STUDI KOMPARASI KINERJA FUZZY TSUKAMOTO DENGAN RULE PAKAR DAN DECISION TREE SIMPLE CART BAGI PENERIMA BANTUAN SISWA MISKIN (STUDI KASUS : SDN 37 BENGKULU SELATAN)

RIOLANDI AKBAR, NIM. 18206050009 (2020) STUDI KOMPARASI KINERJA FUZZY TSUKAMOTO DENGAN RULE PAKAR DAN DECISION TREE SIMPLE CART BAGI PENERIMA BANTUAN SISWA MISKIN (STUDI KASUS : SDN 37 BENGKULU SELATAN). Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Elementary School 37 South Bengkulu State provides a program of assistance for recipients of poor scholarships to students who are less well off financially, with social protection card criteria, average report cards, dependents, parental income, achievements, and home ownership. Constraints that exist, there is a mismatch of the output results in the provision of student assistance because decision methods have not been used for each criterion and the assessment is still a prediction or estimate to prospective beneficiaries. In dealing with these problems, researchers made an idea in the form of determining the recipients of poor student assistance using the Tsukamoto fuzzy method with rules formed from experts and SimpleCart decision trees, expert rules and SimpleCart decision trees were used, namely: 1) to find out the results of the comparison of the two rules in the form of a decision tree right in handling this case, 2) The strengths of the SimpleCart decision tree compared to other decision trees, namely the results are easier to interpret, more accurate and faster calculations, besides the SimpleCart decision tree can be applied to a large number of data sets, very many variables and with mixed variable scales through binary sorting procedures. After calculating based on 75 test data obtained an accuracy of 72% accuracy using expert rules and 76% using the rule decision tree SimpleCart, and the results of the decisions of all students can be known value, which is in the form of can or cannot. So that a conclusion is found that the SimpleCart decision tree rule is more appropriate to use because the rule results are better able to select and search for weight values that produce better output as recipients of poor student assistance.

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