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A Comparative Study of Classification and Clustering Methods for Data Analysis in Digital Transformation and IoT Systems

Turgay Tugay Bilgin1,
Süleyman Burak Altınışık2,
Nihat Aydın Adıgüzel3
1Bursa Teknik Üniversitesi
2Mert Yazılım
3Mert Yazılım
Received:Nov 16, 2023→Accepted:Dec 24, 2023→Published:December 31, 2023
DOI: 10.56038/oprd.v3i1.280
Vol. 3, No. 1

Abstract

This study employs classification and clustering methodologies on datasets derived from digital transformation and Internet of Things (IoT) initiatives within the cable and automotive sectors. The analytical procedures are conducted utilizing the KNIME platform, employing Support Vector Machines (SVM) and K-Means algorithms. The results indicate that SVM exhibits superior accuracy rates compared to K-Means within both industries. The data collection methodology facilitated by the Mert Software IoT platform is identified as reliable and efficacious. The primary objective of this article is to augment decision-making precision in digital transformation software and contribute to the scholarly discourse within this domain.

Keywords
Machine LearningClassificationCluster AnalysisIndustry 4.0.

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Cite This Article
Bilgin, T. T., Altınışık, S. B., Adıgüzel, N. A. (2023). A Comparative Study of Classification and Clustering Methods for Data Analysis in Digital Transformation and IoT Systems. *Orclever Proceedings of Research and Development*, 3(1), 1-18. https://doi.org/10.56038/oprd.v3i1.280

Bibliographic Info

JournalOrclever Proceedings of Research and Development
Volume3
Issue1
Pages1–18
PublishedDecember 31, 2023
eISSN2980-020X

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