Graph-based Process Discovery containing Invisible Non-Prime Task in Procurement of Animal-Based Ingredient of Halal Restaurants

K. R. Sungkono, A. Ahmadiyah, R. Sarno, M. Haykal, Muhammad Rayhan Hakim, Bagas Juwono Priambodo, Muhammad Amir Fauzan, Muhammad Kiantaqwa Farhan
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引用次数: 2

Abstract

A process model based on animal-based ingredients' running procurement will help halal level examiners check halal implementation based on Halal Critical Control Points (HCCP). Process discovery is a study for automatically forming a process model based on a log of running processes. There are several algorithms of process discovery, such as Alpha++ and Alpha#. However, the procurement of animal-based ingredient processes has an invisible non-prime task that has not been discussed in the existing algorithms. This research proposes a graph-based process discovery algorithm to form a process model containing invisible non-prime tasks based on the procurement processes. The log of procurement processes is obtained by using a business process management application, i.e., ProcessMaker. This research evaluates the proposed graph-based algorithm by comparing it with Alpha++ and Alpha# based on fitness and precision. The evaluation verifies that the proposed graph-based algorithm has a better quality of the obtained process model than Alpha++ and Alpha#. The fitness and precision of the graph-based algorithm are 1 and 1. On the other hand, the precisions of Alpha++ and Alpha# are 0.43 and 0.43, respectively.
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清真餐厅动物性食材采购中包含无形非启动任务的基于图的过程发现
基于动物性原料运行采购的流程模型将帮助清真级别审查员检查基于清真关键控制点(HCCP)的清真执行情况。流程发现是一项基于运行流程日志自动形成流程模型的研究。有几种进程发现算法,如Alpha++和Alpha#。然而,基于动物的原料过程的采购存在一个无形的非主要任务,这在现有算法中尚未得到讨论。本研究提出一种基于图的流程发现算法,以形成包含不可见非主要任务的采购流程模型。采购流程的日志是通过使用业务流程管理应用程序(即ProcessMaker)获得的。本研究通过将该算法与Alpha++和Alpha#在适应度和精度上进行比较,对所提出的基于图的算法进行了评价。评价结果表明,所提出的基于图的算法所得到的过程模型质量优于Alpha++和Alpha#。基于图的算法的适应度和精度分别为1和1。而Alpha++和Alpha#的精度分别为0.43和0.43。
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