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ComEngApp : Computer Engineering and Applications Journal
Published by Universitas Sriwijaya
ISSN : 22524274     EISSN : 22525459     DOI : 10.18495
Core Subject : Science, Engineering,
Computer Science & IT Engineering
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 1 Documents
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Search results for , issue "Vol. 6 No. 2 (2017)" : 1 Documents clear
Creating a Business Value while Transforming Data Assets using Machine Learning Dimitrovska, Ivana; Malinovski, Toni
Computer Engineering and Applications Journal (ComEngApp) Vol. 6 No. 2 (2017)
Publisher : Universitas Sriwijaya

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Abstract

Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia, while predicting real-value outcome from a given array of business inputs. The results show that most of the machine learning predictive values for the desired process output deviated from 0 to 15% of actual employees' decision. Hence, it verifies the appropriateness of the chosen approach, with predictive accuracy that can be meaningful in practice. As a machine learning case study in business context, it contains valuable information that can help companies understand the significance of machine learning for enterprise computing. It also points out some potential pitfalls of machine learning misuse.

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