IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR CLASSIFICATION OF PATCHOULI LEAF IMAGES BASED ON MODEL ACCURACY AND EVALUATION
Keywords:
Accuracy, Convolutional Neural Network, Image Classification, Model Evaluation, Patchouli LeavesAbstract
Patchouli (Nilam leaves) holds significant potential as a high-value natural material, especially in the perfume and essential oil industries. However, the classification and quality analysis of patchouli leaves remain a challenge that requires an automated solution based on technology. This study aims to develop a Convolutional Neural Network (CNN) model capable of automatically classifying the condition of patchouli leaves. The image data of patchouli leaves were processed through several preprocessing stages and divided into training and testing data. The designed CNN model utilizes several convolutional layers, pooling, dropout, and dense layers for the training process. The evaluation results using the confusion matrix showed that the model had a very low error rate, with only 1 misprediction in the training data. For the testing data, the model achieved an accuracy of 85% with a loss value of 0.6191496. The model also demonstrated an accuracy of 98.75% with a loss of 0.443462 on the training data. However, improvements in model generalization are still needed to achieve more consistent performance on new data.
Downloads
References
S. K. Pandey dkk., “A Comparative Study on Chemical Composition, Pharmacological Potential and Toxicity of Pogostemon cablin Linn., (Patchouli) Flower and Leaf Essential Oil,” J. Essent. Oil-Bearing Plants, vol. 25, no. 1, hal. 160–179, 2022, doi: 10.1080/0972060X.2021.2013325.
J. Heroweti, M. F. Rochman, D. N. Wibowo, I. R. Khasanah, dan S. Salma, “Efektifitas Penyembuhan Luka Sayat Spray Gel Minyak Nilam Pada Kelinci (Oryctolagus cuniculus),” Media Farm., vol. 18, no. 1, hal. 10–15, 2022, doi: 10.32382/mf.v18i1.2397.
N. Kasim, M. B. Fadilah, W. Al Hidayat, dan R. A. Saputra, “Klasifikasi Jenis Tanaman Herbal Berdasarkan Citra Menggunakan Metode Convolution Neural Network ( CNN ),” J. Tekno Kompak, vol. 19, no. 1, hal. 64–78, 2024, doi: 10.33365/jtk.v19i1.4536.
L. Wang, Y. Liu, X. Zhan, D. Luo, dan X. Sun, “Correction: Photochromic transparent wood for photo-switchable smart window applications (Journal of Materials Chemistry C (2019) 7 (8649-8654) DOI: 10.1039/C9TC02076D),” J. Mater. Chem. C, vol. 7, no. 48, hal. 8649–8654, 2019, doi: 10.1039/c9tc90235j.
A. F. Cobantoro, F. Masykur, dan K. Sussolaikah, “Erformance Analysis of Alexnet Convolutional Neural Network (CNN) Architecture With Image Objects of Rice Plant Leaves,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 8, no. 2, hal. 111–116, 2023, doi: 10.33480/jitk.v8i2.4060.
I. N. Purnama, “Herbal Plant Detection Based on Leaves Image Using Convolutional Neural Network With Mobile Net Architecture,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 6, no. 1, hal. 27–32, 2020, doi: 10.33480/jitk.v6i1.1400.
R. A. Saputra, S. Wasiyanti, A. Supriyatna, dan D. F. Saefudin, “Penerapan Algoritma Convolutional Neural Network Dan Arsitektur MobileNet Pada Aplikasi Deteksi Penyakit Daun Padi,” Swabumi, vol. 9, no. 2, hal. 184–188, 2021, doi: 10.31294/swabumi.v9i2.11678.
A. Julianto, A. Sunyoto, dan F. W. Wibowo, “Optimasi Hyperparameter Convolutional Neural Network Untuk Klasifikasi Penyakit Tanaman Padi,” Tek. Teknol. Inf. dan Multimed., vol. 3, no. 2, hal. 98–105, 2022, doi: 10.46764/teknimedia.v3i2.77.
S. Sheila, M. K. Anwar, A. B. Saputra, F. R. Pujianto, dan I. P. Sari, “Deteksi Penyakit pada Daun Padi Berbasis Pengolahan Citra Menggunakan Metode Convolutional Neural Network (CNN),” J. Multinetics, vol. 9, no. 1, hal. 27–34, 2023, doi: 10.32722/multinetics.v9i1.5255.
B. Setiyono dkk., “Identifikasi Tanaman Obat Indonesia Melalui Citra Daun Menggunakan Metode Convolutional Neural Network (CNN),” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, hal. 385–392, 2023, doi: 10.25126/jtiik.20231026809.
U. Kulsum dan A. Cherid, “Penerapan Convolutional Neural Network Pada Klasifikasi Tanaman Menggunakan ResNet50,” Simkom, vol. 8, no. 2, hal. 221–228, 2023, doi: 10.51717/simkom.v8i2.191.
N. L. Marpaung, R. J. H. Butar Butar, dan S. Hutabarat, “Implementasi Deep learning untuk Identifikasi Daun Tanaman Obat Menggunakan Metode Transfer learning,” J. Edukasi dan Penelit. Inform., vol. 9, no. 3, hal. 348, 2023, doi: 10.26418/jp.v9i3.63895.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Arif Rahman Hakim, Dewi Marini Umi Atmaja

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.