Optimizing Scalability in Spice Identification through Transfer Learning with Convolutional Neural Networks

I Nyoman Switrayana, Muhamad Azwar

Abstract


Indonesia is renowned for its rich diversity of spices, which hold significant cultural and economic value. However, public knowledge of these spices remains limited, making their identification challenging. Addressing this issue, this study aims to develop a scalable spice identification system using Convolutional Neural Networks (CNN) with a Transfer Learning approach. The system is designed to recognize 30 types of spices while maintaining high accuracy, utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework for systematic development. The dataset was collected through open sources and web scraping from Google Images. Four CNN models (ResNet50, EfficientNetB0, Xception, and MobileNet) were evaluated under three data splits: 90:10, 80:20, and 70:30. Performance metrics including accuracy, precision, recall, and F1-score were used for evaluation. Among these models, Xception achieved the best performance in the 90:10 split, with an accuracy of 84.51%, followed by EfficientNetB0 at 83.57%. The results demonstrate that transfer learning effectively enhances model accuracy and scalability, enabling reliable spice identification across diverse categories. This system has practical implications for promoting public awareness, supporting culinary industries, and preserving Indonesia’s rich spice heritage. The proposed approach highlights the potential of CNN-based systems for addressing classification challenges in resource-constrained settings, offering a foundation for future research and real-world applications.


Keywords


Convolutional Neural Network; CRISP-DM; Optimizing; Spices Identification; Transfer Learning

Full Text:

PDF

References


M. Sanjaya and E. Nurraharjo, “Deteksi Jenis Rempah-Rempah Menggunakan Metode Convolutional Neural Network Secara Real Time,” J. Sains Komput. Inform. (J-SAKTI, vol. 7, no. 1, pp. 22–31, 2023.

I. N. Suandana, A. Asriyanik, and W. Apriandari, “Pemanfaatan CNN (Convolution Neural Network) Dan Mobilenet V2 Dalam Klasifikasi Rempah-Rempah Lokal Di Indonesia,” vol. 8, no. 5, pp. 10109–10116, 2024.

Kemenparekraf/Baparekraf RI, “Rempah-Rempah Khas Indonesia yang Banyak Diekspor,” Minggu, 22 Agustus, 2021. https://www.kemenparekraf.go.id/hasil-pencarian/rempah-rempah-khas-indonesia-yang-banyak-diekspor (accessed Dec. 25, 2024).

O. Diaz Annesa, C. Kartiko, and A. Prasetiadi, “Identifikasi Spesies Reptil Menggunakan Convolutional Neural Network (CNN),” Rekayasa Sist. dan Teknol. Inf., vol. 1, no. 3, pp. 899–906, 2019.

B. Dwi Mardiana, W. Budi Utomo, U. Nur Oktaviana, G. Wasis Wicaksono, and A. 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.

M. Harahap, Em Manuel Laia, Lilis Suryani Sitanggang, Melda Sinaga, Daniel Franci Sihombing, and Amir Mahmud Husein, “Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 70–77, 2022, doi: 10.29207/resti.v6i1.3373.

I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (CNN),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.

R. Alondeo Boimau and Y. R. Kaesmetan, “Klasifikasi Citra Digital Bumbu dan Rempah Dengan Algoritma Convolutional Neural Network ( CNN ),” vol. 2, no. 3, 2024.

A. E. Putra, M. F. Naufal, and V. R. Prasetyo, “Klasifikasi Jenis Rempah Menggunakan Convolutional Neural Network dan Transfer Learning,” J. Edukasi dan Penelit. Inform., vol. 9, no. 1, p. 12, 2023, doi: 10.26418/jp.v9i1.58186.

W. M. Pradnya D and A. P. Kusumaningtyas, “Analisis Pengaruh Data Augmentasi Pada Klasifikasi Bumbu Dapur Menggunakan Convolutional Neural Network,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2022, 2022, doi: 10.30865/mib.v6i4.4201.

Kaharuddin, Kusrini, and E. T. Luthfi, “Klasifikasi Jenis Rempah-Rempah Berdasarkan Fitur Warna Rgb Dan Tekstur Menggunakan Algoritma K-Nearest Neighbor,” J. Inf. Interaktif, vol. 4, no. 1, pp. 17–22, 2019.

S. Y. Riska and L. Farokhah, “Klasifikasi Bumbu Dapur Indonesia Menggunakan Metode K-Nearest Neighbors (K-NN),” Smatika J., vol. 11, no. 01, pp. 37–42, 2021, doi: 10.32664/smatika.v11i01.568.

