<![CDATA[Human development must go hand in hand with improving quality of life, as reflected by HDI and its influencing factors. Classifying quality of life based on HDI into developed, developing, and underdeveloped areas offers insights into the human development performance of each district/city in Indonesia. The CatBoost Classifier and SHAP values help build an accurate model while interpreting variable influences. This study analyzes human quality of life classification across districts/cities based on HDI and related factors. The CatBoost model achieved 92.23% accuracy, with the best performance in the developing class, while the underdeveloped class showed low accuracy due to data imbalance. SHAP analysis revealed that average years of schooling, per capita expenditure, and region type were key variables in the developed and developing classes, while island location and sanitation access dominated in the underdeveloped class. These findings highlight the importance of education, economic welfare, and basic infrastructure in shaping quality of life. This research also supports actuarial social risk planning, particularly in designing data- and region-based social security systems.]]>
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