TY - GEN
T1 - Graph-based Process Mining for Measuring Quality of Business Process Model
AU - Sungkono, Kelly Rossa
AU - Sarno, Riyanarto
AU - Mutia Kinanggit, Fara Dinda
AU - Shubhi, Irsyadhani Dwi
AU - Nurlaela, Khofifah
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Process mining can be used to model business processes. In process mining, graph-based can be used as an option in making a process model that is efficient based on quality. This research proposes a method of measuring fitness, precision, simplicity, and generalization. Measurement evaluation is carried out using two processes, port container handling and waste handling. Based on the research, the results of the fitness and precision port container handling using the Proposed Graph-based Quality Measurement and ETM Quality Measurement methods are 1.00. Meanwhile, the generalization obtained from both methods is 0.96. Simplicity generated by the Proposed Graph-based Quality Measurement method is 0.87 meanwhile the result of ETM Quality Measurement method is 1.00. In the case study of waste handling, based on both methods, the fitness is 1.00 and the generalization is 0.83. Precision obtained from the Proposed Graph-based Quality Measurement method is 0.95 and 0.75 from ETM Quality Measurement method. The simplicity obtained from the Graph-based Quality Measurement method is 0.95 meanwhile from ETM Quality Measurement method is 1.00. The simplicity calculation results are different because ETM cannot detect invisible tasks, while Graph-based Quality Measurement can discover them.
AB - Process mining can be used to model business processes. In process mining, graph-based can be used as an option in making a process model that is efficient based on quality. This research proposes a method of measuring fitness, precision, simplicity, and generalization. Measurement evaluation is carried out using two processes, port container handling and waste handling. Based on the research, the results of the fitness and precision port container handling using the Proposed Graph-based Quality Measurement and ETM Quality Measurement methods are 1.00. Meanwhile, the generalization obtained from both methods is 0.96. Simplicity generated by the Proposed Graph-based Quality Measurement method is 0.87 meanwhile the result of ETM Quality Measurement method is 1.00. In the case study of waste handling, based on both methods, the fitness is 1.00 and the generalization is 0.83. Precision obtained from the Proposed Graph-based Quality Measurement method is 0.95 and 0.75 from ETM Quality Measurement method. The simplicity obtained from the Graph-based Quality Measurement method is 0.95 meanwhile from ETM Quality Measurement method is 1.00. The simplicity calculation results are different because ETM cannot detect invisible tasks, while Graph-based Quality Measurement can discover them.
KW - business process
KW - graph
KW - process
KW - process discovery
KW - process mining
KW - process model
KW - quality measurement
UR - http://www.scopus.com/inward/record.url?scp=85147456264&partnerID=8YFLogxK
U2 - 10.1109/ICIEE55596.2022.10010272
DO - 10.1109/ICIEE55596.2022.10010272
M3 - Conference contribution
AN - SCOPUS:85147456264
T3 - 2022 International Conference on Informatics Electrical and Electronics, ICIEE 2022 - Proceedings
BT - 2022 International Conference on Informatics Electrical and Electronics, ICIEE 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Informatics Electrical and Electronics, ICIEE 2022
Y2 - 5 October 2022 through 7 October 2022
ER -