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All Journal International Journal of Advances in Intelligent Informatics ILKOM Jurnal Ilmiah
Melani, Erika Riski
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Computer Science & IT

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A comprehensive comparative analysis of chicken meat classification techniques through machine learning models Anraeni, Siska; Lahuddin, Harlinda; Ramdaniah, Ramdaniah; Melani, Erika Riski; Amalia, Andi Cici; Amaliah, Tazkirah
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2014

Abstract

This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multi-stage approach including data acquisition, preprocessing, feature extraction, and classification. A total of 1,000 chicken images were obtained, consisting of 800 images for training and 200 images for testing, with a proportion of 80:20. Feature extraction was performed using a combination of the HSI (Hue, Saturation, Intensity) color model to capture the color characteristics of chicken and the Local Binary Pattern (LBP) to extract texture information. Classification was performed using the K-Nearest Neighbor (KNN) algorithm with various K values and distance metrics. The experimental results show that the combination of color and texture features provides higher accuracy than using either feature alone. The best model using HSI and LBP feature extraction with K = 1 and K = 3 in the Euclidean distance metric achieved the highest accuracy of 95.4%. With a promising level of accuracy, this method can be applied in automated inspections in the poultry supply chain, improving food safety and helping consumers make better purchasing decisions. However, the main challenge in this study is the variation in lighting during image capture, which causes the fresh and rotten chicken feature values to overlap, thus hindering perfect classification.
Ripeness identification of chayote fruits using HSI and LBP feature extraction with KNN classification Anraeni, Siska; Melani, Erika Riski; Herman, Herman
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1153.150-159

Abstract

This study aims to build a system to identify the ripeness level of chayote that can be done easily and without damaging the quality of the chayote. This study employs digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-Nearest Neighbor classification so that the process of identifying the ripeness level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage. Next is feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP feature extraction and a combination of the two feature extractions. The final stage is to classify objects that are closest to the object being tested using the KNN method. By determining the value of K in the KNN classification method, the results show that the use of the Chebyshev distance calculation model in LBP feature extraction with K = 5 is a test that has the best accuracy of 90%.
Co-Authors Amalia, Andi Cici Amaliah, Tazkirah Herman Lahuddin, Harlinda Ramdaniah Ramdaniah Siska Anraeni