<![CDATA[An abstract is a brief summary of a paper to help readers quickly understand the purpose and relevance of the study. This research addresses the increasing use of gadgets at night among university students, which may affect sleep quality. Many previous studies relied on simple analytical approaches without optimizing the dataset, resulting in less representative findings. Therefore, this study aims to analyze and modify the analytical model of the effect of gadget usage on students’ sleep quality using a public dataset. A quantitative experimental approach was applied by comparing an initial model with a modified model. The dataset was obtained from Kaggle and analyzed using linear regression as the baseline model. Dataset modifications were conducted through data cleaning, normalization, and feature selection to improve model performance. The results indicate that the initial model did not provide optimal analytical outcomes, whereas the modified model produced more stable and representative results. These findings demonstrate that dataset and analytical model modifications play an important role in improving research outcomes, particularly in studies related to gadget usage behavior and student sleep quality.]]>
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