Model Rekomendasi Karier Lulusan Sekolah Menengah Kejuruan Berdasarkan Kompetensi dan Bakat Menggunakan Perbandingan Algoritma Apriori dan FP-Growth - Tarwan; Eko Aji Putra; Arief Wibowo (Jurnal Algoritma, 2026)
The increasingly dynamic development of the job market requires Vocational High School (SMK) graduates to possess adaptive abilities, not only in mastering vocational competencies but also in determining appropriate career paths. However, in reality, many SMK graduates still experience difficulties in choosing careers that align with their competencies and talents. This condition highlights the need for a systematic approach capable of providing data-driven career recommendations. This study aims to develop a data-based career recommendation system using the Apriori and FP-Growth algorithms to identify relationship patterns among vocational competencies, students’ academic talents, and alumni tracer study data. The study offers a new approach to career recommendation systems for Vocational High Schools by integrating students’ academic data and alumni post-graduation histories (tracer studies) within a single pattern analysis framework. In addition to generating association rules that can easily be used as a basis for decision-making, the system also incorporates validation from guidance and counseling teachers (BK teachers) to strengthen data-driven career decisions. Talents are classified into two categories, namely exact/science-oriented and non-exact/non-science-oriented, based on comparisons between average Mathematics grades and non-science subject grades from semesters 1 to 6. Alumni tracer data include post-graduation status (employment, higher education, or others), job relevance, competency certificates, and the positions or work sectors pursued. Subsequently, each student and alumni entity was transformed into transactional data analyzed using the Apriori and FP-Growth algorithms to discover association rules between student profiles and career recommendations. The analysis results indicate strong relationships between combinations of talents and vocational competencies with specific career choices. The inclusion of data from guidance and counseling teachers serves as qualitative input that strengthens the validity of the system’s results. This system can be utilized by schools, guidance counselors, and students as a decision-support tool for making more objective and data-driven career decisions. Therefore, the system supports a vocational education direction that is more integrated with labor market needs.