Articles
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
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DOI: 10.12928/telkomnika.v18i1.12982
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.
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
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DOI: 10.12928/telkomnika.v15i4.6326
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.
Asynchronous agent-based simulation and optimization of parallel business
Aziz Fajar;
Riyanarto Sarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v17i4.10846
A Port Container Terminal (PCT) involves complex business processes which are carried out by at least four organizations, namely PCT Operator, Customer, Quarantine and Customs. Each organization produces event log data from the activities. The event log data from the four organizations contain synchronous and asynchronous activities. In this research, the four organizations are represented by four agents. By simulating this log data using agent based simulation, we get the performance of the current business process. The performance indicators gathered are time and cost which are needed to do the activity (task). After the simulation is complete, we found Asynchronous Waiting Time (AWT). AWT is waiting time which happens because the agent in the simulation cannot do the newly assigned task because the agent is still working on the other task. Therefore, we parallelize the task performed by the agent so that the agent can do multiple tasks at a time. After we parallelize the task, we perform an optimization process using Stochastic Multicriteria Adaptability Analysis 2 (SMAA-2). Thus, the optimal amount of task an agent can do simultaneously is analyzed. This study result shows that parallelization can reduce AWT of the current system and the optimization process using SMAA-2 shows the most optimal number of multiple tasks an agent can do simultaneously.
AHP-TOPSIS for analyzing job performance with factor evaluation system and process mining
Gabriel Sophia;
Riyanarto Sarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v17i3.10408
Job performance is a type of assessments which refers to scalable actions, behaviour and outcomes that employees engage in or bring out linked with and contribute to organizational goals.This research employed the Factor Evaluation System (FES) method to analyze the job performance due to the common usage of the method. In analyzing employees, FES consists of nine factors; however, those nine factors are considered to be insufficient. Hence, the researchers used the process mining method to improve FES. Process mining analyzes job performance in details. The steps taken in process mining consist of time stamp, case, activity, and resources of employee. This means that the method can be continuously used, since the researcher provides weight for each factor. The weight of each factor is obtained from Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution. The result shows that FES with process mining are good for job performance but AHP-TOPSIS is considered to be incompatible for usage compared to the real work because the priority of the FES factors from the method is inconsistent with the priority factor made by manager of the warehouse officer.
Repair and Replacement Strategy for Optimizing Cost and Time of Warranty Process using Integer Programming
Ardy Januantoro;
Riyanarto Sarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v16i6.10407
Warranty is an assurance issued by a company as the manufacturer to guarantee that its product is damage-free within a specified period. The warranty process is usually carried out when a complaint or damage regarding the product is received. The warranty process consists of two decisions that the company establishes to handle the process. The occurring problem is in the warranty process; there is not any standard established to determine the cost to incur for the warranty process. In this research, integer programming method was used to do optimization on repair and replacement strategy in warranty process. Before doing optimization, mathematical model must be created. Using that mathematical model, the results show that the costs of the warranty process decrease by 16.97%, while the time increases by 13.9%. So, with this method company will be increase the profit.
Optimizing Time and Effort Parameters of COCOMO II using Fuzzy Multi-Objective Particle Swarm Optimization
Kholed Langsari;
Riyanarto Sarno;
Sholiq Sholiq
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v16i5.9698
Estimating the efforts, costs, and schedules of software projects is a frequent challenge to software development projects. A bad estimation will result in bad management of a project. Various models of estimation have been defined to complete this estimate. The Constructive Cost Model II (COCOMO II) is one of the most famous models as a model for estimating efforts, costs, and schedules. To estimate the effort, cost, and schedule in project of software, the COCOMO II uses inputs: Effort Multiplier (EM), Scale Factor (SF), and Source Line of Code (SLOC). Evidently, this model is still lack in terms of accuracy rates in both efforts estimated and time of development. In this paper, we introduced to use Gaussian Membership Function (GMF) of Fuzzy Logic and Multi-Objective Particle Swarm Optimization (MOPSO) method to calibrate and optimize the parameters of COCOMO II. It is to achieve a new level of accuracy better on COCOMO II. The Nasa93 dataset is used to implement the method proposed. The experimental results of the method proposed have reduced the error downto 11.89% and 8.08% compared to the original COCOMO II. This method proposed has achieved better results than previous studies.
Developing Distributed System with Service Resource Oriented Architecture
Hermawan Hermawan;
Riyanarto Sarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 2: June 2012
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v10i2.815
Service oriented architecture (SOA) is a design paradigm in software engineering for an enterprise scale which built in a distributed system environment. This paradigm aims at abstracting of application functionality as a service through a protocol in web service technology, namely simple object access protocol (SOAP). However, SOAP have static characteristic and oriented by the service methode, so have restrictiveness on creating and accessing for big numbers of service. For this reason, this reasearch aims at combining SOA with resource oriented architecture (ROA) that is oriented by the service resource use representational state transfer (REST) protocol in order to expand scalability of service. This combination is namely service resource oriented architecture (SROA). SROA can optimize distributing of applications and integrating of services where is implemented to develop the project management software. To realize this model, the software is developed according with framework of Agile model driven development (AMDD) to reduce complexities on the whole stage processing of software development.
Adopted topic modeling for business process and software component conformity checking
Adhatus Solichah Ahmadiyah;
Riyanarto Sarno;
Fony Revindasari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i6.13381
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.
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
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DOI: 10.12928/telkomnika.v19i6.19015
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.
Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method
Kholed Langsari;
Riyanarto Sarno;
Sholiq Sholiq
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v16i5.9703
Estimating the effort and cost of software is an important activity for software project managers. A poor estimate (overestimates or underestimates) will result in poor software project management. To handle this problem, many researchers have proposed various models for estimating software cost. Constructive Cost Model II (COCOMO II) is one of the best known and widely used models for estimating software costs. To estimate the cost of a software project, the COCOMO II model uses software size, cost drivers, scale factors as inputs. However, this model is still lacking in terms of accuracy. To improve the accuracy of COCOMO II model, this study examines the effect of the cost factor and scale factor in improving the accuracy of effort estimation. In this study, we initialized using Particle Swarm Optimization (PSO) to optimize the parameters in a model of COCOMO II. The method proposed is implemented using the Turkish Software Industry dataset which has 12 data items. The method can handle improper and uncertain inputs efficiently, as well as improves the reliability of software effort. The experiment results by MMRE were 34.1939%, indicating better high accuracy and significantly minimizing error 698.9461% and 104.876%.