Recipe Based Skill Matching

Elfaham, Haitham; Grothoff, Julian Alexander; Deppe, Torben Heinz; Azarmipour, Mahyar; Epple, Ulrich

doi:10.1109/indin41052.2019.8972204

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Another model, the decision tree, was used in the study by [12] to predict students' grades and assess their math competency based on profile matching. Other research [13] proposes a capability-matching concept that essentially relies on standardized recipes by offering seamless integration into the available industrial infrastructure. However, only one research [14] decision tree model has similarities and is used for profile matching to determine the promotion of contract employees to permanent employees.…”

Section: Introductionmentioning

confidence: 99%

Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards

Ajhari,

Priambodo,

Yulianti

2024

JOIV : Int. J. Inform. Visualization

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The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.

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Section: Introductionmentioning

confidence: 99%