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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Graph-based algorithm for checking wrong indirect relationships in non-free choice Agung Wiratmo; Kelly Rossa Sungkono; 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.v18i1.12982

Abstract

In this context, this paper proposes a combination of parameterised decision mining and relation sequences to detect wrong indirect relationship in the non-free choice. The existing decision mining without parameter can only detect the direction, but not the correctness. This paper aims to identify the direction and correctness with decision mining with parameter. This paper discovers a graph process model based on the event log. Then, it analyses the graph process model for obtaining decision points. Each decision point is processed by using parameterised decision mining, so that decision rules are formed. The derived decision rules are used as parameters of checking wrong indirect relationship in the non-free choice. The evaluation shows that the checking wrong indirect relationships in non-free choice with parameterised decision mining have 100% accuracy, whereas the existing decision mining has 90.7% accuracy.
Anomaly Detection based on Control-flow Pattern of Parallel Business Processes Hendra Darmawan; Riyanarto Sarno; Adhatus Solichah Ahmadiyah; Kelly Rossa Sungkono; Cahyaningtyas Sekar Wahyuni
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.10568

Abstract

The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities.