Design of a Student Thesis Topic Recommendation System Using the K-Nearest Neighbor Algorithm
DOI:
https://doi.org/10.35870/ijsecs.v6i2.7659Keywords:
Recommendation System, Thesis Topic, K-Nearest NeighborAbstract
Determining the right thesis topic is a challenge for students because it is often not aligned with their abilities, interests, and experience, resulting in a less than optimal research process. This study aims to design a data-based thesis topic recommendation system using the K-Nearest Neighbor (KNN) algorithm. Data were collected through a questionnaire that measures three main aspects of students, namely abilities, interests, and experience in the fields of programming, web development, system security, and computer networks. Qualitative data were then converted into a numeric format using a Likert scale and binary values to be processed as a classification dataset. The KNN algorithm was implemented with Euclidean Distance calculations and a majority voting mechanism using K = 3 and K = 5 values. System testing with a training and test data division ratio of 80:20 resulted in an accuracy rate of 80%. These results indicate that the system is able to provide relevant and objective topic recommendations according to student profiles. This study proves that a data-driven approach and the KNN algorithm can be an effective solution to support systematic academic decision-making.
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