Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction
Article Sidebar
Published:
Jun 30, 2024
Keywords:
Bernoulli Kernel, Chicken detection, Gaussian Kernel, GLCM feature extraction, Naive Bayes Classifier
Section
Articles
Statistics Article
Article View : 118 Times
Main Article Content
Abstract
The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence
Downloads
Download data is not yet available.
Article Details
How to Cite
Surorejo, S. ., Ubaidillah, M. R. ., Syefudin, S., & Gunawan, G. . (2024). Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction. Journal of Intelligent Decision Support System (IDSS), 7(2), 163-172. https://doi.org/10.35335/idss.v7i2.245
References
Ahmed, M. W., Hossainy, S. J., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2023). Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review. Comprehensive Reviews in Food Science and Food Safety, 22(6), 4378–4403. https://doi.org/https://doi.org/10.1111/1541-4337.13227
Balaji, V. R., Suganthi, S. T., Rajadevi, R., Kumar, V. K., Balaji, B. S., & Pandiyan, S. (2020). Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Measurement, 163, 107922. https://doi.org/https://doi.org/10.1016/j.measurement.2020.107922
Chen, J., Zhang, J., Wang, N., Xiao, B., Sun, X., Li, J., Zhong, K., Yang, L., Pang, X., & Huang, F. (2024). Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring. Food Chemistry, 138755. https://doi.org/https://doi.org/10.1016/j.foodchem.2024.138755
Cousins, T., Pentecost, M., & Van Helden, L. (2022). Containment and conversion: Urban livelihoods and the circulation of value amid South Africa’s avian influenza outbreak. American Ethnologist, 49(3), 413–426. https://doi.org/https://doi.org/10.1111/amet.13083
Das, S., Samal, A., & Ojha, P. K. (2024). Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health. Journal of Hazardous Materials, 471, 134326. https://doi.org/https://doi.org/10.1016/j.jhazmat.2024.134326
Farhadpour, S., Warner, T. A., & Maxwell, A. E. (2024). Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices. Remote Sensing, 16(3), 533. https://doi.org/https://doi.org/10.3390/rs16030533
Govoni, C., Chiarelli, D. D., Luciano, A., Ottoboni, M., Perpelek, S. N., Pinotti, L., & Rulli, M. C. (2021). Global assessment of natural resources for chicken production. Advances in Water Resources, 154, 103987. https://doi.org/https://doi.org/10.1016/j.advwatres.2021.103987
Gu, J., & Lu, S. (2021). An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Computers & Security, 103, 102158. https://doi.org/https://doi.org/10.1016/j.cose.2020.102158
Han, Z., Hu, W., Peng, S., Lin, H., Zhang, J., Zhou, J., Wang, P., & Dian, Y. (2022). Detection of standing dead trees after pine wilt disease outbreak with airborne remote sensing imagery by multi-scale spatial attention deep learning and Gaussian kernel approach. Remote Sensing, 14(13), 3075. https://doi.org/https://doi.org/10.3390/rs14133075
Hancock, J. T., Khoshgoftaar, T. M., & Johnson, J. M. (2023). Evaluating classifier performance with highly imbalanced big data. Journal of Big Data, 10(1), 42. https://doi.org/https://doi.org/10.1186/s40537-023-00724-5
Haque, M. H., Sarker, S., Islam, M. S., Islam, M. A., Karim, M. R., Kayesh, M. E. H., Shiddiky, M. J. A., & Anwer, M. S. (2020). Sustainable antibiotic-free broiler meat production: Current trends, challenges, and possibilities in a developing country perspective. Biology, 9(11), 411. https://doi.org/https://doi.org/10.3390/biology9110411
