Disease Classification on Mango Leaves,” Journal of Informatics and Telecommunication Engineering, vol. 8, no. 3, pp. 73–80, 2025, doi: 10.31289/jite.v8i3Spc.14430.
P. D. Rinanda, D. N. Aini, T. A. Pertiwi, S. Suryani, and A. J. Prakash, “Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, pp. 56–61, Feb. 2024, doi: 10.57152/predatecs.v1i2.872.
M. Rizal, V. J. Saputra, D. I. Maulana, F. Budiman, and E. Sugiarto, “Deteksi awal penyakit tanaman berbasis citra daun: studi perbandingan algoritme SVM dan CNN,” IRCS: Integrative Research in Computer Science, vol. 1, no. 1, pp. 61–76, 2025, [Online]. Available: https://ritecs.org/journal/index.php/IRCS/article/view/7
W. M. Ardana and K. Kusrini, “Optimasi Algoritma Convolutional Neural Network dengan Arsitektur Efficientnet-B0 dan Resnet-50 untuk Klasifikasi Jenis Sampah,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. October, pp. 1274–1286, 2025, doi: 10.57152/malcom.v5i4.2030.
I. Pacal et al., “A systematic review of deep learning techniques for plant diseases,” Artif. Intell. Rev., vol. 57, no. 11, p. 304, 2024, doi: 10.1007/s10462-024-10944-7.
V. R. Saragih, N. Azizi, A. Atalarais, R. A. Hatmi, and H. Syahputra, “Detection of mango leaf disease using the convolution neural network method,” TEKNOSAINS?: Jurnal Sains, Teknologi dan Informatika, vol. 11, no. 1, Jan. 2024, doi: 10.37373/tekno.v11i1.639.
A. B. Faqih and D. Avianto, “Identifikasi Penyakit Daun Pada Tanaman Solanaceae Dan Rosaceae Menggunakan Deep Learning,” Jurnal Teknologi Terpadu, vol. 10, no. 2, pp. 105–116, 2024, doi: 10.54914/jtt.v10i2.1440.
N. Sulaiya and S. Banerjee, “A Review on Plant Leaf Disease Detection Using CNN Deep Learning Models,” International Journal of Advanced Research and Multidisciplinary Trends (IJARMT), vol. 2, no. 1, pp. 642–653, 2025, [Online]. Available: https://www.ijarmt.com/index.php/j/article/view/260/177
A. J. Rozaqi, A. Sunyoto, and M. rudyanto Arief, “Deteksi Penyakit pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network,” Creative Information Technology Journal, vol. 8, no. 1, pp. 22–31, 2021, doi: 10.24076/citec.2021v8i1.263.
F. Marpaung, F. Aulia, and R. C. Nabila, “Computer Vision Dan Pengolahan Citra Digital,” 2022, PUSTAKA AKSARA.
H. A. Faiq and H. Sabita, “Pengembangan Model Deep Learning Untuk Pengenalan Wajah pada Sistem Keamanan,” Jurnal TEKNIKA, vol. 18, no. 1, pp. 197–209, 2024, doi: 10.5281/zenodo.10881284.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM), vol. 3, no. 1, pp. 46–56, 2024, doi: 10.58602/jima-ilkom.v3i1.26.
W. R. Perdani, R. Magdalena, and N. K. C. Pratiwi, “Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, no. 2, pp. 322–333, 2022, doi: 10.26760/elkomika.v10i2.322.
K. Kristiawan and A. Widjaja, “Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 1, pp. 35–46, 2021, doi: 10.28932/jutisi.v7i1.3182.
F. R. Valerian, M. Syarief, and D. A. Fatah, “Klasifikasi Tingkat Obesitas Menggunakan Metode Gbm Dan Confusion Matrix,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 2, pp. 2242–2249, 2025, doi: 10.36040/jati.v9i2.13062.
R. Swastika, S. Mukodimah, F. Susanto, M. Muslihudin, and S. I. P. Adab, IMPLEMENTASI DATA MINING (Clastering, Association, Prediction, Estimation, Classification). Penerbit Adab, 2023.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Penyakit Tanaman Mangga Menggunakan Citra Daun Dengan Pendekatan Transfer Learning Efficientnet-B0
ARTICLE HISTORY
Issue
Section
Copyright (c) 2026 Muhammad Raihan, Cindi Wulandari, Rusdiyanto Rusdiyanto, Rudi Kurniawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
