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dc.contributor.author Skorikov, M
dc.contributor.author Hussain, MA
dc.contributor.author Khan, MR
dc.contributor.author Akbar, MK
dc.contributor.author Momen, S
dc.contributor.author Mohammed, N
dc.contributor.author Nashin, T
dc.contributor.editor Karunananda, AS
dc.contributor.editor Talagala, PD
dc.date.accessioned 2022-11-14T09:32:03Z
dc.date.available 2022-11-14T09:32:03Z
dc.date.issued 2020-12
dc.identifier.citation M. Skorikov et al., "Prediction of Absenteeism at Work using Data Mining Techniques," 2020 5th International Conference on Information Technology Research (ICITR), 2020, pp. 1-6, doi: 10.1109/ICITR51448.2020.9310913. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19508
dc.description.abstract High absenteeism among employees can be detrimental to an organization as it can result in productivity and economic loss. This paper looks into a case of absenteeism in a courier company in Brazil. Machine learning techniques have been employed to understand and predict absenteeism. Understanding this would provide human resource managers an excellent decision aid to create policies that can aim to reduce absenteeism. Data has been preprocessed, and several machine learning classification algorithms (such as zeroR, tree-based J48, naive Bayes, and KNN) have been applied. The paper reports models that can predict absenteeism with an accuracy of over 92%. Furthermore, from an initial of 20 attributes, disciplinary failure turns out to be a very prominent feature in predicting absenteeism. en_US
dc.language.iso en en_US
dc.publisher Faculty of Information Technology, University of Moratuwa. en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9310913/ en_US
dc.subject Absenteeism en_US
dc.subject Prediction en_US
dc.subject Data mining en_US
dc.subject Classification en_US
dc.title Prediction of absenteeism at work using data mining techniques en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2020 en_US
dc.identifier.conference 5th International Conference in Information Technology Research 2020 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.proceeding Proceedings of the 5th International Conference in Information Technology Research 2020 en_US
dc.identifier.doi 10.1109/ICITR51448.2020.9310913 en_US


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  • ICITR - 2020 [27]
    International Conference on Information Technology Research (ICITR)

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