<![CDATA[Scholarships play a crucial role in enhancing student motivation and academic achievement. However, the selection process often faces challenges in objectively determining eligibility. This study aims to develop an Artificial Intelligence-based scholarship recommendation system using a fuzzy logic approach to address uncertainty and variation in academic grades. The main contribution of this research is improving the objectivity and adaptability of the scholarship selection system through a fuzzy logic approach capable of assessing eligibility in a gradual and proportional manner. Data were collected from various publicly published scholarship agencies, including GPA, grade point average, and other academic requirements. The method used involves fuzzification to convert numerical data into fuzzy sets, applying fuzzy rules in the inference process, and defuzzification to generate consistently interpretable recommendation scores. The system was built using Python on the Google Colab platform and evaluated through white-box testing to ensure all internal logic flows work as designed. The results show that the fuzzy approach can provide more adaptive and objective recommendations compared to fixed threshold-based selection methods. The practical benefit of this system is to provide scholarship institutions with a transparent, efficient decision support tool that reduces subjectivity in the selection process.]]>
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