<![CDATA[p The development of digital technology has driven the transformation of micro small and medium enterprises MSMEs towards utilizing e commerce platforms as the primary means of marketing products and services However the main challenges faced by MSMEs in utilizing digital platforms lie in the aspects of usability and user experience UX which significantly influence the level of user satisfaction and the sustainability of application use Usability and UX evaluations are traditionally conducted through questionnaire surveys and interviews but these methods have limitations in terms of objectivity cost and time This study proposes the use of deep learning to evaluate usability and UX aspects automatically through analysis of user interactions such as click patterns response times cursor movements and facial expressions or eye tracking The method used in this study involved collecting data from 200 active users of an MSME e commerce platform in Indonesia User interaction data was recorded through a logging system and analyzed using CNN and RNN models CNN was used for visual analysis including screen display patterns and facial expressions while RNN was used to learn the sequence of user interactions on the platform The usability and UX evaluation results were then categorized into high medium and low satisfaction levels Validation was carried out by comparing the results of the deep learning model with the results of the System Usability Scale SUS questionnaire survey with an accuracy rate of 92 3 Persen In conclusion the use of deep learning in usability and UX evaluation provides a more comprehensive faster and more accurate approach than traditional methods This research is expected to form the basis for developing an automated evaluation framework to support the sustainable digital transformation of MSMEs in Indonesia p]]>
