Open AccessDetermination System of Single Tuition Group Using a Combination of Fuzzy C-Means Clustering and Simple Additive Weighting Methods
Author(s) -
. Indrawati,
Muhammad Shahbaz Anwar,
Nova Amalia
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/536/1/012148
Subject(s) - weighting , analytic hierarchy process , fuzzy logic , test (biology) , simple (philosophy) , group (periodic table) , computer science , data mining , operations research , mathematics , statistics , artificial intelligence , medicine , paleontology , philosophy , chemistry , organic chemistry , epistemology , biology , radiology
Single Tuition is a single tuition fee that is borne by each student every semester based on their economic ability while Single Tuition fee is the total operational cost of each student every semester in the Study Program at State Universities. Determination of these fees is calculated based on several criteria such as the work of the father, the work of the mother, the income of the father and mother, the number of dependents of parents, the status of parents, the vehicle owned, taxes borne by parents, and several other criteria. How to calculate and determine the single tuition fee for each student is done manually, so that problems arise that are not efficient with time and cost. Therefore, in this study, a computerized single money determination system was created by making a decision support system that was able to automatically determine the Single Tuition group. Several studies have been conducted relating to this decision-making system, including the methods used such as Multi-Attribute Decision Making, Data Mining, and AHP. In this study using a combination of Fuzzy C-Means and Simple Additive Weighting methods to determine the Single Tuition Fee. Fuzzy C-Means groups the same data into one group, while Simple Additive Weighting performs a weighted sum with a performance rating on each alternative on all attributes. The study used 13 criteria and 62 sub-criteria as parameters in determining the Single Tuition group. From the results of testing 73 test data, it is known the results that in this test the number of clusters is 8 pieces, the number of weights is 2, the iteration is maximum 100, the smallest iteration is 0.01 and the initial iteration 1. The result is known that the decision-making system succeeded in ranking with a margin error 0 - 0.01.