Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm

Authors

  • Wilis Arum Karunia Program Studi Sistem Informasi, Universitas Islam Negeri Sunan Ampel, Surabaya Author
  • Safriya Murni Puspita Program Studi Sistem Informasi, Universitas Islam Negeri Sunan Ampel, Surabaya. Author
  • Dwi Rolliawati Program Studi Sistem Informasi, Universitas Islam Negeri Sunan Ampel, Surabaya. Author
  • Ahmad Yusuf Program Studi Sistem Informasi, Universitas Islam Negeri Sunan Ampel, Surabaya. Author

DOI:

https://doi.org/10.31294/p.v27i1.7577

Keywords:

Vegetable Classification, Convolutional Neural Network, TensorFlow, Keras, Deep Learning

Abstract

This study aims to classify vegetable types using the Convolutional Neural Network (CNN) algorithm with a dataset encompassing 15 vegetable classes and a total of 31,000 images. By utilizing the TensorFlow and Keras libraries, the CNN model was designed with convolutional, pooling, and dense layers to recognize visual features such as color, texture, and shape. The results indicate a highest validation accuracy of 95.83% and a testing accuracy of 93%. This research contributes to the application of the CNN algorithm for image classification and demonstrates its potential in handling multi-class datasets effectively. However, since the vegetables used have very distinct shapes and textures, this study is more relevant in the context of the technical application of the CNN algorithm rather than practical benefits. The research would be more impactful if applied to vegetables with similar shapes and characteristics, thereby supporting farmers or individuals studying vegetable traits in greater depth. Additionally, such an approach could address challenges in differentiating visually similar vegetable types, making the technology more valuable in real-world agricultural or educational settings.

Downloads

Download data is not yet available.

References

Aditya Dwi Putro Wicaksono ; Henri Tantyoko. (2023). JTIM : Jurnal Teknologi Informasi dan Multimedia Hybrid Algoritma Vgg16-Net dengan Support Vector Machine. 5(2), 56–65.

Ahmed, M. I., & Mamun, S. M. (2021). Vegetable Image Dataset. https://doi.org/10.34740/KAGGLE/DSV/2965251

Alkhatib, M. Q., Al-Saad, M., Aburaed, N., Almansoori, S., Zabalza, J., Marshall, S., & Al-Ahmad, H. (2023). Tri-CNN: A Three Branch Model for Hyperspectral Image Classification. Remote Sensing, 15(2), 1–19. https://doi.org/10.3390/rs15020316

Budiawan, R. S., & Hartono, B. (2023). Pengembangan Sistem Pendeteksi Jenis Sayuran dengan Metode CNN Berbasis Android. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 5(1), 62. https://doi.org/10.36499/jinrpl.v5i1.7833

Dhamayanti, R., Fatchiyatur Rohma, M., & Zahara, S. (2021). Penggunaan Deep Learning Dengan Metode Convolutional Neural Network Untuk Klasifikasi Kualitas Sayur Kol Berdasarkan Citra Fisik. SUBMIT: Jurnal Ilmiah Teknologi Informasi Dan Sains, 1 (1), 08–15.

Hasan, M. A., Riyanto, Y., & Riana, D. (2021). Grape leaf image disease classification using CNN-VGG16 model. Jurnal Teknologi Dan Sistem Komputer, 9(4), 218–223. https://doi.org/10.14710/jtsiskom.2021.14013

Malik, R. A., & Zuliarso, E. (2021). Metode Convolutional Neural Network Untuk Mendeteksi Jenis Sayur Menggunakan Tensorflow. Media Bina Ilmiah, 15(1978), 5873–5882. Retrieved from http://ejurnal.binawakya.or.id/index.php/MBI/article/view/1147

Maulana, N., Fauzan, M., Kholiq, R., A, M. D. H., & F, G. S. (2024). Literature Review : Klasifikasi Citra Medis Penyakit Pneumonia dengan Convolutional Neural Network. 3(9), 2339–2342.

