TY - GEN
T1 - Classification talent of employee using C4.5, KNN, SVM
AU - Stephanie, Cecilia
AU - Sarno, Riyanarto
N1 - Publisher Copyright:
© 2019 IEEE
PY - 2019/7
Y1 - 2019/7
N2 - Employees are one of the important points of driving the company. With the existence of capable human resources, the company has competitive advantage compared to competitors. This research will propose a way to classify employees into 4-box talent management. The classification will use the C4.5 method, K-nearest neighbors (KNN), and Support Vector Machine (SVM). In classification, we use 18 criteria to calculate the performance and potential of the employee. The results of mapping the performance and potential of employees will divide employees into 4 labels, label 1 (needs development or too new to evaluate), label 2 (everyday solid contributor), label 3 (potential to be executive in 3+ years), and label 4 (needs to be an executive in 2 years). The results of the employee classification get the result that the SVM method has the highest accuracy, which is 94.62%, with P-value = 3.20E-11. KNN is a method that has the lowest accuracy of 87.37%.
AB - Employees are one of the important points of driving the company. With the existence of capable human resources, the company has competitive advantage compared to competitors. This research will propose a way to classify employees into 4-box talent management. The classification will use the C4.5 method, K-nearest neighbors (KNN), and Support Vector Machine (SVM). In classification, we use 18 criteria to calculate the performance and potential of the employee. The results of mapping the performance and potential of employees will divide employees into 4 labels, label 1 (needs development or too new to evaluate), label 2 (everyday solid contributor), label 3 (potential to be executive in 3+ years), and label 4 (needs to be an executive in 2 years). The results of the employee classification get the result that the SVM method has the highest accuracy, which is 94.62%, with P-value = 3.20E-11. KNN is a method that has the lowest accuracy of 87.37%.
KW - 4-box grid talent management
KW - C4.5
KW - Employee classification
KW - K-nearest neighbors (KNN)
KW - Support Vector Machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85077969086&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT46704.2019.8938508
DO - 10.1109/ICOIACT46704.2019.8938508
M3 - Conference contribution
AN - SCOPUS:85077969086
T3 - 2019 International Conference on Information and Communications Technology, ICOIACT 2019
SP - 388
EP - 393
BT - 2019 International Conference on Information and Communications Technology, ICOIACT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Information and Communications Technology, ICOIACT 2019
Y2 - 24 July 2019 through 25 July 2019
ER -