Ensemble Learning for Optimizing Classification of Pork Adulteration in Beef Based on Electronic Nose Dataset

Pork and beef are the main resources of red meat in the world. However, not everyone can eat pork because of their religious background or other reasons. Therefore, it is very important to ensure the purity of the meat prior to being consumed. This research applied ensemble learning to optimize the classification on Electronic Nose Dataset for Pork Adulteration in Beef. Ensemble learning is one of a method that is widely used and the most successful method to improve performance. This research used several traditional machine learning algorithms and chose a machine learning algorithm, which produced the best result as the base classifier for ensemble learning to optimize the classification on Electronic Nose Dataset for Pork Adulteration in Beef. There were three ensemble learnings used in this research, namely hard voting, stacking, and bagging. The steps conducted in this research comprise (1) preprocessing by de-noising of the raw signals, (2) statistical feature extraction, (3) feature selection, (4) classification, (5) using ensemble learning to improve the performance, and (6) performance evaluation. The experiment result shows that hard voting ensemble learning using K-nearest neighbors (KNN) as base classifier is able to distinguish well between beef, pork, and pork adulteration in beef to seven classes which obtained 98.33% accuracy.