<![CDATA[Fresh graduates face various challenges in competing for employment opportunities, especially due to the intense competition for limited job positions in the labor market. Both individuals as job seekers and organizations as employers must consider multiple complex factors during the recruitment process. These factors include GPA, university background, aptitude test score, number of projects, internship experience, and academic performance at both the secondary (SSC) and higher secondary (HSC) levels. This study aims to classify job placement eligibility using machine learning methods with algorithms such as Naïve Bayes, Deep Learning, Random Forest, and K-Nearest Neighbors (KNN). The experimental results indicate that the Naïve Bayes algorithm achieved the highest accuracy at 81.90%, followed by Deep Learning with 80.20%. These findings suggest that despite its simplicity, Naïve Bayes is highly effective for datasets with relatively independent and well-distributed features. Meanwhile, other algorithms such as KNN and Decision Tree provided competitive results but showed less consistency in generalizing data. For future research, it is recommended to use a larger and more diverse dataset and to consider additional features such as informal work experience, organizational involvement, or personality data to improve prediction accuracy. Furthermore, exploring ensemble learning or hybrid modeling approaches may lead to more optimal classification outcomes.]]>
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