Talent Development Center Recommendation System Using Content-Based Filtering

Maysha Permata Putri; Benny Daniawan

doi:10.18860/mat.v18i1.32816

Talent Development Center Recommendation System Using Content-Based Filtering

Maysha Permata Putri, Benny Daniawan

Abstract

The development of digital technology has led to increased gadget usage among children, often resulting in a decline in interest in productive physical and social activities. The Indonesian Child Protection Commission reported that over 71.3% of school-age children using gadgets daily. This condition highlights the need for efforts to redirect children's attention toward more beneficial activities, one of which is talent development. Early talent development is crucial for supporting personal potential and future career paths. However, limited information often becomes an obstacle in choosing the right place for talent development that suits an individual's needs and interests. This study aims to design a system that can provide talent development center recommendations for seekers. By implementing the Content-Based Filtering (CBF) method, the system matches user preferences—such as interests, skills, and preferred types of activities expressed in keywords—with the descriptions of available talent development center. Weighting is carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to enhance the relevance of the recommendations by calculating the similarity level between talent development center descriptions based on keyword weights. This approach allows the system to provide more personalized recommendations without relying on other users' data. The testing conducted in this study, using 7 sample talent development places, resulted in 5 recommendations with the top recommendation being Chic’s Musik, which had the highest TF-IDF value of 1.9029.

Index Terms— Content-Based Filtering, Recommendation System, TF-IDF, Talent Development

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References

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DOI: https://doi.org/10.18860/mat.v18i1.32816

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