Model Prediksi Untuk Menentukan Predikat Kelulusan Siswa Menggunakan Algoritma NaÃ¯ve Bayes Dan Mlp: Studi Kasus Smk Buddhi Tangerang

IF 0.9 3区数学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Random Structures & Algorithms Pub Date : 2022-03-25 DOI:10.31253/algor.v3i2.1429

Santa Margita

{"title":"Model Prediksi Untuk Menentukan Predikat Kelulusan Siswa Menggunakan Algoritma NaÃ¯ve Bayes Dan Mlp: Studi Kasus Smk Buddhi Tangerang","authors":"Santa Margita","doi":"10.31253/algor.v3i2.1429","DOIUrl":null,"url":null,"abstract":"Students of Vocational High School received the title of graduation after finished their studies. Whether graduating students capable or not to get high predicate was influenced by several factors. The factors that could affect the values are the averages of report, National Examination (UN), skill, Vocational Competency Exam (UKK), and attitude in knowing the pattern of these variables. The previous research showed that NaÃ¯ve Bayes algorithm has high accuracy value. Accuracy value obtained prove that the NaÃ¯ve Bayes has good accuracy percentage. Thus this algorithm can predict graduating students of SMK Buddhi Tangerang in terms of determining the predicate obtained. This research used the NaÃ¯ve Bayes algorithm and MLP in knowing the pattern of these variables. Testing was done by Confusion Matrix. The percentage results of accuracy proved that the NaÃ¯ve Bayes was 92%, while MLP 90%. Thus NaÃ¯ve Bayes algorithm has higher accuracy value than MLP. NaÃ¯ve Bayes algorithm could predict the predicate which was obtained by graduating students of Buddhi Dharma Vocational High School Tangerang.","PeriodicalId":54523,"journal":{"name":"Random Structures & Algorithms","volume":"70 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Random Structures & Algorithms","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.31253/algor.v3i2.1429","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

引用次数: 2

Abstract

Students of Vocational High School received the title of graduation after finished their studies. Whether graduating students capable or not to get high predicate was influenced by several factors. The factors that could affect the values are the averages of report, National Examination (UN), skill, Vocational Competency Exam (UKK), and attitude in knowing the pattern of these variables. The previous research showed that NaÃ¯ve Bayes algorithm has high accuracy value. Accuracy value obtained prove that the NaÃ¯ve Bayes has good accuracy percentage. Thus this algorithm can predict graduating students of SMK Buddhi Tangerang in terms of determining the predicate obtained. This research used the NaÃ¯ve Bayes algorithm and MLP in knowing the pattern of these variables. Testing was done by Confusion Matrix. The percentage results of accuracy proved that the NaÃ¯ve Bayes was 92%, while MLP 90%. Thus NaÃ¯ve Bayes algorithm has higher accuracy value than MLP. NaÃ¯ve Bayes algorithm could predict the predicate which was obtained by graduating students of Buddhi Dharma Vocational High School Tangerang.

查看原文本刊更多论文

模型预测决定谓语毕业学生使用NaA¯有一个贝叶斯算法和Mlp: Smk Buddhi Tangerang案例研究

中职学生在完成学业后获得毕业称号。毕业生能否获得高评价受多种因素的影响。影响这些数值的因素有:报告平均值、国家考试平均值、技能平均值、职业能力考试平均值以及对这些变量模式的认识态度。前人的研究表明，NaÃ¯ve贝叶斯算法具有较高的准确率值。得到的准确率值证明了NaÃ¯ve贝叶斯具有良好的准确率。因此，该算法可以通过确定所获得的谓词来预测SMK i Tangerang的毕业生。本研究使用NaÃ¯ve Bayes算法和MLP来了解这些变量的模式。测试由混淆矩阵完成。准确率百分比结果证明NaÃ¯ve Bayes为92%，MLP为90%。因此NaÃ¯ve Bayes算法比MLP具有更高的精度值。NaÃ¯ve贝叶斯算法可以预测到佛法职业高中Tangerang毕业生获得的谓词。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Random Structures & Algorithms 数学-计算机：软件工程

CiteScore

2.50

自引率

10.00%

发文量

审稿时长

>12 weeks

期刊介绍： It is the aim of this journal to meet two main objectives: to cover the latest research on discrete random structures, and to present applications of such research to problems in combinatorics and computer science. The goal is to provide a natural home for a significant body of current research, and a useful forum for ideas on future studies in randomness. Results concerning random graphs, hypergraphs, matroids, trees, mappings, permutations, matrices, sets and orders, as well as stochastic graph processes and networks are presented with particular emphasis on the use of probabilistic methods in combinatorics as developed by Paul Erdõs. The journal focuses on probabilistic algorithms, average case analysis of deterministic algorithms, and applications of probabilistic methods to cryptography, data structures, searching and sorting. The journal also devotes space to such areas of probability theory as percolation, random walks and combinatorial aspects of probability.