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All Journal Indonesian Journal of Data and Science
Mutia Safitri
Universitas Nusa Putra
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Classification of Employee Attendance Categories Using the Gradient Boosted Trees Algorithm Mutia Safitri; Sudin Saepudin; Carti Irawan; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.301

Abstract

Employee attendance is a crucial factor in human resource management as it affects productivity and operational efficiency. However, the recording and analysis of employee attendance often encounter challenges, particularly in terms of the accuracy and effectiveness of the systems used. This study aims to develop an employee attendance classification model using the Gradient Boosted Trees algorithm to improve the accuracy of grouping attendance categories such as Present, Permission, Sick, Leave, and Absent into attendance level categories: High, Medium, and Low. The research method includes collecting employee attendance data throughout the year 2024. The model evaluation is carried out using metrics such as accuracy, precision, recall, and the confusion matrix. The results indicate that the developed model achieves an accuracy of 100.00%, with a mean precision of 100.00% and a mean recall of 100.00%.
Co-Authors Carti Irawan Mupaat Saepudin, Sudin