Pembuatan Aplikasi Data Mining Untuk Memperediksi Masa Studi Mahasiswa Menggunakan Algoritma Naive Bayes

Victor Tarigan
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Abstract

Data mining is a series of processes to obtain additional value information that is not known manually from a database. Data mining also manages experience or even mistakes in the past to improve the quality of the analysis model, one of which is the learning ability of data mining techniques, namely classification. Class is a learning task that is a new object into one of the class labels or categories on the old object that has been previously defined. This classification uses one method of data mining algorithm that is Naive Bayes.Naive Bayes algorithm works based on a certain distance between two objects by setting the value of k. The value of k is a parameter to determine the distance between the new object to the old object.By using the data mining technique, the university can obtain student academic data, namely the Achievement Index (IP) to predict the student's study period.In this data mining application consists of data testing and data training with NIM input.PHP and the database used is MySQL.The results of this data mining application this system can predict the results of the classification of student study period based on GPA 4 first semester, the average value of high school time, and grades in high school.
开发数据挖掘应用程序,使用天真贝斯算法进行未来的学生研究
数据挖掘是获取数据库中无法手动获取的附加价值信息的一系列过程。数据挖掘还通过管理过去的经验甚至错误来提高分析模型的质量,其中之一就是数据挖掘技术的学习能力,即分类。类是一种学习任务,它是将一个新对象放入一个类的标签或类别中,放在以前已经定义好的旧对象上。这种分类使用了一种数据挖掘算法,即朴素贝叶斯。朴素贝叶斯算法通过设置k的值来确定两个对象之间的一定距离,k的值是确定新对象与旧对象之间距离的一个参数。利用数据挖掘技术,学校可以获得学生的学业数据,即成绩指数(IP),以预测学生的学习时间。在这个数据挖掘应用程序中,使用NIM input.PHP进行数据测试和数据训练,使用的数据库是MySQL。本数据挖掘的结果应用本系统可以根据第一学期GPA 4、高中学习时间平均值、高中成绩预测学生学习时间分类的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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