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All Journal International Journal of Electrical and Computer Engineering Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of ICT Research and Applications Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Electrical Engineering and Computer Science Idealis : Indonesia Journal Information System Jurnal Ilmiah Kajian Keimigrasian (JIKK)
Jaka Sembiring
Institut Teknologi Bandung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Mixed Data Type Analysis: A Systematic Literature Review Pratama, Hasta; Fitriyanti Lubis, Fetty; Sembiring, Jaka
IDEALIS : InDonEsiA journaL Information System Vol. 7 No. 2 (2024): Jurnal IDEALIS Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v7i2.3168

Abstract

This research aims to determine the direction of research in the analysis of mixed data types. The world is currently filled with increasingly diverse data, especially in terms of data types, which are not only numerical or categorical but can be both (mixed). In Data Mining, the analysis of mixed data poses significant challenges because numerical and categorical data exhibit different properties. The research methodology employed in this study utilizes the PICOC framework (Population, Intervention, Comparison, Outcome, Context) to collect and review relevant literature. The primary findings from this comprehensive literature survey reveal that a majority of the research related to mixed data is published in reputable journals Q1, indicating sustained interest in the topic of mixed data analysis. Clustering models emerge as the most frequently used models in the field of mixed data analysis. However, it's noteworthy that accuracy metrics remain the predominant evaluation benchmark, often leading to comparisons with the ideal clustered data. The management of mixed data typically involves normalization techniques, specifically normalizing the scale to amalgamate the two types of data. The conclusion drawn from the results of the literature review is the necessity to develop unlabeled mixed data, encompassing both the model and metrics required to assess the outcomes. Additionally, this research emphasizes the significance of a comprehensive development model, ranging from feature selection to evaluation models. Therefore, the analysis of mixed data types remains a field with ample opportunities for exploration and potential innovation. This potential is particularly evident in the areas of dynamic model development and the ability to handle structured and extensive data.
Co-Authors A.I. Wuryandari A.S. Ahmad Aan Subrata Albarda Albarda Ana Uluwiyah Arry A. Arman Arwin Datumaya Wahyudi Sumari Fetty Fitriyanti Lubis Fitasari Wiharni I Made Ariya Sanjaya Johan P. Sipayung Kurniawan, Novianto Budi Kurniawan, Novianto Budi KUSPRIYANTO Manurung, Raphael Fransiskus Nanang Hariyanto Novianto Budi Kurniawan Nur Iksan Pratama, Hasta Subrata, Aan Suhardi Suhardi Suhardi, Suhardi Suhardi, Suhardi Suhono Harso Supangkat Widya Liana Aji Yoanes Bandung