Hendrickson, O. D., Zvereva, E. A., Vostrikova, N. L., Chernukha, I. M., Dzantiev, B. B., & Zherdev, A. V. (2021). Lateral flow immunoassay for sensitive detection of undeclared chicken meat in meat products. Food Chemistry, 344, 128598. https://doi.org/https://doi.org/10.1016/j.foodchem.2020.128598
Jahir, N. R., Ramakrishna, S., Abdullah, A. A. A., & Vigneswari, S. (2023). Cultured meat in cellular agriculture: Advantages, applications and challenges. Food Bioscience, 102614. https://doi.org/https://doi.org/10.1016/j.fbio.2023.102614
Januardi, J., Lin, S.-W., & Lee, C.-S. (2024). Perishable food supply chain contract with asymmetric freshness information. International Journal of Systems Science: Operations & Logistics, 11(1), 2328026. https://doi.org/https://doi.org/10.1080/23302674.2024.2328026
Ke, B., Khandelwal, M., Asteris, P. G., Skentou, A. D., Mamou, A., & Armaghani, D. J. (2021). Rock-burst occurrence prediction based on optimized Naïve Bayes models. IEEE Access, 9, 91347–91360. https://doi.org/10.1109/ACCESS.2021.3089205
Khaled, A. Y., Parrish, C. A., & Adedeji, A. (2021). Emerging nondestructive approaches for meat quality and safety evaluation—A review. Comprehensive Reviews in Food Science and Food Safety, 20(4), 3438–3463. https://doi.org/https://doi.org/10.1111/1541-4337.12781
Lin, Y., Ma, J., Wang, Q., & Sun, D.-W. (2023). Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Critical Reviews in Food Science and Nutrition, 63(12), 1649–1669. https://doi.org/https://doi.org/10.1080/10408398.2022.2131725
Long, R. (2021). Fairness in machine learning: Against false positive rate equality as a measure of fairness. Journal of Moral Philosophy, 19(1), 49–78. https://doi.org/https://doi.org/10.1163/17455243-20213439
Mizik, T. (2021). Agri-food trade competitiveness: A review of the literature. Sustainability, 13(20), 11235. https://doi.org/https://doi.org/10.3390/su132011235
Movahedi, F., Padman, R., & Antaki, J. F. (2023). Limitations of receiver operating characteristic curve on imbalanced data: assist device mortality risk scores. The Journal of Thoracic and Cardiovascular Surgery, 165(4), 1433–1442. https://doi.org/https://doi.org/10.1016/j.jtcvs.2021.07.041
Pourmohammad-Zia, N., Karimi, B., & Rezaei, J. (2021). Food supply chain coordination for growing items: A trade-off between market coverage and cost-efficiency. International Journal of Production Economics, 242, 108289. https://doi.org/https://doi.org/10.1016/j.ijpe.2021.108289
Ragasa, C., Andam, K. S., Asante, S. B., & Amewu, S. (2020). Can local products compete against imports in West Africa? Supply-and demand-side perspectives on chicken, rice, and tilapia in Ghana. Global Food Security, 26, 100448. https://doi.org/https://doi.org/10.1016/j.gfs.2020.100448
Ramlucken, U., Lalloo, R., Roets, Y., Moonsamy, G., van Rensburg, C. J., & Thantsha, M. S. (2020). Advantages of Bacillus-based probiotics in poultry production. Livestock Science, 241, 104215. https://doi.org/https://doi.org/10.1016/j.livsci.2020.104215
Sethuraman, R. R., & Athisayam, J. S. K. (2021). An improved feature selection based on naive bayes with kernel density estimator for opinion mining. Arabian Journal for Science and Engineering, 46, 4059–4071. https://doi.org/https://doi.org/10.1007/s13369-021-05381-5
Shi, Y., Lu, X., Niu, Y., & Li, Y. (2021). Efficient jamming identification in wireless communication: Using small sample data driven naive bayes classifier. IEEE Wireless Communications Letters, 10(7), 1375–1379. https://doi.org/10.1109/LWC.2021.3064843
Siva Subramanian, R., & Prabha, D. (2021). Customer behavior analysis using Naive Bayes with bagging homogeneous feature selection approach. Journal of Ambient Intelligence and Humanized Computing, 12, 5105–5116. https://doi.org/https://doi.org/10.1007/s12652-022-03968-w
Wineman, A., Njagi, T., Anderson, C. L., Reynolds, T. W., Alia, D. Y., Wainaina, P., Njue, E., Biscaye, P., & Ayieko, M. W. (2020). A case of mistaken identity? Measuring rates of improved seed adoption in Tanzania using DNA fingerprinting. Journal of Agricultural Economics, 71(3), 719–741. https://doi.org/https://doi.org/10.1111/1477-9552.12368
Balaji, V. R., Suganthi, S. T., Rajadevi, R., Kumar, V. K., Balaji, B. S., & Pandiyan, S. (2020). Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Measurement, 163, 107922. https://doi.org/https://doi.org/10.1016/j.measurement.2020.107922
Chen, J., Zhang, J., Wang, N., Xiao, B., Sun, X., Li, J., Zhong, K., Yang, L., Pang, X., & Huang, F. (2024). Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring. Food Chemistry, 138755. https://doi.org/https://doi.org/10.1016/j.foodchem.2024.138755
Cousins, T., Pentecost, M., & Van Helden, L. (2022). Containment and conversion: Urban livelihoods and the circulation of value amid South Africa’s avian influenza outbreak. American Ethnologist, 49(3), 413–426. https://doi.org/https://doi.org/10.1111/amet.13083
Das, S., Samal, A., & Ojha, P. K. (2024). Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health. Journal of Hazardous Materials, 471, 134326. https://doi.org/https://doi.org/10.1016/j.jhazmat.2024.134326
Farhadpour, S., Warner, T. A., & Maxwell, A. E. (2024). Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices. Remote Sensing, 16(3), 533. https://doi.org/https://doi.org/10.3390/rs16030533
Govoni, C., Chiarelli, D. D., Luciano, A., Ottoboni, M., Perpelek, S. N., Pinotti, L., & Rulli, M. C. (2021). Global assessment of natural resources for chicken production. Advances in Water Resources, 154, 103987. https://doi.org/https://doi.org/10.1016/j.advwatres.2021.103987
Gu, J., & Lu, S. (2021). An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Computers & Security, 103, 102158. https://doi.org/https://doi.org/10.1016/j.cose.2020.102158
Han, Z., Hu, W., Peng, S., Lin, H., Zhang, J., Zhou, J., Wang, P., & Dian, Y. (2022). Detection of standing dead trees after pine wilt disease outbreak with airborne remote sensing imagery by multi-scale spatial attention deep learning and Gaussian kernel approach. Remote Sensing, 14(13), 3075. https://doi.org/https://doi.org/10.3390/rs14133075
Hancock, J. T., Khoshgoftaar, T. M., & Johnson, J. M. (2023). Evaluating classifier performance with highly imbalanced big data. Journal of Big Data, 10(1), 42. https://doi.org/https://doi.org/10.1186/s40537-023-00724-5
Haque, M. H., Sarker, S., Islam, M. S., Islam, M. A., Karim, M. R., Kayesh, M. E. H., Shiddiky, M. J. A., & Anwer, M. S. (2020). Sustainable antibiotic-free broiler meat production: Current trends, challenges, and possibilities in a developing country perspective. Biology, 9(11), 411. https://doi.org/https://doi.org/10.3390/biology9110411
Hendrickson, O. D., Zvereva, E. A., Vostrikova, N. L., Chernukha, I. M., Dzantiev, B. B., & Zherdev, A. V. (2021). Lateral flow immunoassay for sensitive detection of undeclared chicken meat in meat products. Food Chemistry, 344, 128598. https://doi.org/https://doi.org/10.1016/j.foodchem.2020.128598
Jahir, N. R., Ramakrishna, S., Abdullah, A. A. A., & Vigneswari, S. (2023). Cultured meat in cellular agriculture: Advantages, applications and challenges. Food Bioscience, 102614. https://doi.org/https://doi.org/10.1016/j.fbio.2023.102614