Minarno, A. E. (2021). Klasifikasi Jenis Batik Menggunakan Algoritma Convolutional Neural Network. Jurnal Repositor, 3(2), 199–206. https://doi.org/10.22219/repositor.v3i2.1201

Mirwansyah, D., & Arief Wibowo. (2022). Fruit Image Classification Using Deep Learning Algorithm: Systematic Literature Review (Slr). Multica Science and Technology (Mst) Journal, 2(2), 120–123. https://doi.org/10.47002/mst.v2i2.356

Munfaati, E. A. N., & Witanti, A. (2024). Klasifikasi Buah dan Sayuran Segar atau Busuk Menggunakan Convolutional Neural Network. JISKA (Jurnal Informatika Sunan Kalijaga), 9(1), 27–38. https://doi.org/10.14421/jiska.2024.9.1.27-38

Nurcahyati, A. D., Akbar, R. M., & Zahara, S. (2022). Klasifikasi Citra Penyakit pada Daun Jagung Menggunakan Deep Learning dengan Metode Convolution Neural Network (CNN). SUBMIT: Jurnal Ilmiah Teknologi Infomasi Dan Sains, 2(2), 43–51. https://doi.org/10.36815/submit.v2i2.1877

Nurrani, H., Andi Kurniawan Nugroho, & Sri Heranurweni. (2023). Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(1), 168–178. https://doi.org/10.29207/resti.v7i1.4715

Pratitis, W. L., Kurniasari, K., & Fata, H. Al. (2023). Classification of Spotted Disease on Sugarcane Leaf Image Using Convolutional Neural

Network Algorithm. JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem Dan Komputer, 3(2), 117. https://doi.org/10.32503/jtecs.v3i2.3433

Prinzky, & Lubis, C. (2022). Klasifikasi Buah Segar Dan Busuk Menggunakan Convolutional Neural Network Berbasis Android. Jurnal Ilmu Komputer Dan Sistem Informasi, 10(2), 10823–10827. https://doi.org/10.24912/jiksi.v10i2.22551

Rasywir, E., Sinaga, R., & Pratama, Y. (2020). Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN). Paradigma - Jurnal Komputer Dan Informatika, 22(2), 117–123. https://doi.org/10.31294/p.v22i2.8907

Roy, S. K., Krishna, G., Dubey, S. R., & Chaudhuri, B. B. (2020). HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 17(2), 277–281. https://doi.org/10.1109/LGRS.2019.2918719

Sharma, A., & Phonsa, G. (2021). Image Classification Using CNN. SSRN Electronic Journal, (Icicc), 1–5. https://doi.org/10.2139/ssrn.3833453

Wijaya, R. B. M. A. A., Putri, D. N. A., & Fudholi, D. R. (2023). Smart GreenGrocer: Automatic Vegetable Type Classification Using the CNN Algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 17(3), 271. https://doi.org/10.22146/ijccs.82377

Winiarti, S., Wukir, C., Ahdiani, U., & Ismail, T. (2022). Klasifikasi Image Untuk Jenis Buku Bacaan Anak-Anak dengan Menggunakan Convolutional Neural Network. Jurnal Media Informatika Budidarma, 6(2), 738. https://doi.org/10.30865/mib.v6i2.3504

Downloads

Published

2025-03-30

Issue

Vol. 27 No. 1 (2025): March 2025 Period

Section

Articles

License

Copyright (c) 2025 Wilis Arum Karunia, Safriya Murni Puspita, Dwi Rolliawati, Ahmad Yusuf (Author)

Creative Commons License

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

Paradigma is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/) , This license permits: Share — copy and redistribute the material in any medium or format for any purpose, even commercially, Adapt — remix, transform, and build upon the material for any purpose, even commercially.

How to Cite

Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm. (2025). Paradigma - Jurnal Komputer Dan Informatika, 27(1), 48-54. https://doi.org/10.31294/p.v27i1.7577