Business process models complexity growth analysis based on scheduling generated by timetabling of courses

Yaqin, Muhammad Ainul and Sarno, Riyanarto and Rochimah, Siti (2021) Business process models complexity growth analysis based on scheduling generated by timetabling of courses. International Journal of Intelligent Engineering and Systems, 14 (6). pp. 66-79. ISSN 2185-3118

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Abstract

Business process models can represent course schedules. Every business process model has complexity. The greater the number of courses and the number of study groups, the more complex the business process model. The rate of increase in the complexity of the business process model is referred to as growth. The growth in the business process model complexity helps estimate business processes resource requirements at a certain complexity level. Therefore the equation for the growth of the complexity of the business process model needs to be formulated. The equation for the growth in the business process model complexity is obtained through several stages, namely developing a course schedule using the aSc TimeTables software. The order of the scheduling results is formatted as an event log. The event log is used as material in the mining process using the inductive mining algorithm to get a business process model represented using the Business Process Modeling Notation (BPMN). The complexity of the business process model is calculated using the Yaqin complexity formula. The equation for the growth function for the complexity of the business process model as a function of the number of study groups is obtained through logistic regression. Logistic regression is based on a logistic equation with 3 parameters, 4 parameters, and 5 parameters. Based on the three types of logistic equations regression results, we find that the logistic equation with 5 parameters has the highest average value of R2, namely 0.9698. Meanwhile, the equations for the growth of the business process model complexity are a function of the number of courses through power regression. The equations obtained through power regression have an average value of R2 = 0.9549. In this study, regression is carried out separately between the growth function as the number of study groups with the growth function as a function of the number of courses. This research novelty lies in using the number of study groups and the number of courses to measure the growth in the business process model complexity.

Item Type: Journal Article
Keywords: growth; complexity; business process model; BPMN; courses schedule
Subjects: 08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences
Divisions: Faculty of Technology > Department of Informatics Engineering
Depositing User: Muhammad Ainul Yaqin
Date Deposited: 26 Apr 2022 09:24

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