In building a classification model, variables containing low predictive information are sometimes used. This can increase the bias on classification. Weight of Evidence (WoE) and Information Value (IV) provide a good theoretical foundation to explore, filtering, and transforming variables in binary classification. The value of IV can help measure the predictive power possessed by a variable in separating binary classes. This research implements this framework to screen 24 predictor variables that will be used in the svm classification model to improve the evaluation of the food insecure household classification model. We use the National Socioeconomic Survey by the Indonesian Central Bureau of Statistics in 2020 for West Java Province and 2021 for East Java Province to produce a classification model. The results of this study showed that WOE was able to improve the model evaluation value from the AUC value of 0.81 to 0.83 for West Java Province and the AUC value of 0.58 to 0.66 for East Java Province.
Open vehicle routing problems, as a variant of vehicle routing problem (VRP), define that the vehicles are not required to return to the depot after completing service. In this paper, we extend the problem to include process control due to the uncertainty of customer demands. Each customer has a demand and each customer must be serviced by a single vehicle and no vehicle may serve a set of customers whose total demand exceeds its capacity. Each vehicle route must start at the depot and end at the last customer it serves. The objective is to define the set of vehicle routes that minimizes the total costs. We solve the stochastic model using a strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method. This strategy is used to force the appropriate non-integer basic variables to move to their neighborhood integer points.
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