F. A. Mufarroha and D. Abdul Fatah, “Klasifikasi Jenis Rempah Penghasil Minyak Atsiri Menggunakan Metode Machine Learning,” J. Simantec, vol. 11, no. 1, pp. 123–130, 2022, doi: 10.21107/simantec.v11i1.19743.

N. P. Batubara, D. Widiyanto, and N. Chamidah, “Klasifikasi Rempah Rimpang Berdasarkan Ciri Warna RGB Dan Tekstur GLCM Menggunakan Algoritma Naive Bayes,” Inform. J. Ilmu Komput., vol. 16, no. 3, p. 156, 2020, doi: 10.52958/iftk.v16i3.2196.

E. Tanuwijaya and A. Roseanne, “Modifikasi Arsitektur VGG16 untuk Klasifikasi Citra Digital Rempah-Rempah Indonesia,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 189–196, 2021, doi: 10.30812/matrik.v21i1.1492.

S. Hermawan and N. Agustina, “Implementasi Convolutional Neural Network untuk Klasifikasi Rempah-Rempah Khas Indonesia,” DoubleClick J. Comput. Inf. Technol., vol. 7, no. 1, pp. 1–7, 2023.

P. Kinanti, R. Gustriansyah, and Z. R. Mair, “Penggunaan Convolutional Neural Network (CNN) Untuk Klasifikasi Jenis Rempah – Rempah,” no. November, 2024.

C. Fadillah and S. Supatman, “Klasifikasi Jenis Rempah-Rempah Alami Untuk Kecantikan Menggunakan Densnet 121,” vol. 9, no. 1, pp. 923–929, 2025.

Y. Hatur Puspita and A. Sabri, “Transfer Learning Model Pralatih MobileNetV2 dan DenseNet121 untuk Klasifikasi Tanaman Rempah,” J. Ilm. Komputasi, vol. 23, no. 1, pp. 67–74, 2024, doi: 10.32409/jikstik.23.1.3502.

C. Nisa and F. Candra, “Klasifikasi Jenis Rempah-Rempah Menggunakan Algoritma Convolutional Neural Network,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 78–84, 2023, doi: 10.57152/malcom.v4i1.1018.

Hajriansyah, “Identifikasi Jenis Rempah-Rempah MenggunakanMetode CNN Berbasis Android,” J. Ris. Sist. Inf. Dan Tek. Inform., vol. 8, no. 1, pp. 223–232, 2023.

C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” Procedia Comput. Sci., vol. 181, no. 2019, pp. 526–534, 2021, doi: 10.1016/j.procs.2021.01.199.

I. N. Switrayana, D. Ashadi, H. Hairani, and A. Aminuddin, “Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods,” Int. J. Eng. Comput. Sci. Appl., vol. 2, no. 2, pp. 81–91, 2023, doi: 10.30812/ijecsa.v2i2.3406.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” vol. 19, no. 2, pp. 107–117, 2015, [Online]. Available: http://image-net.org/challenges/LSVRC/2015/.

M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing,” 2019.

F. Chollet and G. Inc., “Xception: Deep Learning with Depthwise Separable Convolutions,” Comput. Vis. Pattern Recognit., pp. 1251–1258, 2017.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” no. April 2017, 2017, [Online]. Available: https://www.researchgate.net/publication/316184205.

I. N. Switrayana, S. Hadi, and N. Sulistianingsih, “A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker,” vol. 13, pp. 1008–1021, 2024.

I. N. Switrayana and N. U. Maulidevi, “Collaborative Convolutional Autoencoder for Scientific Article Recommendation,” Proc. - 2022 9th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2022, pp. 96–101, 2022, doi: 10.1109/ICITACEE55701.2022.9924130.

R. Maulana, R. Dwi Zahra Putri, T. Ade Amelia, H. Syahputra, and F. Ramadhani, “Identifikasi Jenis Rempah-Rempah Indonesia Dengan Convolutional Neural Network (CNN) Menggunakan Arsitektur VGG16,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 4, pp. 6034–6039, 2024, doi: 10.36040/jati.v8i4.10138.




DOI: http://dx.doi.org/10.24014/coreit.v11i1.35453

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License  site stats  
Jurnal CoreIT by http://ejournal.uin-suska.ac.id/index.php/coreit/ is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.