Penerapan Machine Learning untuk Klasifikasi Tingkat Produktivitas Pabrik Karet GAPKINDO Jambi
DOI:
https://doi.org/10.61132/prosemnasproit.v2i2.101Keywords:
Classification, decision tree, random forest, SVM, KNN, GapkindoAbstract
Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.
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