Python-Based Mapping of Productive Technical Teachers Using Google Colab in Tuban’s Vocational High Schools

Abstract

The availability of productive subject teachers is a crucial factor in ensuring the quality of vocational education, particularly at the level of Vocational High Schools (SMK). This report presents the results of a mapping study on the adequacy of productive teachers in technical departments at public SMKs in Tuban Regency, under the supervision of the Regional Education Office of Bojonegoro. The study aims to identify the distribution of productive teachers across departments, assess their adequacy based on the ideal minimum requirements, and determine whether there is a shortage or surplus relative to the ideal maximum needs. Data were obtained from official school reports and analyzed using descriptive quantitative methods. The findings show that 96% of the 25 mapped departments have met the minimum required number of productive teachers, while one department still faces a shortage. On the other hand, 36% of the departments have a surplus of teachers, as the number exceeds the ideal maximum threshold. The distribution of teachers also reveals disparities both within schools (between departments) and among different schools. This mapping provides valuable insight for schools and the regional education authority in planning teacher redistribution, recruitment, and dual-skill training. Furthermore, the results underscore the importance of developing a more dynamic and integrated teacher data system to support sustainable human resource planning in vocational education.