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All Journal Jurnal Mantik Journal of Artificial Intelligence and Digital Business
Moonap, Dinar Auranisa
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Application of k-nearest neighbors method for detection of beef authenticity based on beef image gunawan, Gunawan; Moonap, Dinar Auranisa; Fadhilah, Nurul
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5281

Abstract

Beef authenticity detection is a significant concern in today's food industry. This study proposes the K-Nearest Neighbors (K-NN) method based on the extraction of the Histogram of Oriented Gradients (HOG) feature to detect the authenticity of beef based on images. A dataset of 40 images of real and fake beef was collected and aggregated into 240 images to increase the variety of data. The imagery is changed to grayscale, and the HOG feature is extracted to capture texture and shape information. The K-NN model is built with optimized parameters using Grid Search and cross-validation techniques. The model was evaluated by measuring accuracy, precision, recall, and F1-score on the test data. The results show that the K-NN model with HOG feature extraction can achieve an accuracy of 80.56%,  precision of 87.10%, recall of 72.97%, and F1-score of 72.97% in classifying real and fake beef. These findings confirm the effectiveness of the proposed method for the rapid and accurate detection of beef authenticity. This research contributes to developing image-based food authenticity detection methods that can be applied to increase consumer confidence in the food industry
Penerapan Metode Naive Bayes untuk Deteksi Keaslian Daging Sapi berdasarkan Citra Daging Sapi Moonap, Dinar Auranisa; Murtopo, Aang Alim; Utami, Erni Unggul Sedya
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2254

Abstract

Penelitian ini bertujuan untuk mengembangkan model deteksi keaslian daging sapi berbasis citra digital menggunakan algoritma Naïve Bayes dengan integrasi fitur warna dan tekstur. Dataset terdiri dari 600 citra daging sapi yang terbagi seimbang antara kelas 0 (grade standar) dan kelas 1 (grade premium), dengan 50 dimensi fitur hasil ekstraksi menggunakan ruang warna RGB dan HSV untuk fitur warna, serta Gray Level Co-occurrence Matrix (GLCM) untuk fitur tekstur. Data dibagi dengan proporsi 80% untuk pelatihan dan 20% untuk pengujian. Hasil evaluasi dari sepuluh kali pengujian menunjukkan akurasi rata-rata 81,83% ± 4,01%, precision 78,26% ± 4,07%, recall 88,50% ± 5,80%, dan F1-score 82,94% ± 3,78%. Confusion matrix mengungkap bahwa model memiliki sensitivitas tinggi dalam mengidentifikasi daging asli (88,5%) dan specificity moderat dalam mendeteksi daging palsu (75,2%). Temuan ini membuktikan bahwa metode Naïve Bayes dengan kombinasi fitur warna dan tekstur efektif digunakan untuk deteksi keaslian daging sapi, sehingga berpotensi diimplementasikan pada sistem pendukung pengawasan mutu di pasar tradisional maupun industri pangan.
Co-Authors Aang Alim Murtopo Erni Unggul Sedya Utami, Erni Unggul Sedya Gunawan Nurul Fadhilah