Analyzing User Needs and Recommending Targeted Features for Bi’ih Village Tourism Website Using Text Mining and K-Means Clustering - Artamevia, Mima; Lubis, Muharman; Mukti, Iqbal Yulizar; Handayani, Dini (Jurnal Teknik Informatika (Jutif), 2025)
Tourism village websites often do not fully reflect user needs, resulting in digital services that cannot be optimally utilized by residents and potential tourists. This situation limits access to information and reduces the effectiveness of tourism promotion efforts, especially in villages that are undergoing digital transformation. This study was conducted to identify the overall needs of users and compile data-based feature recommendations for the development of the Bi'ih Village website as a durian tourism village. The research method used a quantitative approach through the distribution of an online questionnaire to 110 respondents consisting of visitors and residents, with five open-ended questions and several structured questions. The data was analyzed using text mining to find dominant words and themes, as well as the K-Means Clustering technique determined through the Elbow method to group user characteristics. The analysis results showed that there were 2,702 tokens and 677 meaningful words, with the highest demand for government information and visual tourism content. The segmentation process produced three main groups, namely Active Supporters (61.4%), Tech Enthusiasts (27.3%), and Moderate Users (11.4%). This study contributes a data-driven approach to designing more relevant and measurable features for tourism village websites. The impact is expected to increase the adoption of village digital services, strengthen tourism competitiveness, and support the acceleration of the Smart Village concept implementation. The novelty of this study lies in the integration of text mining and clustering as the basis for developing user-oriented feature recommendations.