Penerapan Metode MAUT Dan ROC Pada Sistem Pendukung Keputusan Penerima Bantuan Langsung TunaiPenerapan Metode MAUT Dan ROC Pada Sistem Pendukung Keputusan Penerima Bantuan Langsung Tunai

Abstract

Direct Cash Assistance (BLT) is assistance in the form of cash from the government given to communities affected by Covid-19, as a continuation and expansion of the targets of the target household program. Because the level of social welfare is determined by the government's economic policies. So poverty can also be caused by the failure of economic development planned by the government. Based on the results of the evaluation of the implementation of the Direct Cash Assistance program in the field, there is still a problem of distribution of aid that is not on target and evenly distributed, including due to a lack of accuracy and complexity in data processing in determining the poor. which is the main priority for aid recipients among the large number of population data who propose to receive aid. Direct cash assistance from village funds is intended for village communities who meet certain criteria, including poor families, families with residents who are chronically ill, do not have work, and all of them have not received other social assistance. Therefore, it is necessary to make an application for determining recipients of direct cash assistance by Good. Apart from making it easier for the village government to determine potential recipients of direct cash assistance, the results obtained will also be better. From the initial system which still used a manual system, there were many wrong targets by manipulating existing data, so with the new system which will be developed using an online-based programming language, satisfactory results can be achieved. In determining recipients of direct cash assistance, a decision support system (SPK) is needed. The methods applied in this research are the Multi Attribute Utility Theory (MAUT) and Rank Order Centroid (ROC) methods. The MAUT method is a method that finds the weighted sum of the same values for each utility for each attribute. This method can also process data from all attributes with different utilities. is defined as the weight added to a value that is relevant to the dimension value. Based on the calculation results of the MAUT method, a decision was obtained that the best alternative was alternative A2 in the name of "Edison Fau" with a final utility gain of 0.877.