OPTIMIZATION CVRP WITH MACHINE LEARNING FOR IMPROVED CLASSIFICATION OF IMBALANCED DATA FOOD DISTRIBUTION
DOI:
https://doi.org/10.33480/jitk.v10i4.6467Keywords:
CNN Classification, food distribution, imbalanced data, machine learning, optimizationAbstract
The classification of imbalanced data in food delivery distribution is an important issue that needs to be considered to ensure fairness and efficiency in the food distribution system. This research answers these problems by improving the accuracy of the classification of delivery locations that have imbalanced demand data, so that high priority areas are not neglected. Generating more efficient and cost-effective distribution routes, taking into account vehicle capacity and delivery urgency. Reducing delivery time and potential food waste due to delays or non-optimal route allocation. This study addresses the problem of improving the accuracy of delivery location classification that has imbalanced demand data, so that high priority areas are not neglected. Generate more efficient and cost-effective distribution routes, taking into account vehicle capacity and delivery urgency. Reduce delivery time and potential food wastage due to delays or non-optimal route allocation. This study uses the research stages of data collecting, data preprocessing, and implementation of K-Means and K-NN methods. The results of CVRP testing with K-Means show the value of cluster 7 acc=80, precc=85, recall=84. cluster 9 acc=85, precc=90, recall=91. cluster 11 acc=88, precc=93, recall=94. While the results of CVRP testing with K-NN show the value of K 7 acc=89, precc=88, recall=85. value of K 9 acc=87, precc=90, recall=91. value of K 11 acc=95, precc=97, recall=94. The optimization results show that this approach not only improves operational efficiency but also increases the accuracy of food delivery, which will affect the availability of traditional markets.
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