<![CDATA[This study aims to categorise and analyse the perceptions of female students graduating from Kulliyatul Mu'allimat al-Islamiyyah (KMI) towards the UNIDA Regular community service programme using the K-Means Clustering algorithm. The categorisation was conducted to determine the level of acceptance among female students in an objective, efficient, and transparent manner as a basis for evaluation and development of future community service programmes. The research method used the CRISP-DM framework, which includes the stages of data understanding, data preparation, modelling, evaluation, and deployment. Primary data was obtained from a five-point Likert scale questionnaire completed by 561 respondents. The analysis was carried out through data cleaning and normalisation, modelling with the K-Means algorithm, determining the optimal number of clusters using the Elbow Method, and validation with the Silhouette Score. The results showed that the optimal number of clusters was three, namely positive (25%), neutral (41%), and negative (34%). The positive cluster had high motivation and attitude, the neutral cluster showed moderate scores with weak social aspects, while the negative cluster was low in almost all variables, especially motivation and experience. The Silhouette Score value of 0.61 indicates that the clustering quality is good. This study proves that the application of K-Means Clustering is effective in mapping female students' perceptions systematically and accurately. The results provide practical input for UNIDA to strengthen motivation, social support, and coaching and mentoring strategies so that the community service programme is received more positively]]>
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