KLASIFIKASI KATEGORI KEHADIRAN KARYAWAN MENGGUNAKAN ALGORITMA GRADIENT BOOSTED TREES (Studi Kasus: PT Guna Kemas Indah)

Employee attendance is an important factor in human resource management, because it affects the productivity and efficiency of the company. However, recording and analyzing employee attendance often experiences obstacles, especially in terms of the accuracy and effectiveness of the system used. This study aims to develop an employee attendance classification model using the Gradient Boosted Trees algorithm to increase the accuracy of grouping attendance categories such as Present, Permission, Sick, Leave, and Absent into the High, Medium, Low attendance categories. The research method includes collecting employee attendance data at PT Guna Kemas Indah in 2024. Model evaluation uses the Accuracy, Precision, Recall, and Confusion Matrix metrics. The results of the study show that the developed model has an accuracy of 100% with a mean precision of 100% and a mean recall of 100%.
Keywords : Employee Attendance, Gradient Boosted Trees, Classification, Data Mining, Machine Learning