Sistem Rekomendasi Content Based Filtering Pekerjaan dan Tenaga Kerja Potensial menggunakan Cosine Similarity

Philips Nogo Raharjo(1*), Andreas Handojo(2), Hans Juwiantho(3),

(1) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(2) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(3) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(*) Corresponding Author


During the pandemic, there was an economic problem that forced companies to do something to avoid any loss. One of the action is to terminate the employment with their workforces. In the conventional way, the workforce and the company will waste a lot of time looking for the right fit for them. So, the recommendation system for jobs and workforce plays an important role in these conditions. Because with the existence of recommendation system that can help from both sides, it will speed up the meeting between companies that need workers and workers who need jobs. Based on the test have been carried out, the recommendation system using the TF-IDF model can provide good recommendations based on the calculation of the Mean Reciprocal Rank getting 0.857 and Mean Average Precision of 0.833, where these results are quite good.


Recommendation System; Content-Based Filtering; Cosine Similarity

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