Manajemen Teaching Factory dengan Pendekatan Deep Learning dalam Meningkatkan Kompetensi Peserta Didik di SMKN 1 Cangkringan - Ningrum Pratiwi; Didi Supriadi; Rahmat Mulyono (Universitas Sarjanawiyata Tamansiswa, 2026)
This study aims to analyze Teaching Factory management using a Deep Learning approach in improving student competency at SMKN 1 Cangkringan. This study aims to describe the application of Teaching Factory management functions, analyze the effectiveness of integrating the Deep Learning approach in Teaching Factory learning, and identify supporting and inhibiting factors in improving student competencies, including hard skills, soft skills, and life skills. The background of this research is the demand for vocational education to produce competent, adaptive graduates who are in line with the needs of the business world, industry, and the world of work (DUDIKA). This study used a descriptive qualitative method with a case study approach. The study was conducted at SMKN 1 Cangkringan. The research subjects included the Principal, Vice Principal for Curriculum, Head of the APHP Vocational Program, APHP Productive Teachers, APHP Students, and DUDIKA Partners. The data sources consisted of primary and secondary data. Data collection techniques were conducted through in-depth interviews, observations, and documentation studies. Research instruments included interview guidelines, observation guidelines, and documentation study lists. Data analysis techniques used the Miles and Huberman model, namely data reduction, data presentation, and conclusion drawing, while data validity was tested using source triangulation and techniques. The results of the study show that SMKN 1 Cangkringan has integrated Teaching Factory management through management functions, namely planning, organizing, actuating, and controlling. The Deep Learning approach contributes to creating meaningful, conscious, and contextual learning with the principles of mindful, meaningful, and joyful learning, which is effective in improving student competence. Supporting factors include the commitment of the principal, teacher competence, DUDIKA support, and vocational education policies, while inhibiting factors include differences in student readiness and limitations in certain infrastructure. This study concludes that Teaching Factory Management with a Deep Learning approach is a relevant and effective vocational learning model, and it is recommended to be developed continuously through strengthening management and industry partnerships.