PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU BERDASARKAN USIA, JENIS KELAMIN, DAN INDEKS PRESTASI MENGGUNAKAN ALGORITMA DECISION TREE
PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU BERDASARKAN USIA, JENIS KELAMIN, DAN INDEKS PRESTASI MENGGUNAKAN ALGORITMA DECISION TREE
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PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU BERDASARKAN USIA、JENIS KELAMIN、DAN INDEKS PRESTASI MENGGUNAKAN 算法决策树
DOI：
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发表时间：
2017
期刊：
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通讯作者：
H. Himawan
中科院分区：
文献类型：
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作者：
Agus Romadhona;Suprapedi Suprapedi;H. Himawan
在大学学习期间，需要对学习时间进行预测，根据规定的时间来确定学生学习时间的准确性，从而做到与学习时间相关的智慧预防。本研究旨在寻找预测学生及时毕业的模式，利用数据挖掘技术和模型预测长学习周期，将决策树算法C4.5与ID3和CHAID算法进行比较，利用测试数据确定准确率、召回率和正确率的百分比，得出决策树算法C4.5与其他算法相比具有更好的性能。本研究发现，对学生学习时间的预测受到新生年龄、性别、第一学期至第四学期GPA的影响，其中对按时毕业学生第四学期GPA的影响最大，各属性增益值均为0.340。决策树算法C4.5在数据量389上准确率最高，k-fold=3时准确率为91.51%,k-fold= 5时准确率为90.75,k-fold= 10时准确率为90.77，而ID3和CHAID算法准确率较低。因此，决策树算法C4.5的值精度优于ID3和CHAID算法。在本研究中，训练数据的使用量高达389。为了在每个算法的结果准确性上看到更好的表现，因此为了进一步的研究，训练过程中使用的数据记录的数量应该有所提高。
Prediction of the study period in college is needed in determine the accuracy of the students study period according to the specified time so that wisdom of prevention related to the study period is no ton time could be done. This research aims to find patterns to predict the timely graduation of students usingdata mining techniques and models to predict long period of study was Decision tree algorithm C4.5 to compare with ID3 and CHAID algorithms using test data to determine the percentage of precision, recall and accuracy is obtained that the algorithm Decision Tree C4.5 has a better performance compared with other algorithms. From this research it was found that the prediction of the students study period are affected by incoming students age, gender, GPA semesters 1 through 4 semesters GPA and the most influential is the 4th semester GPA of students graduate on time with a value of 0.340 gain of all attributes. Decision tree algorithm C4.5 reaches the highest accuracy on the amount of data 389 with 91.51% accuracy values for k-fold=3, 90.75 for k-fold = 5 and 90.77 with k-fold = 10, While ID3 and CHAID algorithms achieving a low accuracy value. So thus the value accuracy of Decision Tree algorithm C4.5 is better than the ID3 and CHAID algorithm. In this research, training data are used as much as 389. To see better performance in the accuracy of the results of each algorithm, thus for furthermore research the number of data records used training process should be improved.