Januardi, J., Lin, S.-W., & Lee, C.-S. (2024). Perishable food supply chain contract with asymmetric freshness information. International Journal of Systems Science: Operations & Logistics, 11(1), 2328026. https://doi.org/https://doi.org/10.1080/23302674.2024.2328026
Ke, B., Khandelwal, M., Asteris, P. G., Skentou, A. D., Mamou, A., & Armaghani, D. J. (2021). Rock-burst occurrence prediction based on optimized Naïve Bayes models. IEEE Access, 9, 91347–91360. https://doi.org/10.1109/ACCESS.2021.3089205
Khaled, A. Y., Parrish, C. A., & Adedeji, A. (2021). Emerging nondestructive approaches for meat quality and safety evaluation—A review. Comprehensive Reviews in Food Science and Food Safety, 20(4), 3438–3463. https://doi.org/https://doi.org/10.1111/1541-4337.12781
Lin, Y., Ma, J., Wang, Q., & Sun, D.-W. (2023). Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Critical Reviews in Food Science and Nutrition, 63(12), 1649–1669. https://doi.org/https://doi.org/10.1080/10408398.2022.2131725
Long, R. (2021). Fairness in machine learning: Against false positive rate equality as a measure of fairness. Journal of Moral Philosophy, 19(1), 49–78. https://doi.org/https://doi.org/10.1163/17455243-20213439
Mizik, T. (2021). Agri-food trade competitiveness: A review of the literature. Sustainability, 13(20), 11235. https://doi.org/https://doi.org/10.3390/su132011235
Movahedi, F., Padman, R., & Antaki, J. F. (2023). Limitations of receiver operating characteristic curve on imbalanced data: assist device mortality risk scores. The Journal of Thoracic and Cardiovascular Surgery, 165(4), 1433–1442. https://doi.org/https://doi.org/10.1016/j.jtcvs.2021.07.041
Pourmohammad-Zia, N., Karimi, B., & Rezaei, J. (2021). Food supply chain coordination for growing items: A trade-off between market coverage and cost-efficiency. International Journal of Production Economics, 242, 108289. https://doi.org/https://doi.org/10.1016/j.ijpe.2021.108289
Ragasa, C., Andam, K. S., Asante, S. B., & Amewu, S. (2020). Can local products compete against imports in West Africa? Supply-and demand-side perspectives on chicken, rice, and tilapia in Ghana. Global Food Security, 26, 100448. https://doi.org/https://doi.org/10.1016/j.gfs.2020.100448
Ramlucken, U., Lalloo, R., Roets, Y., Moonsamy, G., van Rensburg, C. J., & Thantsha, M. S. (2020). Advantages of Bacillus-based probiotics in poultry production. Livestock Science, 241, 104215. https://doi.org/https://doi.org/10.1016/j.livsci.2020.104215
Sethuraman, R. R., & Athisayam, J. S. K. (2021). An improved feature selection based on naive bayes with kernel density estimator for opinion mining. Arabian Journal for Science and Engineering, 46, 4059–4071. https://doi.org/https://doi.org/10.1007/s13369-021-05381-5
Shi, Y., Lu, X., Niu, Y., & Li, Y. (2021). Efficient jamming identification in wireless communication: Using small sample data driven naive bayes classifier. IEEE Wireless Communications Letters, 10(7), 1375–1379. https://doi.org/10.1109/LWC.2021.3064843
Siva Subramanian, R., & Prabha, D. (2021). Customer behavior analysis using Naive Bayes with bagging homogeneous feature selection approach. Journal of Ambient Intelligence and Humanized Computing, 12, 5105–5116. https://doi.org/https://doi.org/10.1007/s12652-022-03968-w
Wineman, A., Njagi, T., Anderson, C. L., Reynolds, T. W., Alia, D. Y., Wainaina, P., Njue, E., Biscaye, P., & Ayieko, M. W. (2020). A case of mistaken identity? Measuring rates of improved seed adoption in Tanzania using DNA fingerprinting. Journal of Agricultural Economics, 71(3), 719–741. https://doi.org/https://doi.org/10.1111/1477-9552.12368
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.