Thamrin, Geovanne Farell, Sartika Anori, Putra Jaya, Nalurry Emelsy
Vocational High Schools play a vital and strategic role in preparing students for the modern workforce, whether in the industrial sector or through entrepreneurship. However, challenges such as low labor absorption and growing interest in entrepreneurship necessitate innovative solutions to support career decision-making for graduates. The Career Path Optimization System, powered by Content-Based Filtering and Machine Learning, addresses this need by providing tailored career recommendations based on individual profiles. Using a DevOps approach, the system ensures seamless integration and functionality. It analyzes user preferences, skills, and career goals to suggest relevant job opportunities, training programs, and guidance for independent ventures, optimizing various factors throughout the development process. The system empowers graduates with informed career choices and supports sustainable career growth. Before its implementation, the average student satisfaction score was 3.20 (standard deviation 0.80), and the system usage capability score averaged 2.90 (standard deviation 0.90). After the system's introduction, significant improvements were recorded: the average satisfaction score rose to 4.10 (standard deviation 0.70), and the system usage capability score increased to 3.60 (standard deviation 0.80). These results demonstrate the system's effectiveness in enhancing user satisfaction and usability. By leveraging advanced technology, the Career Path Optimization System not only guides students toward suitable career paths but also improves their readiness for the job market or entrepreneurial ventures, ultimately contributing to their long-term career success. © (2025), American Psychological Association
Department of Electronics Engineering, Universitas Negeri Padang, Padang, Indonesia
