Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model

Geovanne Farell - Universitas Negeri Padang, Padang, Indonesia
Cho Nwe Zin Latt - Pukyong National University, South Korea
Nizwardi Jalinus - Universitas Negeri Padang, Padang, Indonesia
Asmar Yulastri - Universitas Negeri Padang, Padang, Indonesia
Rido Wahyudi - Universitas Negeri Padang, Padang, Indonesia

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Vocational high schools are one of the educational stages impacted by Indonesia's low quality of education. Vocational High Schools play a crucial role in improving human resources. Graduates of Vocational High Schools can continue their education at universities or enter the workforce directly. Many students are found to have not yet considered their career path after graduation. At the same time, graduates are still expected to find mismatched employment with their expertise and skills. This research uses CRISP-DM, or Cross Industry Standard Process for Data Mining, to build machine learning models. The approach used is content-based filtering. This model recommends items similar to previously liked or selected items by the user. Item similarity can be calculated based on the features of the items being compared. After students receive job recommendations through intelligent job matching, they can use these recommendations as references when applying for jobs that align with their results. This process helps students direct their steps toward finding jobs that match their profiles, ultimately increasing their chances of success in the job market. These recommendations are crucial in guiding students toward career paths that align with their abilities and interests. The Intelligent Job Matching Model developed in this research provides recommendations for the job-matching process. This model benefits graduates by providing job recommendations aligned with their profiles and offers advantages to the job market. By implementing the Model of Intelligent Job Matching in the recruitment process, applicants with job qualifications can be matched effectively.


Intelligent job matching; CRISP-DM; Content-based filtering; Machine learning.

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