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Gap analysis business process model by using structural similarity Afrianda Cahyapratama; Kelly Rosa Sungkono; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp124-134

Abstract

Gap analysis process model is a study that can help an institution to determine differences between business process models, such as a model of Standard Operating Procedure and a model of activities in an event log. Gap analysis is used for finding incomplete processes and can be obtained by using structural similarity. Structural similarity measures the similarity of activities and relationships depicting in the models.  This research introduces a graph-matching algorithm as the structural similarity algorithm and compares it with dice coefficient algorithms. Graph-matching algorithm notices parallel relationships and invisible tasks, on the contrary dice coefficient algorithms only measure closeness between activities and relationships. The evaluation shows that the graph-matching algorithm produces 76.76 percent similarity between an SOP model and a process model generating from an event log; while, dice coefficient algorithms produces 70 percent similarity. The ability in detecting parallel relationships and invisible tasks causes the graph-matching algorithm produces a higher similarity value than dice coefficient algorithms.
Synonym based feature expansion for Indonesian hate speech detection Imam Ghozali; Kelly Rossa Sungkono; Riyanarto Sarno; Rachmad Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1105-1112

Abstract

Online hate speech is one of the negative impacts of internet-based social media development. Hate speech occurs due to a lack of public understanding of criticism and hate speech. The Indonesian government has regulations regarding hate speech, and most of the existing research about hate speech only focuses on feature extraction and classification methods. Therefore, this paper proposes methods to identify hate speech before a crime occurs. This paper presents an approach to detect hate speech by expanding synonyms in word embedding and shows the classification comparison result between Word2Vec and FastText with bidirectional long short-term memory which are processed using synonym expanding process and without it. The goal is to classify hate speech and non-hate speech. The best accuracy result without the synonym expanding process is 0.90, and the expanding synonym process is 0.93.