Penanganan Data Tidak Seimbang pada Pemodelan Rotation Forest Keberhasilan Studi Mahasiswa Program Magister IPB
Penanganan Data Tidak Seimbang pada Pemodelan Rotation Forest Keberhasilan Studi Mahasiswa Program Magister IPB
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Penanganan Data Tidak Seimbang pada Pemodelan Rotation Forest Keberhasilan Studi Mahasiswa Program Magister IPB
DOI：
10.29244/xplore.v2i2.99
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Akbar Rizki
中科院分区：
文献类型：
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作者：
Junjun Wijaya;Agus M Soleh;Akbar Rizki
博戈尔农业大学研究生院（SPs-IPB）表示，并非所有IPB硕士课程的学生都能顺利完成学业。这成为IPB在未来选择学生时更具选择性的评价。本研究旨在建立2011年至2015年IPB硕士生成功分类模型。采用的分类方法是轮伐林。毕业生的比例与未通过的学生相比非常大，这可能导致评估值不同。SMOTE（Synthetic Minority Oversampling Technique）是通过生成人工数据来处理这种不平衡数据的方法之一。ROC（Receiver Operating Characteristic）曲线用于查看最佳截断值。有两种分类模型，即SMOTE处理前后的轮伐林模型。比较结果表明，以SMOTE为截断值，以0.6为截断值的轮伐林模型为最优模型。与SMOTE之前的建模相比，该模型可以将灵敏度值提高50%以上，尽管准确性和特异性值有所下降。
Graduate school of Bogor Agricultural University (SPs-IPB) stated that not all students of IPB master program successfully complete their studies. This becomes an evaluation for IPB to be more selective in choosing students in the future. This study aims to model the success classification of IPB master students in 2011 to 2015. The classification method used is rotation forest. The percentage of students who graduated is very large compared to those who did not pass, this can cause the evaluation value different. SMOTE (Synthetic Minority Oversampling Technique) is one of method to handle such unbalanced data by generating artificial data. The ROC (Receiver Operating Characteristic) curve is built to see the optimum cut off value. There are two classification models, they are rotation forest models before and after handled by SMOTE. The comparison results show that the rotation forest model after SMOTE with cut off value 0.6 is the best model. This model can increase the sensitivity value more than 50% although the accuracy and specificity value decreased compared to the modeling before SMOTE.