KLASIFIKASI KETEPATAN LAMA STUDI MAHASISWADENGAN ALGORITMA RANDOM FOREST DAN GRADIENTBOOSTING (Studi Kasus Fakultas Ilmu Komputer Universitas Pembangunan Nasional Veteran Jakarta)
Universities generally admit new students every year and have different quotas for each department, but this is not matched by the number of students graduating, resulting in the number of students increasing every year, as well as the Faculty of Computer Science (FIK) at the National Development Un...
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2023-01-10.
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| 001 | repoupnvj_23475 | ||
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| 100 | 1 | 0 | |a Muhammad Labib Mu'tashim, . |e author |
| 245 | 0 | 0 | |a KLASIFIKASI KETEPATAN LAMA STUDI MAHASISWADENGAN ALGORITMA RANDOM FOREST DAN GRADIENTBOOSTING (Studi Kasus Fakultas Ilmu Komputer Universitas Pembangunan Nasional Veteran Jakarta) |
| 260 | |c 2023-01-10. | ||
| 500 | |a http://repository.upnvj.ac.id/23475/1/ABSTRAK.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/23475/2/AWAL.pdf | ||
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| 500 | |a http://repository.upnvj.ac.id/23475/7/BAB%205.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/23475/8/DAFTAR%20PUSTAKA.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/23475/9/RIWAYAT%20HIDUP.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/23475/10/LAMPIRAN.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/23475/11/HASIL%20PLAGIARISME.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/23475/12/ARTIKEL%20KI.pdf | ||
| 520 | |a Universities generally admit new students every year and have different quotas for each department, but this is not matched by the number of students graduating, resulting in the number of students increasing every year, as well as the Faculty of Computer Science (FIK) at the National Development University (UPN) Veteran Jakarta. The abundance of academic data at FIK UPN Veteran Jakarta can be processed according to what is needed and is useful for finding important information for better faculty development. Therefore, research was conducted to analyze students who graduated on time or not on time with data mining. This study uses the Random Forest and Gradient Boosting methods to determine the level of accuracy and determine which classification model is the best for the accuracy of student graduation. Both of these algorithms have very good accuracy on very large data requirements. The analysis uses data from S1 FIK UPN Veteran Jakarta students class 2015 - 2017. The results of the sample trial on 590 data, the 10 k-fold random forest algorithm obtains 82.64% accuracy and the 3 kfold gradient boosting obtains 79.66% accuracy. The results of this study are used as a basis for decision making for determining policies by the faculty. | ||
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| 690 | |a QA75 Electronic computers. Computer science | ||
| 690 | |a QA76 Computer software | ||
| 655 | 7 | |a Thesis |2 local | |
| 655 | 7 | |a NonPeerReviewed |2 local | |
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