{"title":"Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa","authors":"Nandel Syofneri, Sarjon Defit, Sumijan","doi":"10.37034/jidt.v1i4.13","DOIUrl":null,"url":null,"abstract":"Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.","PeriodicalId":32041,"journal":{"name":"JUTEI Jurnal Terapan Teknologi Informasi","volume":"42 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JUTEI Jurnal Terapan Teknologi Informasi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37034/jidt.v1i4.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.
北坎巴鲁第二职业高中(SMK)是一所工业技术职业学校。现有学生2400人,开设专业14个。在学生中,学生的意志水平仍然很低。导致学生毕业率低。发生这种情况是因为很难预测在北干巴鲁SMK Negeri 2通过能力考试的水平。本研究的目的是协助预测能力测验的通过程度,进而预测学生的毕业情况。使用的方法是反向传播方法。使用此方法,可以使用您想要生成的输入值和目标来完成数据处理。从而在专业能力测试中预测学生的毕业情况。此外,要管理的数据是对计算机网络工程专业第一学期至第五学期的平均职业价值观的概括,以及各学期各学科的总和对2017学年和2018学年目标学生的认识。在Matlab应用程序中使用反向传播方法的计算结果将对未来学生毕业率的评分具有预测性。从而在预测中得到精度值。测试结果表明:5-4-1模式预测学生胜任力测试准确率达85%,5-6-1模式预测准确率达95%,5-8-1模式预测准确率达70%,5-10-1模式预测准确率达85%,5-12-1模式预测准确率达85%。五种模式中,5-6-1的最佳准确率为95%。使用反向传播方法的预测结果可以在明年成为知识。使测试中使用的系统参数能够正确识别。