Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang)

Tubagus Riko Rivanthio, Mardhiya Ramdhani
{"title":"Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang)","authors":"Tubagus Riko Rivanthio, Mardhiya Ramdhani","doi":"10.30998/faktorexacta.v13i2.6588","DOIUrl":null,"url":null,"abstract":"SMA PGRI 1 Subang is a private school that has several missions, one of which is the establishment of academic and non-academic achievements. In an effort to achieve the mission must supervise student achievement. The effort he did was to provide understanding in the selection of majors in accordance with the interests and talents of students. But in the activity of providing understanding, the school does not yet have a model that can evaluate the interests and talents of students to choose majors. The model can be obtained using student data processing. Data processing can be done using data mining, namely data mining clustering techniques. The technique will produce a model in the selection of majors. This clustering process is the process of grouping similar data based on the similarity of data held by students. The research method used is the CRISP-DM method which has 6 stages consisting of: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation, and Dissemination. The data that is processed is 620 data consisting of class of students in 2014, 2015, 2016. The results of processing using clustering obtained 6 clusters that have different models for each cluster. The results of this study can be used by schools in recommending courses chosen by students according to students' interests and talents, so students can learn optimally.Key words: clustering, dataMining, suitability, majors, students","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":"10 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Faktor Exacta","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30998/faktorexacta.v13i2.6588","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

SMA PGRI 1 Subang is a private school that has several missions, one of which is the establishment of academic and non-academic achievements. In an effort to achieve the mission must supervise student achievement. The effort he did was to provide understanding in the selection of majors in accordance with the interests and talents of students. But in the activity of providing understanding, the school does not yet have a model that can evaluate the interests and talents of students to choose majors. The model can be obtained using student data processing. Data processing can be done using data mining, namely data mining clustering techniques. The technique will produce a model in the selection of majors. This clustering process is the process of grouping similar data based on the similarity of data held by students. The research method used is the CRISP-DM method which has 6 stages consisting of: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation, and Dissemination. The data that is processed is 620 data consisting of class of students in 2014, 2015, 2016. The results of processing using clustering obtained 6 clusters that have different models for each cluster. The results of this study can be used by schools in recommending courses chosen by students according to students' interests and talents, so students can learn optimally.Key words: clustering, dataMining, suitability, majors, students
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
对学生专业数据挖掘技术的应用(SMA PGRI 1 Subang案例研究)
SMA PGRI 1苏邦是一所私立学校,它有几个使命,其中一个是建立学术和非学术成果。为了努力完成任务,必须监督学生的成绩。他所做的努力是在根据学生的兴趣和才能选择专业方面提供理解。但在提供理解的活动中,学校还没有一个可以评估学生选择专业的兴趣和才能的模式。该模型可以通过对学生数据的处理得到。数据处理可以使用数据挖掘,即数据挖掘聚类技术来完成。这项技术将为专业的选择提供一种模式。这种聚类过程是基于学生持有的数据的相似性对相似数据进行分组的过程。研究方法采用CRISP-DM方法,分为业务理解、数据理解、数据处理、建模、评估和传播6个阶段。处理的数据为2014年、2015年、2016年班级620个数据。聚类处理结果得到6个聚类,每个聚类具有不同的模型。本研究的结果可以作为学校根据学生的兴趣和才能为学生推荐课程的依据,从而达到学生的最佳学习效果。关键词:聚类,数据挖掘,适用性,专业,学生
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 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
7 weeks
期刊最新文献
Perancangan Diagnosa Covid-19 Menggunakan Metode Case Based Reasoning (CBR) Untuk Mengidentifikasi Tingkatan Gejala Pasien Covid-19 Analisis Model Matematika dan Simulasi Pada Penyebaran Hepatitis Non HepA-E Akut di Indonesia Perancangan Sistem Informasi Hino Service on Site (Studi Kasus : Dealer Hino, PT. Persada Lampung Raya) Penerapan Algoritma Sweep dan Particle Swarm Optimization (PSO) sebagai Alternatif Menentukan Rute Distribusi Penerapan Metode Convolution Neural Network (CNN) Dalam Proses Pengolahan Citra Untuk Mendeteksi Cacat Produksi Pada Produk Masker
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1