Titin Yulianti, Mareli Telaumbanua, Hery Dian Septama, Helmy Fitriawan, Afri Yudamson


Identifying beef manually has some drawbacks because human visual has limitations and there are differences of human perception in assessing object quality. Several researches developed beef quality assessment methods based on image feature extraction. However, not all features support for obtaining the classification results that have high accuracy. The efficiency will be achieved if the classification analyzes only the relevant features. Therefore, a feature selection process is required to select relevant features and to eliminate irrelevant features to obtain more accurate and faster classification results. One of the feature selection algorithms is the F-Score which is a simple technique that measures the discrimination of two sets of real numbers. The features with the lowest ranking from the F-Score will be eliminated one by one until the most relevant features are obtained. The test is carried out by analyzing the classification results in the form of sensitivity, specificity, and accuracy values. The results of this research showed that by using the F-Score feature, the most relevant features for the classification of freshness level of local beef are obtained using the K-Nearest Neighbor (KNN) method. These features include the average color intensity R and standard deviation with a sensitivity of 0.8, a specificity of 0.93, and an accuracy of 86%.


Keywords:  Classification, Fiture Selection, F-Score, K-Nearest Neighbor, Local beef

Full Text:



Chen, Y.W. and Lin, C.J. 2008. Combining SVMs with Various Feature Selection Strategies. In Feature Extraction Book, Springer, Berlin, Heidelberg; 315-324.

Chian, V. N., Saad, F. S. A., Ibrahim, M.F., Sudin, S., Zakaria, A., and Shakaff, A. Y. M. 2014. Meat Color Recognition and Classification Based on Color using NIR/VIS Camera. Presented at the 8th MUCET, 10-11 November 2014, Melaka, Malaysia.

Guzek, D., Glapska, D., Pogorzelski, G., Kozań, K. et al. 2013. Variation of Meat Quality Parameters Due to Conformation and Fat Class in Limousin Bulls Slaughtered at 25 to 27 Months of Age. Asian Australasian Journal Animal Science, 26(5): 716-722.

Han, J., Kamber, M., and Pei, J. 2012. Data Mining - Concepts and Techniques. 3rd Edition. Elsevier Inc., Amsterdam, The Netherland.

Kadir, A. dan Susanto, A. 2013. Teori dan Aplikasi Pengolahan Citra. Andi Offset, Yogyakarta.

Kiswanto. 2012. Identifikasi Citra untuk Mengidentifikasi Jenis Daging Sapi dengan Menggunakan Transformasi Wavelet Haar. Tesis Magister. Universitas Diponegoro, Semarang.

Omar, N., Jusoh, F., Othman, M. S., Ibrahim, R. 2013. Review of Feature Selection for Solving Classification Problems. Jurnal Information System Research and Innovation (JISRI): 64-70

Sun, X., Chen, K. J., Maddock-Carlin, K. R., Anderson, V. L., Lepper A. N., Schwartz, C. A. 2012. Predicting beef tenderness using color and multispectral image texture feature. Meat Science Journal, 92: 386-393.

Witten, I. H. and Frank, E. 2011. Data Mining - Practical Machine Learning Tools and Techniques. 3rd, Morgan Kaufmann Publisher, San Francisco.

Yudamson, A. 2017. Rerata Intensitas Warna Terpisah untuk Identifikasi Daging Kambing, Daging Babi, Daging Celeng, dan Daging Anjing. JURNAL Pengabdian Kepada Masyarakat, 23(1): 211-213.

Yulianti, T., Yudamson, A., Septama, H. D., Sulistiyanti, S. R., Setiawan, F. X. A., and Telaumbanua, M. (2016). Meat quality classification based on color intensity measurement method. In 2016 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia: 248–252.

Yuristiawan, D. 2015. Aplikasi Pendeteksi Tingkat Kesegaran Daging Sapi Lokal Menggunakan Ekstraksi Fitur Warna dengan Pendekatan Statistika. Riptek, 9(1): 9-16.



  • There are currently no refbacks.

Copyright (c) 2021 Titin Yulianti, Mareli Telaumbanua, Hery Dian Septama, Helmy Fitriawan, Afri Yudamson

Analytics JTEP Stats


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.