Publication:
Predictive modeling on Telekom Malaysia berhad direct exchange line growth

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dc.Conferencecode83955
dc.Conferencedate28 September 2010 through 30 September 2010
dc.ConferencelocationBali
dc.Conferencename2nd International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2010
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Pendidikan Sultan Idris (UPSI)
dc.contributor.affiliationsUniversiti Utara Malaysia (UUM)
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorRasli R.M.en_US
dc.contributor.authorRasli R.M.en_US
dc.contributor.authorNorwawi N.Md.en_US
dc.date.accessioned2024-05-29T01:56:13Z
dc.date.available2024-05-29T01:56:13Z
dc.date.issued2010
dc.description.abstractData mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous data is derived. Telekom Malaysia Berhad (TM) is Malaysia's premier communications provider that provides the digital backbone and communication facilities. Direct Exchange Line (DEL) is one of its core telephony services that handle large volume and variety of data in its daily operations. Therefore, it is hard to reveal knowledge structures that can guide decisions in conditions of limited certainty. The main objective of this study is to identify the most appropriate DM techniques (logistic regression, decision trees and neural networks) for predicting DEL growth based on five physical attributes constitute of 672 instances leading to a target (either increase or decrease). The finding is important especially in assisting the prediction of DEL growth in Telekom, thus leading on gaining better understanding on the future of the market based on the current and previous situation. � 2010 IEEE.
dc.description.natureFinalen_US
dc.description.sponsorshipAsia Model. Simul. Soc. (AMSS), UK Simul. Soc. (UKSim)
dc.description.sponsorshipIEEE UK RI Comput. Chapter, Eur. Simul. Counc. (EUROSIM)
dc.description.sponsorshipEuropean Council for Modelling and Simulation (ECMS)
dc.description.sponsorshipUniv. Technol. Malaysia (UTM), Inst. Technol. Bandung (ITB)
dc.description.sponsorshipUniv. Sci. Malaysia (USM), Univ. Malaysia Sabah (UMS)
dc.identifier.ArtNo5701828
dc.identifier.doi10.1109/CIMSiM.2010.80
dc.identifier.epage105
dc.identifier.isbn9780770000000
dc.identifier.scopus2-s2.0-79952087411
dc.identifier.spage100
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79952087411&doi=10.1109%2fCIMSiM.2010.80&partnerID=40&md5=aef4da45b4aded7c03559ea2b72c73e4
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9809
dc.languageEnglish
dc.language.isoen_US
dc.relation.ispartofProceedings - 2nd International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2010
dc.sourceScopus
dc.subjectData miningen_US
dc.subjectDELen_US
dc.subjectPredictive modeling techniquesen_US
dc.subjectTelekom Malaysia Berhaden_US
dc.subjectCommunication facilitiesen_US
dc.subjectDELen_US
dc.subjectHistorical dataen_US
dc.subjectKnowledge structuresen_US
dc.subjectLarge databaseen_US
dc.subjectLimited certaintyen_US
dc.subjectLogistic regressionsen_US
dc.subjectMalaysiaen_US
dc.subjectNew technologiesen_US
dc.subjectPredictive informationen_US
dc.subjectPredictive modelingen_US
dc.subjectPredictive modeling techniquesen_US
dc.subjectPredictive modelsen_US
dc.subjectTelephony servicesen_US
dc.subjectData warehousesen_US
dc.subjectDecision treesen_US
dc.titlePredictive modeling on Telekom Malaysia berhad direct exchange line growth
dc.typeConference Paperen_US
dspace.entity.typePublication

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