Comparative Analysis of Clustering Approaches in Assessing ChatGPT User Behavior

Dedy Setiawan; Daniel  Arsa; Lucky Enggrani Fitri; Farah Fadhila Putri Zahardy

doi:10.31154/cogito.v10i2.661.366-379

Authors

Dedy Setiawan Universitas Jambi
Daniel Arsa Universitas Jambi
Lucky Enggrani Fitri Universitas Jambi
Farah Fadhila Putri Zahardy Universitas Jambi

DOI:

https://doi.org/10.31154/cogito.v10i2.661.366-379

Keywords:

ChatGPT, K-Means, K-Medoids, UTAUT, Silhouette Score

Abstract

ChatGPT is an artificial intelligence technology that is widely used and discussed. The technology invites mixed responses from various parties, mainly because of the benefits and risks of its use in multiple fields. Jambi University students also feel the influence of ChatGPT's presence in education. To determine the behavior of Jambi University students in using ChatGPT, four UTAUT variables were used, namely Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC) as independent variables in measuring the behavior of using ChatGPT. Where UTAUT states these four variables have a positive influence on the actual behavior of technology use. This study used K-Means and K-Medoids Clustering to group Jambi University students based on ChatGPT usage behavior. Based on the Silhouette Score calculation, each method's optimal number of clusters is 2. K-Means is considered more optimal in forming 2 clusters because it obtained a Silhouette Score of 0.2123864, higher than K-Medoids, which is 0.1766865.

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