根据2013年课程同事的社会态度评价,对性格进行分类

Imron Rosadi
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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
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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
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