<![CDATA[Amikom University Yogyakarta, has a Master of Informatics Engineering study program with three concentrations of specialization: Business Intelligence, Digital Information Intelligence, and Intelligence Animation. The choice of concentration by prospective students has been based on subjectivity, not on competence or work experience. To overcome this, this research proposes an algorithm-based concentration prediction and recommendation model to help prospective students choose the appropriate concentration. The dataset is obtained through questionnaires collected from active and inactive students. This research uses the K-Means algorithm for clustering raw data (unsupervised) in order to generate target classes, which are then classified using Naïve Bayes. The clustering process determines concentration labels such as Business Intelligence and others, while the SMOTE technique is used to balance the dataset to avoid data imbalance problems. This approach aims to produce more objective and accurate recommendations in determining student concentrations, reducing the tendency of subjectivity, and increasing the relevance of student competencies to the chosen field of specialization. From this research, the K-Means DBI score is 0.277 and the Naïve Bayes prediction accuracy score is 89%. This research aims to produce more objective and accurate recommendations in determining student concentrations, reducing subjectivity, and increasing the relevance of student competencies to the chosen field of specialization. The proposed model is expected to help universities in designing a more targeted admission strategy, as well as supporting students in making academic decisions that are in accordance with their abilities and interests, thereby increasing the effectiveness of the learning process and the suitability of graduates to the needs of the world of work.]]>
