Abstract
Rubber latex is a raw material that is widely used as the main ingredient in the manufacture of various kinds of tools, so that the quality of the rubber latex must be paid close attention. In connection with this quality, there are several things that must be considered including the age of the tree, tapping time, dry rubber content, color and texture. The problem in determining the best quality of rubber latex is that it is not paid enough attention to by farmers and most farmers tap rubber tree sap only based on feeling or hunch. If this continues to happen, it will cause the production of tools that use rubber latex to become of less quality. In addition, the production department will also need a long time to select quality raw materials. Decision support systems can be used as an alternative to help with these problems. The Multi-Attribute Utility Theory (MAUT) method is used as a method for analyzing sample data from rubber latex based on the value of each existing criterion. The stages of the MAUT method have seven processes, namely the starting stage, determining alternatives and also criteria, displaying inputted data and alternatives, determining the weights for each criterion, creating a normalization matrix and decision matrix, then doing the sum of the results of normalization with criterion weights. After obtaining the decision value, the system will display the results of recommendations or decisions from each available alternative. From testing 30 rubber latex data, it was found that 17 data showed good quality with a value of 0.56 to 1.00 and 13 data showed bad quality with a value of 0.20 to 0.55. After a comparison between manual calculations and the system built did not show any difference in results, so that the decision support system built can be a tool to assist in making decisions regarding the quality of rubber latex
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