TELKOMNIKA (Telecommunication Computing Electronics and Control)
Business processes and software components, especially class diagrams, have a firm connection. Co... more Business processes and software components, especially class diagrams, have a firm connection. Considering software components support the business process in providing an excellent product and service. Besides, business process changes affect on software component design. One of them usually appears on the label or name of the software component or business process. Sometimes, a related business process and software component appears in the different label but the same meaning rather than using the same label. This situation is problematic when there are many changes to be made, in which the software component's modifying process becomes quite long. Therefore, the software maintainers should obtain an efficient procedure to shorten the modifying process. One solution is by using conformity checking, which helps the software maintainers know which software component is related to a specific business process. This paper compared two leading topic modeling techniques, namely probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), to determine which one has a better performancefor process traceability.
TELKOMNIKA (Telecommunication Computing Electronics and Control)
Business processes are implemented in an organization. When a business process is run, it generat... more 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.
TELKOMNIKA (Telecommunication Computing Electronics and Control)
In this context, this paper proposes a combination of parameterised decision mining and relation ... more 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.
Indonesian Journal of Electrical Engineering and Computer Science
The issues measures duration of stay the container logistic processes at ports in developing coun... more The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The L* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.
Indonesian Journal of Electrical Engineering and Computer Science
The achievement of accepting optimal tax need effective and efficient tax supervision can be achi... more The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS ba...
Much corporate organization nowadays implement enterprise resource planning (ERP) to manage their... more Much corporate organization nowadays implement enterprise resource planning (ERP) to manage their business processes. Because the processes run continuously, ERP produces a massive log of processes. Manual observation will have difficulty monitoring the enormous log, especially detecting anomalies. It needs the method that can detect anomalies in the large log. This paper proposes the integration of process mining, fuzzy multi-attribute decision making and fuzzy association rule learning to detect anomalies. Process mining analyses the conformance between recorded event logs and standard operating procedures. The fuzzy multi-attribute decision making is applied to determine the anomaly rates. Finally, the fuzzy association rule learning develops association rules that will be employed to detect anomalies. The results of our experiment showed that the accuracy of the association rule learning method was 0.975 with a minimum confidence level of 0.9 and that the accuracy of the fuzzy a...
TELKOMNIKA (Telecommunication Computing Electronics and Control)
Business processes and software components, especially class diagrams, have a firm connection. Co... more Business processes and software components, especially class diagrams, have a firm connection. Considering software components support the business process in providing an excellent product and service. Besides, business process changes affect on software component design. One of them usually appears on the label or name of the software component or business process. Sometimes, a related business process and software component appears in the different label but the same meaning rather than using the same label. This situation is problematic when there are many changes to be made, in which the software component's modifying process becomes quite long. Therefore, the software maintainers should obtain an efficient procedure to shorten the modifying process. One solution is by using conformity checking, which helps the software maintainers know which software component is related to a specific business process. This paper compared two leading topic modeling techniques, namely probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), to determine which one has a better performancefor process traceability.
TELKOMNIKA (Telecommunication Computing Electronics and Control)
Business processes are implemented in an organization. When a business process is run, it generat... more 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.
TELKOMNIKA (Telecommunication Computing Electronics and Control)
In this context, this paper proposes a combination of parameterised decision mining and relation ... more 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.
Indonesian Journal of Electrical Engineering and Computer Science
The issues measures duration of stay the container logistic processes at ports in developing coun... more The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The L* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.
Indonesian Journal of Electrical Engineering and Computer Science
The achievement of accepting optimal tax need effective and efficient tax supervision can be achi... more The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS ba...
Much corporate organization nowadays implement enterprise resource planning (ERP) to manage their... more Much corporate organization nowadays implement enterprise resource planning (ERP) to manage their business processes. Because the processes run continuously, ERP produces a massive log of processes. Manual observation will have difficulty monitoring the enormous log, especially detecting anomalies. It needs the method that can detect anomalies in the large log. This paper proposes the integration of process mining, fuzzy multi-attribute decision making and fuzzy association rule learning to detect anomalies. Process mining analyses the conformance between recorded event logs and standard operating procedures. The fuzzy multi-attribute decision making is applied to determine the anomaly rates. Finally, the fuzzy association rule learning develops association rules that will be employed to detect anomalies. The results of our experiment showed that the accuracy of the association rule learning method was 0.975 with a minimum confidence level of 0.9 and that the accuracy of the fuzzy a...
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