Sieve Diagram For Data Exploration of Instagram Usage Habit Obtained From Indonesia Questioner’s Sample

Abstract

Exploration data using a scatter plot made it more accessible when the datasets correlated. However, the case of Instagram Usage Habit in the previous study was hard to follow. The problem was that many datasets were not specific. Therefore, it difficult to classification for a Decision Tree. The other option of exploration data used the sieve diagram. The sieve diagram summarizes the relationship between the categorical variables using frequencies—the purpose of this study to understand the data and found out what wrong in the datasets. Based on the result of the sieve diagram in the study, the main problem found out in the age of the respondent on attributes. We deduce that several attributes had not characteristic unique for the habit of using Instagram because the attributes based on age have a similar pattern. We suggest that the questions for respondents need to be improved, such as Tiered questions. Therefore, The classification of decision trees would become more precise in the subsequent studies.

A Proposed Model for E-learning Adaptability Measurement During COVID-19 Pandemic Using Data Mining Techniques

“…This paper utilizes Sieve diagram to explore the dataset. Sieve Diagram is graphical representation of frequencies in contiguous table [19]. It utilizes frequencies to outline the links between categorical variables [19].…”

“…Sieve Diagram is graphical representation of frequencies in contiguous table [19]. It utilizes frequencies to outline the links between categorical variables [19]. In addition, it uses color to represent positive and negative deviation from independence.…”

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Pemetaan Mosaic Plot dalam Menganalisis Fundamental Saham Perusahaan pada Aplikasi IPOT IndoPremier Securitas

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