Розробка гібридної моделі інформаційного пошуку та графу знань для узгодження компетенцій між різними фреймворками

The object of this study is the alignment of Indonesian computing graduate learning outcomes (CPL – Capaian Pembelajaran Lulusan) with three heterogeneous competency frameworks: the European skills, competences, qualifications and occupations (ESCO), the occupational information network (O*NET), and the Standar Kompetensi Kerja Nasional Indonesia (SKKNI). The problem addressed is the absence of a unified pipeline capable of simultaneously mapping CPL across these frameworks while accounting for national qualification hierarchies and cross-lingual constraints. An IR-KG (information retrieval–knowledge graph) model is proposed with a seven-stage pipeline using a hybrid scoring function S_final = α·S_sem + β·S_gr + γ·S_con, integrating TF-IDF (term frequency–inverse document frequency) semantic similarity, ESCO knowledge graph cohesion, and domain constraint scores from ISCED-F 2013 (International Standard Classification of Education: Fields of Education and Training) and APTIKOM 2022 (Asosiasi Pendidikan Tinggi Informatika dan Komputer) classifications. The balanced configuration (α = β = γ = 0.33) achieves a mean selection objective of 0.537, a 26.1% improvement over the semantic baseline. External consistency validation yields a relaxed consistency rate of 27.1% (8.7× above random baseline), confirming valid alignment signal capture. The CRI-KG (Career Readiness index based on knowledge graph) reveals a gradient R_SKKNI >> R_ONET > R_ESCO, exposing persistent gaps in international framework coverage. The pipeline is applicable for curriculum audit, qualification recognition policy, and national-to-international framework integration where labelled training data are unavailable