Klasifikasi Daun Jahe dan Kunyit Menggunakan CNN MobileNetV2
DOI:
https://doi.org/10.29407/m8vd1w22Abstract
Identifikasi tanaman jahe dan kunyit berdasarkan daun masih dapat menimbulkan kesalahan karena keduanya memiliki kemiripan bentuk, warna, dan tekstur daun. Penelitian ini bertujuan mengembangkan model klasifikasi daun jahe dan kunyit menggunakan Convolutional Neural Network dengan arsitektur MobileNetV2. Dataset yang digunakan terdiri dari dua kelas, yaitu daun jahe dan daun kunyit, dengan masing-masing kelas berjumlah 500 citra. Tahapan penelitian meliputi pengumpulan dataset, preprocessing, augmentasi citra, pelatihan model, pengujian, dan evaluasi menggunakan accuracy, precision, recall, dan F1-score. Pengujian dilakukan melalui tiga skenario berdasarkan jumlah epoch, yaitu 10 epoch, 15 epoch, dan 25 epoch. Hasil terbaik diperoleh pada skenario kedua dengan 15 epoch, yaitu precision sebesar 0,97, recall sebesar 0,97, F1-score sebesar 0,97, dan accuracy sebesar 0,97. Hasil ini menunjukkan bahwa MobileNetV2 mampu mengklasifikasikan daun jahe dan kunyit dengan baik.
Keywords:
CNN, jahe, klasifikasi citra, kunyit, MobileNetV2##plugins.themes.default.displayStats.downloads##
References
[1] S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, ―Identification of
Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,‖ Electronics, vol. 10,
no. 12, p. 1388, 2021, doi: 10.3390/electronics10121388.
[2] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, ―MobileNetV2:
Inverted residuals and Linear Bottlenecks,‖ in Proceedings of the IEEE Conference on
Computer vision and Pattern Recognition, 2018, pp. 4510–4520, doi:
10.1109/CVPR.2018.00474.
[3] B. Dey, J. Ferdous, R. Ahmed, and J. Hossain, ―Assessing Deep Convolutional Neural
Network Models and Their Comparative Performance for Automated Medicinal Plant Identification from Leaf Images,‖ Heliyon, vol. 10, no. 1, p. e23655, 2024, doi:
10.1016/j.heliyon.2023.e23655.
[4] M. S. I. Musyaffa, N. Yudistira, M. A. Rahman, A. H. Basori, A. B. F. Mansur, and J.
Batoro, ―IndoHerb: Indonesia Medicinal Plants Recognition Using Transfer Learning
and Deep learning,‖ Heliyon, vol. 10, no. 23, p. e40606, 2024, doi:
10.1016/j.heliyon.2024.e40606.
[5] S. Salsabila, A. Suharso, and P. Purwantoro, ―Comparison of Deep learning
Architectures in Identifying Types of Medicinal Plant Leaf Images,‖ Journal of Applied
Informatics and Computing, vol. 8, no. 1, pp. 39–46, 2024, doi: 10.30871/jaic.v8i1.6289.
[6] L. Falaschetti, L. Manoni, D. Di Leo, D. Pau, V. Tomaselli, and C. Turchetti, ―A CNNBased Image Detector for Plant Leaf Diseases Classification,‖ HardwareX, vol. 12, p.
e00363, 2022, doi: 10.1016/j.ohx.2022.e00363.
[7] A. S. Paymode and V. B. Malode, ―Transfer Learning for Multi-Crop Leaf Disease
Image Classification Using Convolutional Neural Network VGG,‖ Artificial
Intelligence in Agriculture, vol. 6, pp. 23–33, 2022, doi: 10.1016/j.aiia.2021.12.002.
[8] G. S. Hukkeri, B. C. Soundarya, H. L. Gururaj, and V. Ravi, ―Classification of Various
Plant Leaf Disease Using Pretrained Convolutional Neural Network on ImageNet,‖ The
Open Agriculture Journal, vol. 18, p. e18743315305194, 2024, doi:
10.2174/0118743315305194240408034912.
[9] W. B. Demilie, ―Plant Disease Detection and Classification Techniques: A Comparative
Study of the Performances,‖ Journal of Big Data, vol. 11, no. 1, p. 5, 2024, doi:
10.1186/s40537-023-00863-9.
[10] H. Wang, S. Qiu, H. Ye, and X. Liao, ―A Plant Disease Classification Algorithm Based
on Attention MobileNet V2,‖ Algorithms, vol. 16, no. 9, p. 442, 2023, doi:
10.3390/a16090442.
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