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Budy Satria
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Journal : joiv international journal on informatics visualization

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Comparative Analysis of Weighted-KNN, Random Forest, and Support Vector Machine Models for Beef and Pork Image Classification Using Machine Learning Satria, Budy; Afrianto, Nurdi; Ningsih, Lidya; Sakinah, Putri; Sidauruk, Acihmah; Mayola, Liga
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3736

Abstract

The actual problem that occurs in the sale of meat by some conventional market traders is mixing beef with pork because of the high selling price. The difference between pork and beef lies in the color and texture of the meat. However, many people do not understand this difference. This study aims to provide a solution to distinguish the two types of beef through a classification process by obtaining the best accuracy using the W-KNN, RF, and SVM models based on machine learning. This study compares the model's performance based on the number of datasets, comprising 400 original images (200 beef and 200 pork images), using a 80:20 ratio for training and test data. The extraction process uses two algorithms: HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue). The model evaluation uses a confusion matrix that includes accuracy, Precision, Recall, and F1-score. Based on the results of the model testing, it was found that the random forest algorithm gave the best overall results, with the highest accuracy of 98.75%, Precision of 97%, F1-score of 98%, and recall of 99% on the number of decision trees of 400. This shows the stability and generalization of the superior model. The random forest algorithm is the most effective for classifying beef and pork data with minimal errors. Implications for further research include using a deep learning approach, especially for image processing, to detect differences in each meat characteristic and increase accuracy.
Co-Authors Abda Abda Abdullah Al Ramadhani Abdur Rohim Abdussalam Al Akbar Acihmah Sidauruk Adinda Raudhatul Jannah Afdhal Dinilhak Afifah Nur Latifah Afrianto, Nurdi Ahda Rindang Al Amin Ajeng Dwi Asti Al Hafiz, Muhammad Aldo Eko Syaputra Anggi Hadi Wijaya Arifan Rahman Arkan Ubaidillah Warman Ashari, Muhammad Al Azizah, Tiara Bister Purba Budi Permana Putra Candra Surya Chairil, Slamet Darmansah Darmansah Darmansah Darmansah Darmansah Darmansah Dedi Lesmana Derisma Derisma Devina Amanda Putri Dori Gusti Alex Candra Dwi Asti, Ajeng Efendi, Irman Efitra Efitra Efitra Fadilah, Sandi FAJAR, LM Firma Firma Firma Firma Fitra Yuda Gusla Nengsih, Yeyi Hafiz Mursalan Hendra, Yomei Humayra Fahreri Karimah Irsyadiyah Khairi, Luthfil Khelvin Ovela Putra Khoiri Putra Mujiza Leonard Tambunan Lidya Ningsih Liga Mayola LM Fajar Israwan, LM Fajar Luthfil Khairi Maulana, Fajar Meiditra, Irzon Meza Silvana Muhammad Al Ashari Muhammad Al Hafiz Muhammad Iqbal Muhammad Iqbal Mulia Sulistiyono Murni, Aprilia Mutamassikin Mutamassikin Nurcholis, Muhammad Tofa Nurfiah Prastya, Adi Qori Islami Aris Radillah, Teuku Raditya Wardhana Rahman, Arifan Rahman, Zumardi Rahmi Eka Putri Resad Setyadi SAKINAH, PUTRI Samosir, Khairunnisa Sandi Fadilah Sandi Fadilah Saputra, Alex Rizky Sarah Astiti S.Kom., M.MT Saskia Alifah Sepriano Sepriano Siti Sahara Lubis Sri Wulandari Supriatin Supriatin, Supriatin Syamsul Arifin Syarif Hidayatullah Tomy Nanda Putra Vita Amelia, Vita Wahyudi Willi Novrian Willi Novrian Yessi Ratna Sari Yessi Ratna Sari Yessi Ratna Sari Yeyi Gusla Nengsih Zamora, Sasya