Pengujian Model Klasifikasi Kesegaran Daging Sapi Berbasis GLCM (Gray Level Co-occurrence Matrix) dan Algoritma Machine Learning - MUHAMAD IKBAL RAMDANI; HANNY HIKMAYANTI HANDAYANI; YUSUF EKA WICAKSANA; TOHIRIN AL-MUDZAKIR (Institut Teknologi Nasional Bandung, 2025)
AbstrakDaging sapi merupakan sumber hewani yang penting, namun konsumsi masyarakat Indonesia masih rendah dan harga yang terus meningkat mendorong adanya praktik curang, seperti mencampur daging segar dan tidak segar. Hal ini berdampak pada kesehatan karena daging sapi tidak segar mengandung bakteri berbahaya. Penelitian ini dilakukan untuk mengklasifikasikan kesegaran daging sapi dengan memanfaatkan metode ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) dan algoritma Random Forest serta Decision Tree. Penelitian ini menggunakan 400 data citra augmentasi dan dibagi menjadi 238 data latih dan 160 data uji atau dengan rasio 60:40. Hasil penelitian ini menunjukkan Accuracy sebesar 93% untuk Random Forest dan 88% untuk Decision Tree.Kata kunci: Daging Sapi, Klasifikasi Citra, Gray Level Co-ccurrence Matrix (GLCM), Random Forest, Decision TreeAbstractBeef is a vital source of animal protein. However, its consumption in Indonesia remains relatively low. The continuous increase in beef prices has led to fraudulent practices, such as mixing fresh and non-fresh meat, which poses serious health risks due to the presence of harmful bacteria in spoiled meat. This research aims to classify the freshness level of beef using feature extraction techniques through the Gray Level Co-occurrence Matrix (GLCM) and the Random Forest Algorithm. The study uses 400 augmented image data, divided into 238 training data and 160 testing data with a 60:40 ratio. The results show that the Random Forest algorithm achieved an Accuracy of 93%, while the Decision Tree reached 88%.Keywords: Beef, Image Classification, Gray Level Co-occurrence Matrix (GLCM), Random Forest, Decision Tree