Klasifikasi Jenis Daun Tanaman Herbal Menggunakan Metode Convolutional Neural Network Dengan Arsitektur AlexNet
DOI:
https://doi.org/10.54895/intech.v6i2.3275Keywords:
AlexNet, Convolutional Neural Network, Herbal Leaves, Image Classification, Deep LearningAbstract
Indonesia has rich biodiversity, including various types of herbal plants commonly used in traditional medicine. However, manual identification and classification of herbal leaves remain challenging due to similarities in shape, color, and texture between species. This study aims to develop an automatic classification system for herbal leaf types using the Convolutional Neural Network (CNN) method with the AlexNet architecture. The dataset used in this research was obtained from a public Kaggle repository and consists of ten classes of herbal leaves, namely guava, curry, basil, turmeric, mint, papaya, betel, soursop, aloe vera, and green tea. Image preprocessing includes resizing to 224×224 pixels, normalization, and data augmentation to improve model generalization. The model was trained and tested using an 80:20 data split with several experimental configurations. The best performance was achieved at a learning rate of 0.0001, batch size of 32, and 25 epochs, resulting in a training accuracy of 88.25% and a testing accuracy of 73.50%. These results show that AlexNet can effectively extract visual features from herbal leaf images and perform accurate classification. This study demonstrates that CNN-based classification is an efficient approach for recognizing herbal plants automatically.Downloads
References
A. M. Atha and E. Zuliarso, “Deteksi Tanaman Herbal Khusus Untuk Penyakit Kulit Dan Penyakit Rambut Menggunakan Convolutional Neural Network (CNN) Dan Tensorflow,” JUPITER, vol. 14, no. Oktober, pp. 1–10, 2022.
Susilo Yulianto, “Penggunaan Tanaman Herbal Untuk Kesehatan,” J. Kebidanan Dan Kesehat. Tradis., vol. 2, no. 1, pp. 1–59, 2017.
C. Du and S. Gao, “Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network,” IEEE Access, vol. 5, pp. 15750–15761, 2017, doi: 10.1109/ACCESS.2017.2735019.
Josua Nainggolan, Debi Yandra Niska, F. Marpaung, Insan Taufik, and K. S. S, “Palm Fruit Ripeness Detection System Using Convolutional Neural Network (CNN) Algorithm,” J. Artif. Intell. Eng. Appl., vol. 4, no. 3, pp. 1700–1705, 2025, doi: 10.59934/jaiea.v4i3.989.
D. P. Adriani et al., “Klasifikasi Tanaman Obat-Obatan Berdasarkan Citra Daun Dengan Menggunakan Jaringan Syaraf Tiruan,” vol. 6, no. 3, pp. 39–47, 2020.
K. Usman, N. C. P. Kumalasari, N. Ibrahim, H. Syahrian, and V. R. Puspitasari, “Evaluasi Optimizer pada Residual Network untuk Klasifikasi Klon Teh Seri GMB Berbasis Citra Daun,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 9, no. 4, p. 841, 2021, doi: 10.26760/elkomika.v9i4.841.
A. Latifah, D. Tresnawati, and H. Sanjaya, “Media Pembelajaran Menggunakan Teknologi Augmented Reality untuk Tanaman Daun Herbal,” J. Algoritm., vol. 19, no. 2, pp. 515–526, 2022, doi: 10.33364/algoritma/v.19-2.1138.
A. ANHAR and R. A. PUTRA, “Perancangan dan Implementasi Self-Checkout System
pada Toko Ritel menggunakan Convolutional Neural Network (CNN),” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 11, no. 2, p. 466, 2023, doi: 10.26760/elkomika.v11i2.466.
F. A. Danendra, F. T. Anggraeny, and H. Maulana, “Klasifikasi Citra Penyakit Daun Cabai Rawit Dengan Menggunakan CNN Arsitektur AlexNet dan SqueezeNet,” Syntax J. Inform., vol. 12, no. 01, pp. 50–61, 2023.
