Klasifikasi Multi Class Pada Metode Kerja Jarak Jauh Menggunakan Algoritma Decision Tree dan Imbalance Data

  • Jefina Tri Kumalasari Universitas Bina Sarana Informatika, Jakarta
  • Agustiena Merdekawati Universitas Bina Sarana Informatika, Jakarta
  • Apriliani Hidayati Universitas Bina Sarana Informatika, Jakarta

Abstract

The COVID-19 pandemic that has hit the world has forced workers to work from home to prevent the spread of the virus. However, until the pandemic is over, based on survey conducted on 100 employee, found that working from home (Work from Home) is still a choice that many workers are interested in because it considered to provide flexibility and save more times. But some of them prefer to work from the office because it is considered easier than to focus and can increase productivity and more interested mixed mode of working. The analysis and comparison determined to find out about which work locations are more popular with workers. One solution to overcome this problem is that a classification method is needed to group the factors that influence the choice of work location. The classification method used for data processing is the Decision Tree method. The method for class imbalance problems uses the Synthetic Minority Over-sampling Technique (SMOTE) method. Tests were execute using Decision Tree and SMOTE split data which obtained an accuracy of up to 83.08% at a ratio of 0.5 (5:5). In this research, it was found that 13% of workers preferred to work from home, 25% chose to work from an office, and 27% chose mixed work models.

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Published
2024-01-30
How to Cite
KUMALASARI, Jefina Tri; MERDEKAWATI, Agustiena; HIDAYATI, Apriliani. Klasifikasi Multi Class Pada Metode Kerja Jarak Jauh Menggunakan Algoritma Decision Tree dan Imbalance Data. JISAMAR (Journal of Information System, Applied, Management, Accounting and Research), [S.l.], v. 8, n. 1, p. 109-117, jan. 2024. ISSN 2598-8719. Available at: <https://journal.stmikjayakarta.ac.id/index.php/jisamar/article/view/1350>. Date accessed: 25 feb. 2024. doi: https://doi.org/10.52362/jisamar.v8i1.1350.