From Data to Insight: A Machine Learning Approach in Classifying Dairy Cow Productivity Level and Identifying Important Influencing Variables

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing classification method. The novelty of this study lies in integrating model-agnostic interpretability with multiple supervised classifiers to generate transparent, data-driven insights into dairy productivity determinants. Results indicate that the top-performing methods, SVM and NN, achieved predictive accuracies ranging from 70% to 89%. Specifically, the SVM model achieved an accuracy of 0.799, a precision of 0.845, and an F1-s