Descriptive Modeling Uses K-Means Clustering for Employee Presence Mapping

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Author(s)

Warnia Nengsih 1,* Muhammad Mahrus Zain 1

1. Information Technology of Department, Pekanbaru,28265 Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2020.04.02

Received: 21 Aug. 2019 / Revised: 23 Oct. 2019 / Accepted: 7 May 2020 / Published: 8 Aug. 2020

Index Terms

Descriptive Modelling, K-Means, Clustering

Abstract

Human resource is valuable asset for an agency. The success of an institution is not only determined by the quality of its human resources, but also by the level of discipline. The discipline of an employee in an institution can be seen and measured by the level of attendance in doing a job, because the level of attendance is one of the factors that determine productivity. The current problem is the management level of the company that has difficulty in monitoring and controlling the employee attendance data. There needs to be a mapping and grouping to find out patterns of absence. Mapping or patterns that are obtained help management levels to monitor employees, take approaches and take action so as to improve employee discipline. In this study, it was used descriptive modeling with the implementation of the k-means clustering method. The results of the mapping obtained help the management level in controlling and monitoring as a reference for the next policy maker.

Cite This Paper

Warnia Nengsih, Muhammad Mahrus Zain, "Descriptive Modeling Uses K-Means Clustering for Employee Presence Mapping", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.4, pp. 15-20, 2020. DOI:10.5815/ijieeb.2020.04.02

Reference

[1]MacQueen, J. B., "Some Methods for classification and Analysis of Multivariate Observations". Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. 1. University of California Press. March 2013, pp. 281–297. MR 0214227. Zbl 0214.46201. Retrieved 2009-04-07.
[2]Madhuri V. Joseph, "Significance of Data Warehousing and Data Mining in Business Applications", International journal of Soft Computing and Engineering, March 2013, Vol No:3,Issue no:.
[3]C. Zhang, and Z. Fang, "An improved k-means clustering algorithm", Journal of Information & Computational Science, 10(1), 2013, 193-199
[4]Fahad, A, Alshatri, N., Tari, Z., AlAmri, A., Zomaya, Y., Khalil, I., Foufou, S., Bouras, A, "A Survey of Clustering Algorithms for Big Data: Taxonomy & Empirical Analysis," Emerging Topics in Computing, IEEE Transactions on,2014 ,vol.PP, no.99, pp.1,1.
[5]Mortenson, M. J., Doherty, N. F., & Robinson, S. (2014). OperationalresearchfromTaylorism to terabytes:aresearch agenda for the analyticsage. European Journal of OperationalResearch, 583-595.
[6]SAP. SAP HANA Marketplace. Retrievedfrom SAP : http://marketplace.saphana.com SAP. (2014, 05 31). SAP HANA partner race`. Retrievedfrom SAP : http://global.sap.com/germany/campaigns/2 012_inmemory/partner-race/race.epx S
[7]Kwame Boakye Agyapongn, DR.J.B Hayfron-Acquah " An overview of Data Mining Models(Decsriptive and Predictive") International Journal of Software and Hardware Research in Enginering Volume 4 Issue 5 May 2016
[8]Shalini S Singh & N C Chauhan, “K- means v/s K- medoids: A Comparative Study”, National Conference on Recent Trends in Engineering & Technology, 2011.
[9]Anil K. Jain, “Data clustering: 50 years beyond Kmeans”, 19th International Conference in Pattern Recognition,2009.
[10]Arora, Deepali, Varshney, "Analysis of K-Means and K-Medoids Algorithm For Big Data", International Conference on Information Security & Privacy (ICISP2015), 2015.
[11]H. Jiawei,M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques", San Francisco California, Morgan Kaufmann Publishers, 2012.