Convolutional neural network model for early detection of meatballs containing borax
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Published:
Nov 25, 2023
Keywords:
Convolution Neural Network, Deep Learning, Food Safety, Image Classification, Meatball Borax
Section
Computer Science
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Abstract
The use of borax in meatballs to improve the texture and durability of meatballs is still rampant. Borax is very dangerous for consumers. Currently, monitoring of meatballs containing borax is done by experts in the laboratory. The public needs to know this information quickly. Therefore, a system is needed that can detect meatballs containing borax in real time. In this study we built a lightweight Convolutional Neutal Network (CNN) model and searched for optimal hyperparameters for the classification of meatballs containing borax. The results show that the proposed model outperforms other models in classifying meatballs containing borax with an accuracy value of 90%.
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How to Cite
Michael, A. . and Rusman, J. . (2023) “Convolutional neural network model for early detection of meatballs containing borax”, Jurnal Mantik, 7(3), pp. 2411-2420. doi: 10.35335/mantik.v7i3.4411.
References
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Fung, F., Wang, H. S., & Menon, S. (2018). Food Safety in the 21st Century. Biomedical Journal, 41(2), 88–95. https://doi.org/10.1016/j.bj.2018.03.003
Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, 448–456.
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516. https://doi.org/10.1007/s10462-020-09825-6
Lin, Y., & Wu, J. (2020). A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/5426532
Long, M., & Zeng, Y. (2019). Detecting iris liveness with batch normalized convolutional neural network. Computers, Materials and Continua, 58(2), 493–504. https://doi.org/10.32604/cmc.2019.04378
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Minarno, A. E., Hazmi Cokro Mandiri, M., Munarko, Y., & Hariyady, H. (2021). Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(2 SE-). https://doi.org/10.22219/kinetik.v6i2.1219
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Pradhana, A. A. S., Astuti, S. D., Fauziah, Permatasari, P. A. D., Agustina, R., Yaqubi, A. K., Setyawati, H., Winarno, & Putra, C. D. (2023). Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network. Journal of Electrical and Computer Engineering, 2023, 1–10. https://doi.org/10.1155/2023/8847929
Pratiwi, Y. S., Rini, D. M., Munarko, H., Prayitno, H., & Endah, R. W. (2020). Borax as a Non-Food Grade Addivite in the Perspective of Foof Safety and Human Resources Formation: A Literature Review. 1st International Conference Eco-Innovation in Science, Engineering, and Technology, 2020, 119–126. https://doi.org/10.11594/nstp.2020.0518
Purwanto, S. D., & Santoso, I. B. (2017). Sistem Identifikasi Kandungan Boraks pada Bakso Daging Sapi Berbasis Android Menggunakan Algoritma Naive Bayes Classifier. Matics, 9(1), 33. https://doi.org/10.18860/mat.v9i1.3954
Putri, D. P., Yulianti, L. E., Susanti, N. D., & A Putri, S. K. D. F. (2023). Detection of borax on snacks for school children (cilok and siomay) in subang district. IOP Conference Series: Earth and Environmental Science, 1168(1). https://doi.org/10.1088/1755-1315/1168/1/012044
Ranbir, Kumar, M., Singh, G., Singh, J., Kaur, N., & Singh, N. (2022). Machine Learning-Based Analytical Systems: Food Forensics. ACS Omega, 7(51), 47518–47535. https://doi.org/10.1021/acsomega.2c05632
Rane, S. (2011). Street Vended Food in Developing World: Hazard Analyses. Indian Journal of Microbiology, 51(1), 100–106. https://doi.org/10.1007/s12088-011-0154-x
Saputra, D. D., Fitriyah, H., & Setiawan, E. (2019). Sistem Klasifikasi Bakso yang Mengandung Boraks dengan Sensor Warna Menggunakan Metode K-Nearest Neighbor Berbasis Arduino. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(10), 10294–10300. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6663
Satcher, D. (2000). Food safety: A growing global health problem. Jama, 283(14), 1817. https://doi.org/10.1001/jama.283.14.1817
Anushka, Arya, C., Tripathi, A., Singh, P., Diwakar, M., Sharma, K., & Pandey, H. (2021). Object Detection using Deep Learning: A Review. Journal of Physics: Conference Series, 1854(1). https://doi.org/10.1088/1742-6596/1854/1/012012
Ba?tanlar, Y., & Ozuysal, M. (2014). Introduction to Machine Learning Second Edition. In Methods in molecular biology (Clifton, N.J.) (Vol. 1107). https://doi.org/10.1007/978-1-62703-748-8_7
Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6(March), 100134. https://doi.org/10.1016/j.mlwa.2021.100134
Dara, S., & Tumma, P. (2018). Feature Extraction by Using Deep Learning: A Survey. Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018, Iceca, 1795–1801. https://doi.org/10.1109/ICECA.2018.8474912
Elgendy, M. (2019). Deep Learning for Vision Systems. Manning Publications.
