Hyperparameter Tuning Dalam Deteksi Penyakit Daun Jeruk Dengan Pendekatan Transfer Learning
DOI:
https://doi.org/10.47065/jieee.v4i3.2354Keywords:
Leaf Disease Detection; Mobilenetv2; Transfer Learning; Hyperparameter Tuning; CNNAbstract
Diseases affecting citrus leaves, such as CVPD, black spot, and canker, pose a serious threat to the sustainability of citrus production, especially in the Malang region. This study aims to develop an orange leaf disease detection system based on images using the MobileNetV2 architecture with a transfer learning approach. Optimization was performed through hyperparameter tuning techniques, including batch size, learning rate, regularization, and optimizer settings, to improve the model's performance in classifying leaf images into five disease classes: black spot, canker, greening, melonose, and healthy. The dataset used was sourced from Kaggle and processed with augmentation and normalization to improve model generalization. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the model could classify diseases with high accuracy, supporting early detection and helping farmers maintain plant health sustainably. This approach not only provides AI-based technological solutions in agriculture but also supports environmentally friendly and efficient farming practices. The image data is divided into four classes: black spot (169 images), canker leaves (163 images), healthy leaves (58 images), and yellowing leaves (204 images). The total dataset comprises 594 images.
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References
"S. A. Mardiani", “Analisis Keberlanjutan Budidaya Jeruk Malang Melalui Pendekatan Mds (Multidimensional Scaling),” Foliumjurnal Ilmu Pertan., Vol. 7, No. 1, Pp. 13–28, 2023, Doi: 10.33474/Folium.V7i1.19179.
And H. P. H. R. Riswandi, R. Jamiah, N. Mardhatillah, “‘Klasifikasi Penyakit Pada Citra Daun Jeruk Menggunakan Arsitektur Mobilenet Berbasis Mobile Platform,’” J. Fokus Elektroda Energi List. Telekomun. Komputer, Elektron. Dan Kendali, Vol. 6, No. 4, P. 212, 2021, Doi: Doi:10.33772/Jfe.V6i4.19113.
C. Claudia And J. Edwin And J. Edwin, “‘Pemanfaatan Cnn Dalam Deteksi Penyakit Tanaman Berbasis Citra,’” Wacana J. Ilm. Ilmu Komun., Vol. 21, No. 2, Pp. 178–195, 2022, Doi: Doi:10.32509/Wacana.V21i2.2000.
I. Maulana, N. Khairunisa, And R. Mufidah, “Deteksi Bentuk Wajah Menggunakan Convolutional Neural Network (Cnn),” Jati (Jurnal Mhs. Tek. Inform., Vol. 7, No. 6, Pp. 3348–3355, 2024, Doi: 10.36040/Jati.V7i6.8171.
C. Shorten And T. M. Khoshgoftaar, “‘A Survey On Image Data Augmentation For Deep Learning,’” J. Big Data, Vol. 6, No. 1, 2019, Doi: Doi:10.1186/S40537-019-0197-0.
And F. F. R. C. Sigitta, R. H. Saputra, “‘Deteksi Penyakit Tomat Melalui Citra Daun Menggunakan Metode Convolutional Neural Network,’” " Avitec (Aviation Electron. Inf. Technol. Telecommun. Electr. Control., Vol. 5, No. 1, P. 43, 2023, Doi: 10.28989/Avitec.V5i1.1404.
M. Amin And A. Bindas, “‘Pengklasifikasi Bibit Kelapa Menggunakan Algoritma Deep Learning Convolutional Neural Network,’” J. Perangkat Lunak, Vol. 6, Pp. 405–413, 2024, Doi: Doi:10.32520/Jupel.V6i3.3654.
I. Rusydy, I. Canbulat, C. Zhang, C. Wei, And A. Mcquillan, “‘The Development And Implementation Of Design Flowchart For Probabilistic Rock Slope Stability Assessments: A Review,’” Geoenvironmental Disasters, Vol. 11, No. 1, 2024, Doi: 10.1186/S40677-024-00290-9.
