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All Journal Perfecting a Video Game with Game Metrics Journal of Information Systems Engineering and Business Intelligence Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Amelia Effendi, Yutika
Universitas Airlangga
Computer Science & IT Electrical & Electronics Engineering

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Hierarchy Process Mining from Multi-Source Logs Riyanarto Sarno; Yutika Amelia Effendi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6326

Abstract

Nowadays, large-scale business processes is growing rapidly; in this regards process mining is required to discover and enhance business processes in different departments of an organization. A process mining algorithm can generally discover the process model of an organization without considering the detailed process models of the departments, and the relationship among departments. The exchange of messages among departments can produce asynchronous activities among department process models. The event logs from departments can be considered as multi-source logs, which cause difficulties in mining the process model. Discovering process models from multi-source logs is still in the state of the art, therefore this paper proposes a hierarchy high-to-low process mining approach to discover the process model from a complex multi-source and heterogeneous event logs collected from distributed departments. The proposed method involves three steps; i.e. firstly a high level process model is developed; secondly a separate low level process model is discovered from multi-source logs; finally the Petri net refinement operation is used to integrate the discovered process models. The refinement operation replaced the abctract transitions of a high level process model with the corresponding low level process models. Multi-source event logs from several departments of a yarn manufacturing were used in the computational study, and the results showed that the proposed method combined with the modified time-based heuristics miner could discover a correct parallel process business model containing XOR, AND, and OR relations.
Improved fuzzy miner algorithm for business process discovery Yutika Amelia Effendi; Riyanarto Sarno; Danica Virlianda Marsha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.19015

Abstract

Return material authorization (RMA) is a process in which a company decides to repair or replace customer’s defect product during the warranty period. To execute RMA, both company and customer obliged to follow standard operating procedure (SOP) which usually consists of many business processes of a company well. As the business process could cause inefficiencies, a company should improve their business process regularly. The best way is using process discovery. This research proposes a new improved fuzzy miner algorithm to represent binary correlation between activities. This new algorithm utilizes binary significance and binary correlation equally to acquire fuzzy model. While the original fuzzy miner algorithm uses various binary correlation metrics, the improved fuzzy miner algorithm uses only one metric and could capture the fuzzy model, accurately based on the event logs to capture more accurate business process model. In this research, ProM fuzzy miner is used as a comparison to the proposed improved time-based fuzzy miner. The results showed that the improved algorithm has higher value on conformance checking and able to capture business process model based on time interval, by using only time-interval significance as a binary correlation metrics.
Time-based α+ miner for modelling business processes using temporal pattern Yutika Amelia Effendi; Riyanarto Sarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.12733

Abstract

Business processes are implemented in an organization. When a business process is run, it generates event log. One type of event log is double timestamp event log. Double timestamp has the start and complete time of each activity executed in the business process and has a close relationship with temporal pattern. In this paper, seven types of temporal pattern between activities were presented as extended version of relations used in the double timestamp event log. Since the event log was not always executed in sequential way, therefore using temporal pattern, event log was divided into several small groups to mine the business process both sequential and parallel. Both temporal pattern and Time-based α+ Miner algorithm were used to mine process model, determined sequential and parallel relations and then evaluated the process model using fitness value. This paper was focused on the advantages of temporal pattern implemented in Time-based α+ Miner algorithm to mine business process. The results also clearly stated that the proposed method could present better result rather than that of original α+ Miner algorithm.
Process Discovery of Business Processes Using Temporal Causal Relation Yutika Amelia Effendi; Nania Nuzulita
Journal of Information Systems Engineering and Business Intelligence Vol. 5 No. 2 (2019): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.519 KB) | DOI: 10.20473/jisebi.5.2.183-194

Abstract

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log
Discovering Process Model from Event Logs by Considering Overlapping Rules Yutika Amelia Effendi; Riyanarto Sarno
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.404 KB) | DOI: 10.11591/eecsi.v4.1093

Abstract

Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of  the  discovered process  models is  a  must. Nowadays, using process  execution  data in the  past, process  models with rules underlying decisions in processes can be enriched, called decision mining. Rules defined over process data specify choices between multiple activities. One out of multiple activities is allowed to be executed in existing decision mining methods or it is known as mutually-exclusive rules. Not only mutually-exclusive rules, but also fully deterministic because all factors which influence decisions are recorded. However, because of non-determinism or incomplete   information,   there   are   some   cases   that   are overlapping  in  process  model.  Moreover,  the  rules  which are generated  from  existing  method  are  not  suitable  with  the recorded data. In this paper, a discovery technique for process model with data by considering the overlapping rules from event logs is presented. Discovering overlapping rules uses decision tree learning techniques, which fit the recorded data better than the existing method. Process model discovery from event logs is generated using Modified Time-Based Heuristics Miner Algorithm. Last, online book store management process model is presented in High-level BPMN Process Model.
The application of instrumentation system on a contactless robotic triage assistant to detect early transmission on a COVID-19 suspect Niko Azhari Hidayat; Prisma Megantoro; Abdufattah Yurianta; Amila Sofiah; Shofa Aulia Aldhama; Yutika Amelia Effendi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1334-1344

Abstract

This article discusses the instrumentation system of airlangga robotic triage assistant version 1 (ARTA-1), a robot used as a contact-free triage assistant for Coronavirus disease (COVID-19) suspects. The triage process consists of automatic vital signs check-up as well as the suspect’s anamnesis that in turns will determine whether the suspect will get a specific care or not. Measurements of a suspect’s vital conditions, i.e. temperature, height, and weight, are carried out with sensors integrated with the Arduino boards, while a touch-free, hand gesture questions and answers is carried out to complete anamnesis process. A portable document format (PDF) format of the triage report, which recommends what to do to the suspect, will then be automatically generated and emailed to a designated medical staff.
Co-Authors Abdufattah Yurianta Aldhama, Shofa Aulia Danica Virlianda Marsha Nania Nuzulita Niko Azhari Hidayat Prisma Megantoro Riyanarto Sarno Sofiah, Amila