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All Journal Dharmawangsa: International Journal of the Social Sciences, Education and Humanitis
Viki, Zakial
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Humanities Education Social Sciences Other

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ANALYSIS OF K-MEANS ALGORITHM FOR RECOMMENDATIONS STUDENT CAREER DETERMINATION Fadhilah, Cut; Nunsina, Nunsina; Viki, Zakial
Dharmawangsa: International Journal of the Social Sciences, Education and Humanitis Vol 3, No 3 (2022): Social Sciences, Education and Humanities
Publisher : Universitas Dharmawangsa Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/ijsseh.v3i3.2816

Abstract

ABSTRACTCareer is a person's progress in a job that is obtained through training or work experience during his life. The stages in a career start from knowing the type of job you are interested in based on your expertise, so there is a reference for finding the job you want. After knowing the job you want, the next step is to stay focused and deepen your skills in that field, so you can master the job you're looking for. Based on these stages, a system is needed that can recommend careers that can assist students in determining careers that match their potential based on their academic grades. In this study, the K-Means algorithm was used to analyze the problem. This study designed a k-means algorithm analysis system for career suitability recommendations for web-based students using HTML, PHP, CSS and XAMPP programming languages. The method used in this study is the Unified Modeling Language (UML) method. This research is able to provide career recommendations for students using the k-means clustering algorithm for three types of careers, namely Web Engineer, Programmer and Software Engineering. This study produces an accuracy rate of 96.6% with manual calculations with results in the system This research is able to provide career recommendations for students using the k-means clustering algorithm for three types of careers, namely Web Engineer, Programmer and Software Engineering. This study produces an accuracy rate of 96.6% with manual calculations with results in the system This research is able to provide career recommendations for students using the k-means clustering algorithm for three types of careers, namely Web Engineer, Programmer and Software Engineering. This study produces an accuracy rate of 96.6% with manual calculations with results in the systemKeywords: Career, K-means, Recommendations.
Co-Authors Fadhilah, Cut Nunsina, Nunsina