Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)
Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)
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Prediksi Kelulusan Mahasiswa menggunakan 朴素贝叶斯算法（Studi Kasus 5 PTS di Banda Aceh）
DOI：
10.35870/jtik.v3i2.77
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Iqbal
中科院分区：
文献类型：
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作者：
M. Munawir;T. Iqbal
研究人员使用CodeIgniter和React-Js构建的电子问卷应用程序本研究旨在通过使用rapidminer工具进行数据挖掘，从2010-2014年的Feeder应用程序页面收集学生数据，假设该学生班级已于2018年毕业。数据收集自班达亚齐市的5所私立大学。那么通过观察毕业生的毕业水平，利用数据挖掘可以为教育机构带来相当大的贡献，努力提高高等教育的课程能力，期望数据挖掘的结果可以作为课程标准的参考，作为毕业生能力提高的一种形式。该研究方法使用跨行业数据挖掘标准流程（CRISP-DM），该流程用作标准数据挖掘流程以及从业务理解，数据理解，数据准备，建模，评估和部署开始的阶段的研究方法。结果表明，基于所选传球准确度属性的毕业预测数据挖掘算法与朴素贝叶斯算法的预测水平一致，预测准确率为84%。被发现有显着影响的分类过程的数据属性是GPA和研究长度。结果表明，60%的毕业生是在ASM Nusantara和AMIK Indonesia接受教育的学生，而在班达亚齐STIES和Serambi University Mecca，预计毕业率为52%。另一件事是不同于STIA Iskandar Thani，那里毕业的预测只有48%，不按时通过的预测是52%。这个预测的结果可以揭示并成为未来的学生或学者的建议，以增加毕业生的数量，增加学生对高等院校的信心。关键词：预测，学生毕业，朴素贝叶斯算法。
The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm.