From Data to Insight: A Machine Learning Approach in Classifying Dairy Cow Productivity Level and Identifying Important Influencing Variables - Fauzi, Fatkhurokhman; Fauzan, Achmad; Widiyanto, Rhendy K P; Notodiputro, Khairil Anwar; Sartono, Bagus (Knowledge Engineering and Data Science, 2025)
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-score of 0.795, while the NN model obtained an accuracy of 0.786, a precision of 0.806, and an F1-score of 0.791. A permutational multivariate analysis of variance (PERMANOVA) on evaluation metrics revealed no statistically significant difference between the two methods. By applying PVI, nine key variables were consistently highlighted by both models as significant predictors for classifying dairy cow productivity levels (e.g., high vs. low yield) in Indonesia. These variables include farm altitude, the numbers of dairy heifers, lactating cows, and dry cows, the average duration of lactation and dry periods per cow annually, the daily amounts of forage, concentrate, and agricultural by-product feed provided per cow. These findings not only enhance model interpretability but also offer practical guidance for farm-level decision-making, the development of data-driven decision support systems, and the design of targeted policy interventions to improve dairy productivity in Indonesia, demonstrating the real-world applicability of machine-learning-based insights to strengthen dairy farm performance.