Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002834	T144272	T1442722024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1442722024

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xviii, xxi hlm.; ilus.; tab.; 29 hlm

Language

Indonesia

ISBN/ISSN

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Classification

518.107

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Prodi Teknik Informatika
Komparasi

Specific Detail Info

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Statement of Responsibility

UIN YOLA

Other version/related

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