<![CDATA[Student interest in Informatics is still commonly identified through teacher observation, creating a need for a more objective data-driven approach. This study develops an XGBoost classification model and explains the factors underlying its decisions using SHAP. A quantitative data-analytics design followed the CRISP-DM stages using 27 student records and seven features: assignment scores, daily tests, practical scores, midterm scores, final scores, attendance, and attitude. The dataset was split into 80% training and 20% testing data. On six held-out cases, the model correctly classified two students as Interested and four as Not Interested, producing 100% Accuracy, Precision, Recall, and F1-Score, with an AUC of 1.00. SHAP analysis identified daily-test scores as the strongest contributor (1.1179), followed by assignment, final-exam, practical, and midterm scores. These findings indicate that XGBoost combined with SHAP can support measurable and transparent identification of student interest, although the limited dataset requires caution when generalizing the performance.]]>
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