Classification Of The Maturity Level Of Glutinous Rice Tape Fermentation Using Convolutional Neural Network

Rizqi Yunianti, Murinto Murinto

Abstract

Stiky tape is a popular snack in Indonesia made from fermented ketan rice. One of the main benefits of eating white cheddar rice is to trigger the digestive system. Excessive consumption can result in a decrease in sweetness and inappropriate texture. Therefore, it is necessary to classify the maturity level of the tape, so that there is no excessive maturity that results in adverse effects on the body and the quality of the tapes.The study aims to test the accuracy of the white tape maturity classification program as well as design and implement a classification system using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The white tape image data set was obtained with the iPhone X camera in jpg format, covering three maturity classes: raw, ripe, and rotten, each consisting of 400 images. The data set is divided into 768 training data, 192 validation data, and 240 test data, then processed through preprocessing stages including resize, augmentation, and rescale. The CNN model was implemented with the VGG16 architecture and tested on various Epochs, producing an accuracy of 0.98 on Epoches 20 and 30, and reaching 0.99 on the 40th. The results of the research showed that the CNN method with VGG-16 architecture was effective in classifying the maturity level of the tape, achieving high accuration and significant consistency as the number of Epochs increased. This implementation is expected to preserve the quality of the tapes and extend the application of modern technology in traditional industries.

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References

D. Candra, G. Wibisono, M. Ayu, and M. Afrad, “LEDGER: Journal Informatic and Information Technology Transfer Learning model Convolutional Neural Network menggunakan VGG-16 untuk Klasifikasi Tumor Otak pada Citra Hasil MRI,” Open Access Ledger, vol. 3, no. 1, 2024.

V. Anand, S. Gupta, D. Koundal, S. Mahajan, A. K. Pandit, and A. Zaguia, “Deep learning based automated diagnosis of skin diseases using dermoscopy,” Comput. Mater. Contin., vol. 71, no. 2, pp. 3145–3160, 2022, doi: 10.32604/cmc.2022.022788.

Murinto, “Klasifikasi Penyakit Kulit Wajah Menggunakan Metode Convolutional Neural Network Classification,” vol. 18, no. 2, pp. 183–190, 2021, [Online]. Available: https://eprints.umm.ac.id/79440/%0Ahttps://eprints.umm.ac.id/79440/1/Pendahuluan.pdf

H. S. Gill, O. I. Khalaf, Y. Alotaibi, S. Alghamdi, and F. Alassery, “Fruit Image Classification Using Deep Learning,” Comput. Mater. Contin., vol. 71, no. 2, pp. 5135–5150, 2022, doi: 10.32604/cmc.2022.022809.

S. Ashari, G. J. Yanris, and I. Purnama, “Oil Palm Fruit Ripeness Detection using Deep Learning,” Sinkron, vol. 7, no. 2, pp. 649–656, 2022, doi: 10.33395/sinkron.v7i2.11420.

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.

A. Kholik, “Klasifikasi Menggunakan Convolutional Neural Network (Cnn) Pada Tangkapan Layar Halaman Instagram,” J. Data Min. dan Sist. Inf., vol. 2, no. 2, p. 10, 2021, doi: 10.33365/jdmsi.v2i2.1345.

M. Z. Ersyad, K. N. Ramadhani, and A. Arifianto, “Pengenalan Bentuk Tangan Dengan Convolutional Neural Network (Cnn),” eProceedings Eng., vol. 7, no. 2, pp. 8212–8222, 2020.

D. H. Putra and V. Yudisthiana, “Perbandingan Tingkat Akurasi Arsitektur Convolutionsl Neural Network untuk Model Deteksi Penggunaan Masker Secara Otomatis ( Comparison of Convolutional Neural Network Architecture Accuracy Levels for Automatic Mask Use Detection Models )”.

N. Ho and Y. C. Kim, “Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification,” Sci. Rep., vol. 11, no. 1, pp. 1–11, 2021, doi: 10.1038/s41598-021-81525-9.

D. Han, Q. Liu, and W. Fan, “A new image classification method using CNN transfer learning and web data augmentation,” Expert Syst. Appl., vol. 95, pp. 43–56, 2018, doi: 10.1016/j.eswa.2017.11.028.

“Confusion Matrix,” SpringerReference. 2012. doi: 10.1007/springerreference_178869

DOI: https://doi.org/10.37058/innovatics.v7i1.11442

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