KOMPARASI KINERJA ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR MACHINE DALAM ANALISIS POTENSI TURNOVER KARYAWAN

ZIDANE, NAUVAL AHMAD and Utami, Alvi Syahrini and Kurniati, Junia (2024) KOMPARASI KINERJA ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR MACHINE DALAM ANALISIS POTENSI TURNOVER KARYAWAN. Undergraduate thesis, Sriwijaya University.

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Abstract

Employee turnover, whether voluntary or involuntary, has a negative impact on company costs and productivity which influences employee decisions to stay or move, which can be analyzed using data mining techniques. This research aims to compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in analyzing potential employee turnover to provide in-depth insights to organizations. In Random Forest, parameters in the form of the number of trees are used, and in Support Vector Machine, parameters in the form of C values are used. The research results show that the Random Forest classification method has higher performance than the Support Vector Machine (SVM) method in the dataset tested. Random Forest shows performance stability with accuracy ranging from 65.93% to 78%, as well as relatively consistent precision, recall and F1-Score values, even with variations in the number of trees. On the other hand, SVM shows an accuracy level ranging from 46.10% to 51.76%, and there are indications of overfitting.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Komparasi, Kinerja, Random Forest, Support Vector Machine, Turnover
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nauval Ahmad Zidane
Date Deposited:	17 May 2024 06:27
Last Modified:	17 May 2024 06:27
URI:	http://repository.unsri.ac.id/id/eprint/144272

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