TY - GEN
T1 - Majority vote technique based on multi rough set for multi attributes decision-making system
T2 - 15th International Conference on Quality in Research: International Symposium on Electrical and Computer Engineering, QiR 2017
AU - Yulianti, Asri
AU - Sumpeno, Surya
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/5
Y1 - 2017/12/5
N2 - In the government agencies, civil servants are required to have competence or ability to finish the work effectively and efficiently. In fact, the decision-making system for determining position and assignment of civil servants' functional works is still performed manually, so it takes a longer time. Moreover, the results are not totally accurate in terms of their competency. Rough set, hereinafter called Single Rough Set, is a common method to solve this problem, but the process may be very complex and still has the unclassified result. In this research, Multi Rough Set and Majority Vote technique are proposed to enhance system performance of single rough set with multi attributes of job competency. It obtains accuracy rate with 5-fold cross-validation that is 83.67% better than a Single Rough Set and it has 0.947 Area Under Curve (AUC) derived from Receiver Operator Characteristic (ROC). Thus, it can be said that the system performance of Multi Rough Set can be considered excellent in classifying job competency for civil servants' functional works.
AB - In the government agencies, civil servants are required to have competence or ability to finish the work effectively and efficiently. In fact, the decision-making system for determining position and assignment of civil servants' functional works is still performed manually, so it takes a longer time. Moreover, the results are not totally accurate in terms of their competency. Rough set, hereinafter called Single Rough Set, is a common method to solve this problem, but the process may be very complex and still has the unclassified result. In this research, Multi Rough Set and Majority Vote technique are proposed to enhance system performance of single rough set with multi attributes of job competency. It obtains accuracy rate with 5-fold cross-validation that is 83.67% better than a Single Rough Set and it has 0.947 Area Under Curve (AUC) derived from Receiver Operator Characteristic (ROC). Thus, it can be said that the system performance of Multi Rough Set can be considered excellent in classifying job competency for civil servants' functional works.
KW - decision-making system
KW - job competency
KW - majority vote
KW - multi attributes
KW - multi rough set
UR - http://www.scopus.com/inward/record.url?scp=85045914102&partnerID=8YFLogxK
U2 - 10.1109/QIR.2017.8168449
DO - 10.1109/QIR.2017.8168449
M3 - Conference contribution
AN - SCOPUS:85045914102
T3 - QiR 2017 - 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering
SP - 45
EP - 51
BT - QiR 2017 - 2017 15th International Conference on Quality in Research (QiR)
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2017 through 27 July 2017
ER -