And K. M. H. B. S. Acarya, A. Muhaimin, “‘Identifikasi Penyakit Daun Jeruk Siam Menggunakan Convolutional Neural Network (Cnn) Dengan Arsitektur Efficientnet,’” G-Tech J. Teknol. Terap., Vol. 8, No. 2, Pp. 1040–1048, 2024, Doi: 10.33379/Gtech.V8i2.4120.
L. A. Susanto, “‘Pemilihan Hyperparameter Pada Alexnet Cnn Untuk Klasifikasi Citra Penyakit Kedelai,’” Indexia Informatics Comput. Intell. Journal, Vol. 5, No. 2, Pp. 113–122, 2023, Doi: Doi:10.30587/Indexia.V5i02.5508.
M. F. Aditama And M. Haryanti, “‘Sistem Pengenalan Dan Verifikasi Wajah Menggunakan Transfer Learning Berbasis Raspberry Pi,’” J. Teknol. Ind., Vol. 12, No. 1, Pp. 30–39, 2023, Doi: Doi:10.35968/Jti.V12i1.1045.
M. D. Pranatha, M. A. Maricar, And G. H. Setiawan, “‘Implementasi Arsitektural Resnet-34 Dalam Klasifikasi Gambar Penyakit Pada Daun Kentang,’” J. Teknol. Dan Sist. Inf. Bisnis, Vol. 6, No. 3, Pp. 575–580, 2024, Doi: 10.47233/Jteksis.V6i3.1431.
A. Hosna, E. Merry, J. Gyalmo, Z. Alom, Z. Aung, And M. A. Azim, “‘Transfer Learning: A Friendly Introduction,’” J. Big Data, Vol. 9, No. 1, 2022, Doi: 10.1186/S40537-022-00652-W.
N. Putri Arafa, S. Rahma Basri, R. Ratnasari, And R. Adi Saputra, “Klasifikasi Penyakit Pada Daun Tanaman Cabai Dengan Pendekatan Artificial Neural Network (Ann),” Jati (Jurnal Mhs. Tek. Inform., Vol. 8, No. 6, Pp. 12865–12871, 2024, Doi: Doi:10.36040/Jati.V8i6.12140.
M. G. Somoal And A. R. Dzikrillah, “Komparasi Mobilenetv2 Dengan Kustomisasi Transfer Learning Dan Hyperparameter Untuk Identifikasi Tumor Otak,” J. Teknol. Inf. Dan Ilmu Komput., Vol. 12, No. 1, Pp. 229–240, 2025, Doi: 10.25126/Jtiik.2025129582.
C. Claudia And J. Edwin, “Motivasi Membagikan Konten Tanaman Hias Di Social Networking Sites,” Wacana J. Ilm. Ilmu Komun., Vol. 21, No. 2, Pp. 178–195, 2022, Doi: 10.32509/Wacana.V21i2.2000.
S. W. P. Listio, “Performance Of Deep Learning Inception Model And Mobilenet Model On Gender Prediction Through Eye Image,” Sinkron, Vol. 7, No. 4, Pp. 2593–2601, 2022, Doi: 10.33395/Sinkron.V7i4.11887.
D. Lizard Sambawo Dimara, B. Rahmat, And H. Maulana, “Identifikasi Penyakit Daun Padi Dengan Metode Transfer Learning Mobilenet - Support Vector Machine,” Jati (Jurnal Mhs. Tek. Inform., Vol. 9, No. 3, Pp. 5413–5420, 2025, Doi: 10.36040/Jati.V9i3.14245.
J. I. Komputasi, V. No, M. Ssd, V. Mobilenet, And S. Model, “Pembuatan Aplikasi Deteksi Objek Menggunakan Tensorflow Object Detection Api Dengan Memanfaatkan Ssd Mobilenet V2 Sebagai Model Pra - Terlatih,” J. Ilm. Komputasi, Vol. 19, No. 3, Pp. 421–430, 2020, Doi: 10.32409/Jikstik.19.3.68.
F. Zaelani And Y. Miftahuddin, “Perbandingan Metode Efficientnetb3 Dan Mobilenetv2 Untuk Identifikasi Jenis Buah-Buahan Menggunakan Fitur Daun,” J. Ilm. Teknol. Infomasi Terap., Vol. 9, No. 1, Pp. 1–11, 2022, Doi: 10.33197/Jitter.Vol9.Iss1.2022.911.
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