Rancang Bangun Sistem Identifikasi Varietas Tebu Menggunakan Kemiripan D-WDAG

Authors

  • Adi Heru Utomo
  • Abd. Charis Fauzan

Abstract

Dynamic wDAG Similarity algorithm can be applied to sugarcane annotation. At first, we have to make a wDAG structure of
many different varieties of sugarcane. We also have to make wDAG of sugarcane that will be annotated. Then, we have to
calculate the similarity between wDAG types of sugarcane that will be annotated and wDAG of all the existing types of sugarcane.
This similarity calculation results will present sequence similarities ranging from the most similar to the most distant from
sugarcane varieties were annotated. This Dynamic wDAG Similarity algorithm has difference compared with the previous wDAG
Similarity algorithm. WDAG used in this research has the node labeled , arc labeled and arc weighted, where the weight of the
arc can be changed dynamically. This research fixes the previous studies of static wDAG, in which the weight values on the arc of
wDAG can not be changed. On Dynamic wDAG, the weight on each arc is based on the fuzzy calculations that show the tendency
of sugarcane varieties were annotated. And the fuzzy value is calculated based on agronomic traits of sugarcane to be annotated.
Leaf node is the part of wDAG that will be compared first. The similarity calculation result between the two wDAG is affected by
data on a leaf node to be compared and the weights of the arcs. The result shows that this method gained the average of Precision
of 96%, the average of Recall of 88.5%, and the average of Accuracy of 96%.

Keywords— Dynamic wDAG, Sugarcane classification, Sugarcane variety identification, wDAG similarity.

References

H.A. Utomo, R. Sarno, R.V.H. Ginardi.†Sugarcane Variety

Identification Using Dynamicâ€. 2016 International Conference on

Information, Communication Technology and System (ICTS).

Weighted Directed Acyclic Graph Similarityâ€

R. Sarno, Endang Wahyu Pamungkas, Dwi Sunaryono, Sarwosri.

“Business Process Composition Based On Meta Modelsâ€. Intelligent

Technology and Its Applications (ISITIA), 2015 International

Seminar. Page(s) :315 – 318, ISBN: 978-1-4799-7710-9

R. Sarno, Bandung Arry Sanjoyo, Imam Mukhlash, Hanim Maria

Astuti, “Petri Net Model of ERP Business Process Variations for

Small and Medium Enterprisesâ€, Journal of Theoretical and Applied

Information Technology, 54 (1), pp. 31-38. 2013.

R. Sarno, Putu Linda Indita Sari, Hari Ginardi, Dwi Sunaryono, Imam

Mukhlash, “Decision mining for multi choice workflow patternsâ€.

International Conference on Computer, Control, Informatics and

Its Applications (IC3INA). Pages: 337 – 342, DOI:

1109/IC3INA.2013.6819197

R. Sarno, Endang Wahyu Pamungkas, Dwi Sunaryono, Sarwosri,

“Workflow Common Fragments Extraction based on WSDL

Similarity and Graph Dependencyâ€. International Seminar on

Intelligent Technology and Its Applications (ISITIA) 2015.

https://doi.org/10.1109/isitia.2015.7219997

Aalst, W.M.P. van der. “The Application of Petri Nets to Workflow

Management. Journal of Circuit, Systems and Computersâ€, Vol. 8,

No. 1, p. 21-66, 1998.

A.J.M.M. Weijters, W.M.P. var der Aalst and A.K. Alves de

Medeiros. (n.d.). “Process Mining with the Heuristics Miner

Algorithmâ€. Eindhoven, Netherland: Eindhoven University of

Technology.

P. Weber, “A Framework for The Comparison of Process Mining

Algorithmsâ€,School of Computer Science University of Brimingham

pp.1, 2009.

S. Huda, R. Sarno and Tohari Ahmad,“Increasing Accuracy of

Process-based Fraud Detection Using a Behavior Model", Surabaya,

International Journal of Software Engineering and Its Applications,

August 2013.

W.M.P. Van Der Aalst, “Process Mining:Discovery, Conformance

and

Enhancement of Business Processesâ€, Netherlands, Springer, 2011.

R. Sarno., Hari Ginardi, Endang Wahyu Pamungkas, Dwi Sunaryono.

Clustering of ERP Business Process Fragments. International

Conference on Computer, Control, Informatics and Its Applications

(IC3INA), 2013. https://doi.org/10.1109/ic3ina.2013.6819194

Dijkman, R., Marlon Dumas, Boudewijn van Dongen, Reina Käärik,

Jan Mendling. Similarity of business process models: Metrics and

evaluation. Information Systems, Volume 36, Issue 2, April 2011,

Pages 498-516

Leonard, Michael, et al. 2005. An Introduction to Similarity Analysis

Using SAS. SAS Institute Inc., Cary, NC.

Dumas, M., Garcıa-Banuelos, L., Dijkman, R.M.: Similarity Search

of Business Process Models. IEEE Data Eng. Bull. 32(3) (2009) 2328

Published

2018-02-08