Herbal Plant Classification Using EfficientNetV2B0 Model and CRISP-DM Approach
DOI:
https://doi.org/10.52436/1.jutif.2025.6.4.5141Keywords:
Classification, EfficientNetV2B0, Herbal PlantsAbstract
Herbal remedies have long been utilized by Indonesian communities as part of traditional medicine. However, identification of these natural resources is often challenging due to the morphological similarities among various species, which demand expert knowledge to differentiate. This study aims to implement the EfficientNetV2B0 model architecture for classifying medicinal leaves through an Android-based application designed to support recognition tasks. The dataset was composed of augmented images of plant foliage. The model was trained using the TensorFlow framework and evaluated to measure classification performance. Results demonstrate that EfficientNetV2B0 achieves excellent accuracy, with validation scores exceeding 97%, outperforming several other deep learning models. The resulting application allows the general public to identify local medicinal species more easily. This study contributes to the field of computer vision by providing an accurate and efficient classification framework, particularly beneficial for health-related informatics in biodiversity-rich regions.
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References
B. A. Suliasih and A. Mun’im, “Review : potensi dan masalah dalam pengembangan kemandirian bahan baku obat tradisional di Indonesia,” Chem. Mater., vol. 1, no. 1, pp. 28–33, 2022.
A. Setiawan, “Keanekaragaman Hayati Indonesia: Masalah dan Upaya Konservasinya,” Indones. J. Conserv., vol. 11, no. 1, pp. 13–21, 2022, doi: 10.15294/ijc.v11i1.34532.
S. Yulianto, “Penggunaan Tanaman Herbal Untuk Kesehatan,” J. Kebidanan dan Kesehat. Tradis., vol. 2, no. 1, pp. 1–7, 2017, doi: 10.37341/jkkt.v2i1.37.
L. Kristiana, A. Paramita, H. Maryani, and P. Andarwati, “Exploration of Indonesian Medicinal Plants Supporting Physical Fitness: Analysis of Research on Medicinal Plants and Herbs 2012, 2015, and 2017,” J. Kefarmasian Indones., vol. 12, no. 1, pp. 79–89, 2022.
et al Grenvilco DO, “Vol. 16 No. 3 / Juli - September 2023,” Pemanfaat. Tanam. Herb. Sebagai Obat Tradis. Untuk Kesehat. Masy. Di Desa Guaan Kec. Mooat Kabupaten Bolaang Mongondow Timur, vol. 16, no. 3, pp. 1–20, 2023.
N. Sari, sudewi, fenny hasanah, salma Handayani, and putra penyabar Gulo, “Pengenalan Dan Pemanfaatan Tanaman Herbal DalamPengobatan Tradisional Anak Di Kelurahan MusamPembangunan,” J. Pengabdi. Masy. Tjut Nyak Dhien, vol. 4, no. 1, pp. 68–73, 2025.
P. Maji, D. Ghosh Dhar, P. Misra, and P. Dhar, “Costus speciosus (Koen ex. Retz.) Sm.: Current status and future industrial prospects,” Ind. Crops Prod., vol. 152, no. March, p. 112571, 2020, doi: 10.1016/j.indcrop.2020.112571.
R. P. S. Putra, C. S. K. Aditya, and G. W. Wicaksono, “Herbal Leaf Classification Using Deep Learning Model Efficientnetv2B0,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 9, no. 2, pp. 301–307, 2024, doi: 10.33480/jitk.v9i2.5119.
Bella Dwi Mardiana, Wahyu Budi Utomo, Ulfah Nur Oktaviana, Galih Wasis Wicaksono, and Agus Eko Minarno, “Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 1, pp. 20–26, 2023, doi: 10.29207/resti.v7i1.4550.
J. Liu, M. Wang, L. Bao, and X. Li, “EfficientNet based recognition of maize diseases by leaf image classification,” J. Phys. Conf. Ser., vol. 1693, no. 1, 2020, doi: 10.1088/1742-6596/1693/1/012148.
