Classification of fresh chicken meat and tainted chicken meat using naive bayes classifier algorithm
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Published:
Mar 30, 2024
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Classification, Chiken Meat, GLCM, Naive Bayes, RapidMiner
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Abstract
This research discusses the classification of fresh and tainted chicken meat using the Naive Bayes Classifier (NBC) algorithm based on Gray Level Co-occurrence Matrix (GLCM) feature extraction, with the aim of developing an efficient and accurate classification method. This research aims to utilize image processing and machine learning technologies to distinguish fresh chicken meat from tainted ones, which is crucial for the food industry. The research methodology involved the use of GLCM for texture feature extraction from chicken meat images, with the implementation of the NBC model through RapidMiner, offering an intuitive and efficient approach. The results showed the success of the model in achieving 80% accuracy, with an average precision of 81.25%, recall of 80%, and F1-score of 80.62%, confirming its ability in chicken meat classification. The integration of GLCM and RapidMiner in the application of NBC not only improves accuracy and objectivity in chicken meat classification but also provides a foundation for the wider application of machine learning techniques in ensuring food safety and consumer satisfaction
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Zain, A. M. ., Ali Murtopo, A., Fadila, N., & Gunawan, G. (2024). Classification of fresh chicken meat and tainted chicken meat using naive bayes classifier algorithm. Journal of Intelligent Decision Support System (IDSS), 7(1), 19-26. https://doi.org/10.35335/idss.v7i1.212
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Agustina, F., & Ardiansyah, Z. A. (2020). Identifikasi Citra Daging Ayam Kampung dan Broiler Menggunakan Metode GLCM dan Klasifikasi-NN. 25 Jurnal Infokam, XVI(1). https://doi.org/https://doi.org/10.53845/infokam.v16i1.196
Alghamdi, N. A., & Al-Baity, H. H. (2022). Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence. Sensors (Basel, Switzerland), 22(20). https://doi.org/10.3390/s22208071
Ali, A., Samara, W., Alhaddad, D., Ware, A., & Saraereh, O. A. (2022). Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms. Sensors, 22(3). https://doi.org/10.3390/s22031016
Alizadeh, S. H., Hediehloo, A., & Harzevili, N. S. (2021). Multi independent latent component extension of naive Bayes classifier. Knowledge-Based Systems, 213, 106646. https://doi.org/10.1016/J.KNOSYS.2020.106646
Antika, R., Rifa, A., Dikananda, F., Indriya Efendi, D., & Narasati, R. (2023). PENERAPAN ALGORITMA DECISION TREE BERBASIS POHON KEPUTUSAN DALAM KLASIFIKASI PENYAKIT JANTUNG. JITA, 7(6). https://doi.org/https://doi.org/10.36040/jati.v7i6.8264
Anugrah Pratama, D., Rizal Mutaqin, I., & Rafael Manuela, K. (2023). Analisis Terjadinya Kanker Paru-Paru Pada Pasien Menggunakan Decision Tree: Penerapan Algoritma C4.5 Dan RapidMiner Untuk Menentukan Risiko Kanker Pada Gejala Pasien. JTMEI, 2, 156–170. https://doi.org/10.55606/jtmei.v2i4.3004
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-019-6413-7
Fadli, M., & Saputra, R. A. (2023). Classification And Evaluation Of Performance Models Random Forest For Stroke Prediction. Jurnal Teknik, 12(02). https://doi.org/http://dx.doi.org/10.31000/jt.v12i2.9099
Fajrin Mustafa, W., Hidayat, S., & Hatta Fudholi, D. (2024). Prediksi Retensi Pengguna Baru Shopee Menggunakan Machine Learning. JURNAL MEDIA INFORMATIKA BUDIDARMA , 8(1). https://doi.org/10.30865/mib.v8i1.7074
