A Study on the Effectiveness of k-NN Algorithm for Career Guidance in Education

Indriyani Indriyani; Laros Tuhuteru; Gentur Wahyu Nyipto Wibowo; Alex Wenda; I Nengah Sandi

doi:10.35877/454RI.jinav1491

Authors

Indriyani Institut Teknologi dan Bisnis STIKOM Bali, Kota Denpasar, Bali, 80234, Indonesia
Laros Tuhuteru Faculty of Teacher Training and Education, IAIN Syekh Nurjati Cirebon, Kota Cirebon, Jawa Barat, 45132, Indonesia
Gentur Wahyu Nyipto Wibowo Universitas Islam Nahdlatul Ulama Jepara, Kabupaten Jepara, Jawa Tengah, 59451, Indonesia
Alex Wenda Department of Electrical Engineering, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia https://orcid.org/0000-0002-4878-4890
I Nengah Sandi Faculty of Mathematics and Natural Sciences, Universitas Udayana, Kabupaten Badung, Bali, 80361, Indonesia

DOI:

https://doi.org/10.35877/454RI.jinav1491

Keywords:

academic performance, career guidance, education, Euclidean distance, k-NN algorithm

Abstract

This study aims to evaluate the performance of k-NN algorithm in recommending career paths for students based on their interests, past courses, and career goals. The k-NN algorithm was applied to a dataset of student information and its performance was evaluated using quantitative or qualitative measures such as accuracy or user satisfaction. The results indicated that the algorithm provided accurate recommendations and that the choice of k and the use of Euclidean distance measure were crucial for the performance of the algorithm. However, the study also highlighted the limitations of the research, such as the size and diversity of the dataset used, which could have affected the generalizability of the results. This study emphasizes the potential of data-driven approaches in career guidance in education and the k-NN algorithm as a valuable tool in this field. Future research could include incorporating additional factors such as student demographics or academic performance into the algorithm and using more diverse and larger datasets

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