Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation
An optimization model of Economic Order Quantity (EOQ) is one of the methods used to determine the inventory order quantity that can minimize the cost of storage and the cost of ordering supplies. The optimization model in this paper is an optimization model of Economic Order Quantity (EOQ) with financial constraints and market tolerance. Financial constraints include the limited capital available per period and the advanced payment. In the market tolerance factor, there are two periods of market tolerance and three levels of the backorder. Based on this model, numerical simulations can be formulated and carried out so that the best optimal solution was obtained when conditions are unlimited, complete backorder, no advanced payment and the market tolerance period is 0.2 with a total cost decrease of 28,92% and a total profit increase of 13,37% from the initial condition.
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