Analisis Perbandingan Naïve Bayes dan Random Forest dalam Klasifikasi Validitas Data Inventory PT Suplai Indo Tehnik
DOI:
https://doi.org/10.55606/juitik.v6i3.2775Keywords:
10-Fold Cross Validation, Data Mining, Inventory Validity, Naïve Bayes, Random ForestAbstract
Accuracy of inventory data is vital for distribution operational efficiency, yet data quality issues such as duplication and manual validation errors remain persistent at PT Suplai Indo Tehnik. This study aims to implement and compare the performance of Naïve Bayes and Random Forest algorithms in classifying inventory data validity into Valid and Invalid classes. The dataset comprises 3,236 cleaned operational records spanning 2022 to 2026 across eight selected attributes. Modeling was conducted using Orange Data Mining evaluated via 10-Fold Cross Validation. Results reveal that Random Forest significantly outperforms Naïve Bayes across all metrics, achieving 93.3% Accuracy, 93.3% Precision, 93.3% Recall, 93.3% F1-Score, an Area Under Curve (AUC) of 0.980, and a Matthews Correlation Coefficient (MCC) of 0.863, whereas Naïve Bayes yielded an Accuracy of 58.9% and an AUC of 0.635. The lower performance of Naïve Bayes stems from the violation of its conditional independence assumption due to inter-attribute correlations, whereas Random Forest's ensemble mechanism effectively captures complex feature interactions. Practically, these findings recommend Random Forest for automating real-time inventory data validation to enhance data accuracy and operational consistency.
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