<![CDATA[The rapid development of information technology has encouraged the use of spatial data in various fields, including in education. This study aims to analyze the application of classification algorithms in the learning of geographic information systems (GIS) at Citra Bangsa University. The main focus lies in how students understand and implement algorithms such as K-Nearest Neighbor (K-NN), Decision Tree, and Naive Bayes in the context of spatial data. The approach used is descriptive qualitative with data collection techniques through an open questionnaire to 26 students of Citra Bangsa University, who followed project-based GIS learning. The results of the study show that the use of classification algorithms can improve students' understanding of basic GIS concepts, especially in spatial data processing and visualization. And also the results obtained from 26 students with different percentage points for the questionnaire given. Nonetheless, some obstacles were found such as technical difficulties in the data classification process and low initial understanding of algorithm concepts. This study provides an overview of the importance of integrating the concept of algorithms and GIS in the learning process to improve students' competencies in the field of spatial technology]]>
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