M. E. Al Rivan and A. Setiawan, “Pengenalan Gestur Angka Pada Tangan Menggunakan Arsitektur AlexNet Dan LeNet Pada Metode Convolutional Neural Network,” Komputika J. Sist. Komput., vol. 11, no. 1, pp. 19–28, 2022, doi: 10.34010/komputika.v11i1.5176.
M. E. Al Rivan and A. G. Riyadi, “Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language,” J. Komput. Terap., vol. 7, no. 1, pp. 53–61, 2021, doi: 10.35143/jkt.v7i1.4489.
Q. N. Azizah, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network AlexNet,” sudo J. Tek. Inform., vol. 2, no. 1, pp. 28–33, 2023, doi: 10.56211/sudo.v2i1.227.
K. Saputra S, D. Niska, I. Taufik, M. Hidayat, and D. Dharma, “Classification of Herbal Plants Based on Leaf Images using Convolutional Neural Network,” 2022, doi: 10.4108/eai.11-10-2022.2325271.
M. Samel, A. Tiwari, and P. S. Parvatikar, “Leaf Recognition using Convolution Neural Network - AlexNet,” Int. J. Eng. Res. Technol., vol. 10, no. 01, pp. 316–320, 2021.
D. Irfansyah, M. Mustikasari, and A. Suroso, “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” J. Inform. J. Pengemb. IT, vol. 6, no. 2, pp. 87–92, 2021, doi: 10.30591/jpit.v6i2.2802.
M. Kevin Santosa, M. Hanindia Prami Swari, and A. Nugroho Sihananto, “Implementasi Arsitektur Alexnet Dan Resnet34 Pada Klasifikasi Citra Penyakit Daun Kentang Menggunkan Transfer Learning,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 5, pp. 3293–3301, 2024, doi: 10.36040/jati.v7i5.7337.
B. Setiyono et al., “Identifikasi Tanaman Obat Indonesia Melalui Citra Daun Menggunakan Metode Convolutional Neural Network (CNN),” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, pp. 385–392, 2023, doi: 10.25126/jtiik.20236809.
S. Adiningsi and R. A. Saputra, “Identification of Medicinal Plant Leaf Types Using the Convolutional Neural Network (CNN) Method with the VGG16 Model,” J. Inform. Polinema, vol. 9, no. 4, pp. 451–460, 2023.
A. Saputro, S. Mu’min, Moch. Lutfi, and H. Putri, “Deep Transfer Learning Dengan Model Arsitektur Vgg16 Untuk Klasifikasi Jenis Varietas Tanaman Lengkeng Berdasarkan Citra Daun,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 609–614, 2022, doi: 10.36040/jati.v6i2.5456.
I. A. M. Zin, Z. Ibrahim, D. Isa, S. Aliman, N. Sabri, and N. N. A. Mangshor, “Herbal plant recognition using deep convolutional neural network,” Bull. Electr. Eng. Informatics, vol. 9, no. 5, pp. 2198–2205, 2020, doi: 10.11591/eei.v9i5.2250.
M. Micheni, R. Birithia, C. Mugambi, B. Too, and M. Kinyua, “Identification of Maize Leaf Diseases Using Support Vector Machine and Convolutional Neural Networks AlexNet and ResNet50,” Indones. J. Comput. Sci., vol. 12, no. 1, pp. 1518–1530, 2023.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. (NeurIPS / NIPS), pp. 1–9, 2012, doi: https://doi.org/10.48550/arXiv.1404.5997.
I. P. Putra, Rusbandi, and D. Alamsyah, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network AlexNet,” sudo J. Tek. Inform., vol. 2, no. 1, pp. 28–33, 2023, doi: 10.56211/sudo.v2i1.227.
A. Nugroho and Y. Religia, “Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging,” J. RESTI, vol. 5, no. 3, pp. 504–510, 2021, doi: 10.29207/resti.v5i3.3067.
A. M. Atha and E. Zuliarso, “Dataset Tanaman Herbal”, [Online]. Available: https://www.kaggle.com/datasets/anefiamutiaraatha/dataset-tanaman- herbal?resource=download
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 Dicko, Muhammad Ezar Al Rivan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
