K. R. Sungkono, A. Ahmadiyah, R. Sarno, M. Haykal, Muhammad Rayhan Hakim, Bagas Juwono Priambodo, Muhammad Amir Fauzan, Muhammad Kiantaqwa Farhan
{"title":"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","authors":"K. R. Sungkono, A. Ahmadiyah, R. Sarno, M. Haykal, Muhammad Rayhan Hakim, Bagas Juwono Priambodo, Muhammad Amir Fauzan, Muhammad Kiantaqwa Farhan","doi":"10.1109/APWiMob51111.2021.9435261","DOIUrl":null,"url":null,"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.","PeriodicalId":325270,"journal":{"name":"2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)","volume":"78 1-2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Graph-based Process Discovery containing Invisible Non-Prime Task in Procurement of Animal-Based Ingredient of Halal Restaurants\",\"authors\":\"K. R. Sungkono, A. Ahmadiyah, R. Sarno, M. Haykal, Muhammad Rayhan Hakim, Bagas Juwono Priambodo, Muhammad Amir Fauzan, Muhammad Kiantaqwa Farhan\",\"doi\":\"10.1109/APWiMob51111.2021.9435261\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":325270,\"journal\":{\"name\":\"2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)\",\"volume\":\"78 1-2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APWiMob51111.2021.9435261\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWiMob51111.2021.9435261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph-based Process Discovery containing Invisible Non-Prime Task in Procurement of Animal-Based Ingredient of Halal Restaurants
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.