Comparison of Machine Learning Model Performance in Classifying the Geographical Origin of Emeralds
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Abstract
This research aims to 1) construct machine learning models for classifying the geographical origins of emeralds from four sources—Colombia, Zambia (Kafubu), Zambia (Musakashi), and Afghanistan; and 2) compare the performance and accuracy of these models in origin classification. The study followed the six-phase CRISP-DM data mining methodology. The dataset was divided into training and testing sets in an 80:20 ratio, and a SMOTE technique was applied to address data imbalance issues. Feature selection was performed using the One-Way ANOVA technique combined with the Pearson correlation coefficient, resulting in 11 optimal elemental features. Four supervised machine learning algorithms were employed to construct the classification models: Random Forests, Support Vector Machines, Naïve Bayes, and k-Nearest Neighbors. Model performance was evaluated using a confusion matrix. The results revealed that the Random Forests model achieved the highest classification performance with 100% accuracy, followed by the Naïve Bayes and k-Nearest Neighbors models, both attaining an accuracy of 99.08%. The Support Vector Machines model produced the lowest accuracy at 97.25%. These findings demonstrate that machine learning techniques are highly effective tools for supporting the classification of emerald origins.
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