Giglioni, V., García-Macías, E., Venanzi, I., Ierimonti, L., & Ubertini, F. (2021). The use of receiver operating characteristic curves and precision-versus-recall curves as performance metrics in unsupervised structural damage classification under changing environment. Engineering Structures, 246, 113029. https://doi.org/10.1016/J.ENGSTRUCT.2021.113029
Gunawan, E., Wahyudi, J., & Sari, Y. (2021). PENDEKATAN BERBASIS KECERDASAN BUATAN DENGAN METODE NAÏVE BAYES UNTUK WEBSITE BAZNAS. JTIULM, 6(1). https://doi.org/https://doi.org/10.20527/jtiulm.v6i1.68
Maneno, R., Baso, B., Manek, P. G., & Fallo, K. (2023). Deteksi Tingkat Kematangan Buah Pinang Menggunakan Metode Support Vector Machine Berdasarkan Warna Dan Tekstur. Journal of Information and Technology, 3(2), 60–66. https://doi.org/10.32938/jitu.v3i2.5323
Mengiste, E., Mannem, K. R., Prieto, S. A., & Garcia de Soto, B. (2024). Transfer-Learning and Texture Features for Recognition of the Conditions of Construction Materials with Small Data Sets. Journal of Computing in Civil Engineering, 38(1). https://doi.org/10.1061/jccee5.cpeng-5478
Mondejar, M. E., Avtar, R., Diaz, H. L. B., Dubey, R. K., Esteban, J., Gómez-Morales, A., Hallam, B., Mbungu, N. T., Okolo, C. C., Prasad, K. A., She, Q., & Garcia-Segura, S. (2021). Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Science of The Total Environment, 794, 148539. https://doi.org/10.1016/J.SCITOTENV.2021.148539
Pirmansyah, F., & Wahyudi, T. (2023). IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA C4.5 UNTUK PREDIKSI EVALUASI ANGGOTA SATUAN PENGAMANAN STUDI KASUS PT. YIMM PULOGADUNG. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(3), 1566–1580. https://doi.org/10.35870/jimik.v4i3.370
Putra, A. P., & Syafira, A. F. (2023). Analisis Sentimen Data Twitter Topik Politik Dengan Metode Naive Bayes Dan Convolutional Neural Networks (Cnn). Jurnal Ilmiah Wahana Pendidikan, Oktober, 9(20), 36–41. https://doi.org/10.5281/zenodo
Putri, O. O., & Yodfiatfinda, Y. (2021). ANALYSIS OF MARKETING MARGIN OF THE BROILER CHICKEN TRADERS IN PD PASAR JAYA, PASAR MINGGU. Jurnal Bioindustri, 03(02). https://doi.org/https://doi.org/10.31326/jbio.v3i2.910.g471
Rachman, R., & Moritami, S. (2020). Sistem Pakar Deteksi Penyakit Refraksi Mata Dengan Metode Teorema Bayes Berbasis Web. JURNAL INFORMATIKA, 7(1). http://ejournal.bsi.ac.id/ejurnal/index.php/ji
Sen, S., Saha, S., Chatterjee, S., Mirjalili, S., & Sarkar, R. (2021). A bi-stage feature selection approach for COVID-19 prediction using chest CT images. Applied Intelligence, 51(12), 8985–9000. https://doi.org/10.1007/s10489-021-02292-8
Taranto-Vera, G., Galindo-Villardón, P., Merchán-Sánchez-Jara, J., Salazar-Pozo, J., Moreno-Salazar, A., & Salazar-Villalva, V. (2021). Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature. Journal of Supercomputing, 77(10), 11481–11513. https://doi.org/10.1007/s11227-021-03708-5
Ulinnuha, N., Fanani, A., & Sunan Ampel Surabaya, U. (2023). Classification of Drop Out Status for Students Using Naïve Bayes with Information Gain Feature Selection. Jurnal Teknologi Informasi, 22(4), 1014–1025. https://doi.org/https://doi.org/10.33633/tc.v22i4.9004
Ullu, H. H., Baso, B., Risald, R., Manek, P. G., & Chrisinta, D. (2022). Ektraksi Fitur Berbasis Tekstur Pada Citra Tenun Timor Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM). Journal of Information and Technology, 2(2), 70–74. https://doi.org/10.32938/jitu.v2i2.3245
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