Q. Zheng, M. Yang, X. Tian, N. Jiang, and D. Wang, “A full stage data augmentation method in deep convolutional neural network for natural image classification,” Discret. Dyn. Nat. Soc., vol. 2020, 2020, doi: 10.1155/2020/4706576.
M. A. Hasanah, S. Soim, and A. S. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 103–108, 2021, doi: 10.30871/jaic.v5i2.3200.
A. Rianti, N. W. A. Majid, and A. Fauzi, “CRISP-DM: Metodologi Proyek Data Science,” Pros. Semin. Nas. Teknol. Inf. dan Bisnis, pp. 107–114, 2023, [Online]. Available: https://ojs.udb.ac.id/index.php/Senatib/article/view/3015
D. Jabi and R. Y. Hayuningtyas, “Sistem Informasi Penggajian Karyawan Berbasis Website Pada Sekolah Tunas Bangsa Greenville,” Reputasi J. Rekayasa Perangkat Lunak, vol. 3, no. 2, pp. 31–36, 2022, [Online]. Available: http://103.75.24.116/index.php/reputasi/article/view/1601
O. Rochmawanti, F. Utaminingrum, F. A. Bachtiar, F. Ilmu, K. Universitas, and P. Korespondensi, “Analisis Performa Pre-Trained Model Convolutional Neural Performance Analysis of Pre-Trained Convolutional Neural,” vol. 8, no. 4, pp. 805–814, 2021, doi: 10.25126/jtiik.202184441.
A. R. Adawiyah, D. Muriyatmoko, A. Musthafa, and T. Harmini, “KLASIFIKASI JUDUL DAN TATALETAK BUKU DENGAN ALGORITMA CNN DI PERPUSTAKAAN UNIDA GONTOR,” vol. 4, no. 2, pp. 96–104, 2024.
N. IBRAHIM et al., “Klasifikasi Tingkat Kematangan Pucuk Daun Teh menggunakan Metode Convolutional Neural Network,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 1, p. 162, 2022, doi: 10.26760/elkomika.v10i1.162.
H. Hassan et al., “Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks,” Comput. Biol. Med., vol. 141, no. September 2021, p. 105123, 2022, doi: 10.1016/j.compbiomed.2021.105123.
Afis Julianto, Andi Sunyoto, and Ferry Wahyu Wibowo, “Optimasi Hyperparameter Convolutional Neural Network Untuk Klasifikasi Penyakit Tanaman Padi,” Tek. Teknol. Inf. dan Multimed., vol. 3, no. 2, pp. 98–105, 2022, doi: 10.46764/teknimedia.v3i2.77.
M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
M. Rafif, D. U. Kusumaning Putri, and L. Awaludin, “Penggunaan Pre-trained Model untuk Klasifikasi Kualitas Sekrup,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 12, no. 2, p. 133, 2022, doi: 10.22146/ijeis.78112.
R. Tobiasz, G. Wilczynski, P. Graszka, N. Czechowski, and S. Luczak, “Edge Devices Inference Performance Comparison,” J. Comput. Sci. Eng., vol. 17, no. 2, pp. 51–59, 2023, doi: 10.5626/JCSE.2023.17.2.51.
L. Escobar, P. Gallardo, J. González-Anaya, J. L. González, G. Montúfar, and A. H. Morales, “Enumeration of max-pooling responses with generalized permutohedra,” pp. 1–32, 2022, [Online]. Available: http://arxiv.org/abs/2209.14978
S. De and S. L. Smith, “Batch normalization biases residual blocks towards the identity function in deep networks,” Adv. Neural Inf. Process. Syst., vol. 2020-Decem, no. NeurIPS, 2020.
A. Fauzi, B. Soerowirdjo, and E. Haryatmi, “Herbal plant leaves classification for traditional medicine using convolutional neural network,” IAES Int. J. Artif. Intell., vol. 13, no. 3, pp. 3322–3329, 2024, doi: 10.11591/ijai.v13.i3.pp3322-3329.
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Copyright (c) 2025 Anisya Sonita, Kurnia Anggriani, Arie Vatresia, Tiara Eka Putri, Yulia Darnita , Syakira Az Zahra, Vilda Aprilia, Dzakwan Ammar Aziz
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