Fatikhani, D. A., & Setiawan, A. (2019). The relationship between the level of knowledge regarding fast food and the dietary habits among adolescents in Jakarta, Indonesia. Enfermeria Clinica, 29, 172–175. https://doi.org/10.1016/j.enfcli.2019.04.025
Fung, F., Wang, H. S., & Menon, S. (2018). Food Safety in the 21st Century. Biomedical Journal, 41(2), 88–95. https://doi.org/10.1016/j.bj.2018.03.003
Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, 448–456.
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516. https://doi.org/10.1007/s10462-020-09825-6
Lin, Y., & Wu, J. (2020). A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/5426532
Long, M., & Zeng, Y. (2019). Detecting iris liveness with batch normalized convolutional neural network. Computers, Materials and Continua, 58(2), 493–504. https://doi.org/10.32604/cmc.2019.04378
Michael, A., Palelleng, S., Devi Damayanti, I., & Rusman, J. (2023). Kombinasi Pretrained Model dan Random Forest Pada Klasifikasi Bakso Mengandung Boraks dan Non-Boraks Berbasis Citra. Teknika, 12(1), 27–32. https://doi.org/10.34148/teknika.v12i1.591
Minarno, A. E., Hazmi Cokro Mandiri, M., Munarko, Y., & Hariyady, H. (2021). Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(2 SE-). https://doi.org/10.22219/kinetik.v6i2.1219
Nurkhamidah, S. (2017). Identifikasi Kandungan Boraks Dan Formalin Pada Makanan Dengan Menggunakan Scientific Vs Simple Methods. Sewagati, 1(1), 26. https://doi.org/10.12962/j26139960.v1i1.2985
Pradhana, A. A. S., Astuti, S. D., Fauziah, Permatasari, P. A. D., Agustina, R., Yaqubi, A. K., Setyawati, H., Winarno, & Putra, C. D. (2023). Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network. Journal of Electrical and Computer Engineering, 2023, 1–10. https://doi.org/10.1155/2023/8847929
Pratiwi, Y. S., Rini, D. M., Munarko, H., Prayitno, H., & Endah, R. W. (2020). Borax as a Non-Food Grade Addivite in the Perspective of Foof Safety and Human Resources Formation: A Literature Review. 1st International Conference Eco-Innovation in Science, Engineering, and Technology, 2020, 119–126. https://doi.org/10.11594/nstp.2020.0518
Purwanto, S. D., & Santoso, I. B. (2017). Sistem Identifikasi Kandungan Boraks pada Bakso Daging Sapi Berbasis Android Menggunakan Algoritma Naive Bayes Classifier. Matics, 9(1), 33. https://doi.org/10.18860/mat.v9i1.3954
Putri, D. P., Yulianti, L. E., Susanti, N. D., & A Putri, S. K. D. F. (2023). Detection of borax on snacks for school children (cilok and siomay) in subang district. IOP Conference Series: Earth and Environmental Science, 1168(1). https://doi.org/10.1088/1755-1315/1168/1/012044
Ranbir, Kumar, M., Singh, G., Singh, J., Kaur, N., & Singh, N. (2022). Machine Learning-Based Analytical Systems: Food Forensics. ACS Omega, 7(51), 47518–47535. https://doi.org/10.1021/acsomega.2c05632
Rane, S. (2011). Street Vended Food in Developing World: Hazard Analyses. Indian Journal of Microbiology, 51(1), 100–106. https://doi.org/10.1007/s12088-011-0154-x
Saputra, D. D., Fitriyah, H., & Setiawan, E. (2019). Sistem Klasifikasi Bakso yang Mengandung Boraks dengan Sensor Warna Menggunakan Metode K-Nearest Neighbor Berbasis Arduino. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(10), 10294–10300. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6663
Satcher, D. (2000). Food safety: A growing global health problem. Jama, 283(14), 1817. https://doi.org/10.1001/jama.283.14.1817
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