{"title":"根据2013年课程同事的社会态度评价,对性格进行分类","authors":"Imron Rosadi","doi":"10.30736/teknika.v10i2.237","DOIUrl":null,"url":null,"abstract":"This aim of this researh is to classify an assessment sentence from students, then classified to produce an information. Classification is used to determine the character of each student so that teachers have no difficulty in assessing the social character of the student, based on asesment orientation On Kurikulum 2013. This research is empasize to processing opinion for the students to evaluate their friend in SMA Negeri 1 Ngimbang Lamongan, and the opinion to evaluate each student will classified into 6 social attitudes that is Honest, Discipline, Responsibility, Pay Attention (mutual cooperation, tolerance), well manered and selsf confidence, this research is divide into 2 phase that is the process to produce training data (dataset) and the process to classify opinion (test data). Both off the process are to at extracting the attributes and object components which commented in every document and to decide the classification social attitudes classfication for each student.On the system for this character clasification produce accuracy with the sucess rate from the result of the testing clasficataion use Algoritman Naive Bayes success rate of 72%, this approach paper I use of 80%, the succes rate of 72% because the use of the sentence for rating colleague had the rate of variants are quite low that affect the process off making the data that will be used to the system off the clasificaton of characterer","PeriodicalId":17707,"journal":{"name":"Jurnal Qua Teknika","volume":"20 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"KLASIFIKASI KARAKTER BERDASARKAN PENILAIAN SIKAP SOSIAL TEMAN SEJAWAT PADA KURIKULUM 2013 MEMANFAATKAN NAIVE BAYES\",\"authors\":\"Imron Rosadi\",\"doi\":\"10.30736/teknika.v10i2.237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This aim of this researh is to classify an assessment sentence from students, then classified to produce an information. Classification is used to determine the character of each student so that teachers have no difficulty in assessing the social character of the student, based on asesment orientation On Kurikulum 2013. This research is empasize to processing opinion for the students to evaluate their friend in SMA Negeri 1 Ngimbang Lamongan, and the opinion to evaluate each student will classified into 6 social attitudes that is Honest, Discipline, Responsibility, Pay Attention (mutual cooperation, tolerance), well manered and selsf confidence, this research is divide into 2 phase that is the process to produce training data (dataset) and the process to classify opinion (test data). Both off the process are to at extracting the attributes and object components which commented in every document and to decide the classification social attitudes classfication for each student.On the system for this character clasification produce accuracy with the sucess rate from the result of the testing clasficataion use Algoritman Naive Bayes success rate of 72%, this approach paper I use of 80%, the succes rate of 72% because the use of the sentence for rating colleague had the rate of variants are quite low that affect the process off making the data that will be used to the system off the clasificaton of characterer\",\"PeriodicalId\":17707,\"journal\":{\"name\":\"Jurnal Qua Teknika\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Qua Teknika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30736/teknika.v10i2.237\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Qua Teknika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30736/teknika.v10i2.237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
本研究的目的是对来自学生的评价句子进行分类,然后分类产生一个信息。分类是用来确定每个学生的性格,使教师在评估学生的社会性格时没有困难,基于评估取向on Kurikulum 2013。本研究的重点是处理学生对SMA Negeri 1 Ngimbang Lamongan中的朋友的评价意见,并将每个学生的评价意见分为诚实,纪律,责任,关注(相互合作,宽容),礼貌和自信6种社会态度。本研究分为两个阶段,即产生训练数据(数据集)的过程和对意见进行分类(测试数据)的过程。这两个过程都是为了提取每个文档中评论的属性和对象组件,并确定每个学生的分类社会态度分类。关于系统对于这个字符的分类产生的准确率与成功率,从测试的结果看,使用算法朴素贝叶斯分类的成功率为72%,本文中我使用的方法成功率为80%,72%的成功率是因为使用同事的句子进行评级时所产生的变异率相当低,从而影响了这个过程,使得系统将使用的数据用于字符的分类
KLASIFIKASI KARAKTER BERDASARKAN PENILAIAN SIKAP SOSIAL TEMAN SEJAWAT PADA KURIKULUM 2013 MEMANFAATKAN NAIVE BAYES
This aim of this researh is to classify an assessment sentence from students, then classified to produce an information. Classification is used to determine the character of each student so that teachers have no difficulty in assessing the social character of the student, based on asesment orientation On Kurikulum 2013. This research is empasize to processing opinion for the students to evaluate their friend in SMA Negeri 1 Ngimbang Lamongan, and the opinion to evaluate each student will classified into 6 social attitudes that is Honest, Discipline, Responsibility, Pay Attention (mutual cooperation, tolerance), well manered and selsf confidence, this research is divide into 2 phase that is the process to produce training data (dataset) and the process to classify opinion (test data). Both off the process are to at extracting the attributes and object components which commented in every document and to decide the classification social attitudes classfication for each student.On the system for this character clasification produce accuracy with the sucess rate from the result of the testing clasficataion use Algoritman Naive Bayes success rate of 72%, this approach paper I use of 80%, the succes rate of 72% because the use of the sentence for rating colleague had the rate of variants are quite low that affect the process off making the data that will be used to the system off the clasificaton of characterer