基于saw方法的scratch编程的奖学金确定决策支持系统

Indra Gunawan, Dentaruni Cahya Purnomo
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引用次数: 1

摘要

奖学金是向个人提供经济援助或支持的一种努力,这些个人有望在接受教育的过程中支持自己的需要。2020年,Cepu第一国家职业学校有1000多名学生,在300份奖学金申请中,只有9人获得奖学金。因此,需要一个工具来选择有资格和不太有资格获得奖学金的学生。决策支持系统是一种可以用来计算选拔学生的工具。所使用的方法是简单加性加权(SAW),因为它是一种简单的方法,但它仍然是一种准确的方法,因为它的误差水平很低。本研究的目的是使用Scratch编程将简单加性加权(SAW)方法应用于决策支持系统奖学金,Scratch编程是一种相当新开发的编程语言,与使用其他编程语言相比,它更容易应用于各种有吸引力的创作,而且时间更短。在本研究中使用的标准是父母的收入,父母的家属,家到学校的距离,和学生的年龄。从本研究得到的结果和分析得出的结论是,使用均方误差(MSE)的测试系统具有非常低的误差水平,其等于1.3312E-31,可以说该系统是准确的,并且从使用黑匣子的测试功能获得了100%的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DECISION SUPPORT SYSTEM FOR DETERMINATION OF SCHOLARSHIP USING SCRATCH PROGRAMMING BASED ON SAW METHOD
Scholarships are one of the efforts to provide financial assistance or support to individuals who are expected to be able to support the need in the process of taking education. In 2020, 1st State Vocational School of Cepu had more than 1000 students and from 300 scholarship submissions only 9 people managed to get a scholarship. Therefore a tool is needed to select students who are entitled and less entitled to get scholarships. Decision Support System is one of the tools that can be used to calculate to select the students. The method used is Simple Additive Weighting(SAW) because the method is a simple but it’s still an accurate method, because it has a very low error level. The aim of this study is to apply the Simple Additive Weighting(SAW) method for Decision Support System scholarships using Scratch Programming which is a programming language that is fairly newly developed and easy to apply to various attractive creations with a shorter time compared to using other programming languages. The criteria used in this study are parent’s income, parent’s dependents, Home-to-school Distance , and the students age. The results obtained and analyzed from this study concluded that the testing system using Mean Square Error(MSE) has a very low level of error which is equal to 1.3312E-31 it can be said that the system is accurate, and from the test functionality using the Black Box obtained 100% results.
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