Sistem Rekomendasi Content Based Filtering Pekerjaan dan Tenaga Kerja Potensial menggunakan Cosine Similarity
Keywords:
Kano method, service programme designAbstract
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.References
[1] Cahyani, D. E., & Patasik, I. (2021). Performance comparison
of TF-IDF and Word2Vec models for emotion text
classification. Bulletin of Electrical Engineering and
Informatics, 10(5), 2780-2788. DOI:
https://doi.org/10.11591/eei.v10i5.3157
[2] Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018, April).
A hybrid approach using collaborative filtering and content
based filtering for recommender system. In Journal of
Physics: Conference Series (Vol. 1000, No. 1, p. 012101). IOP
Publishing. DOI:10.1088/1742-6596/1000/1/012101
[3] Gunawan, D., Sembiring, C. A., & Budiman, M. A. (2018,
March). The implementation of cosine similarity to calculatetext relevance between two documents. In Journal of physics:
conference series (Vol. 978, No. 1, p. 012120). IOP
Publishing. DOI:10.1088/1742-6596/978/1/012120
[4] Gupta, A., & Garg, D. (2014). Applying data mining
techniques in job recommender system for considering
candidate job preferences. 2014 International Conference on
Advances in Computing, Communications and Informatics
(ICACCI).Published.https://doi.org/10.1109/icacci.2014.6968
361
[5] Malinowski, J., Keim, T., Wendt, O., & Weitzel, T. (2006).
Matching People and Jobs: A Bilateral Recommendation
Approach. Proceedings of the 39th Annual Hawaii
International Conference on System Sciences (HICSS’06).
Published. https://doi.org/10.1109/hicss.2006.266
[6] Rizal, J. G. (2020, August 11). Pandemi Covid-19, Apa Saja
Dampak pada Sektor Ketenagakerjaan Indonesia? Halaman
all - Kompas.com. KOMPAS.Com.
https://www.kompas.com/tren/read/2020/08/11/102500165/p
andemi-covid-19-apa-saja-dampak-pada-sektorketenagakerjaan-indonesia-?page=all
[7] Qaiser, S., & Ali, R. (2018). Text mining: use of TF-IDF to
examine the relevance of words to documents. International
Journal of Computer Applications, 181(1), 25-29.
DOI:10.5120/ijca2018917395
[8] Valverde-Rebaza, J. C., Puma, R., Bustios, P., & Silva, N. C.
(2018). Job Recommendation Based on Job Seeker Skills: An
Empirical Study. In Text2Story@ ECIR (pp. 47-51).
[9] Van Meteren, R., & Van Someren, M. (2000, May). Using
content-based filtering for recommendation. In Proceedings of
the machine learning in the new information age:
MLnet/ECML2000 workshop (Vol. 30, pp. 47-56).
[10] Zhang, L., Zhang, S., & Balog, K. (2019, July). Table2vec:
Neural word and entity embeddings for table population and
retrieval. In Proceedings of the 42nd International ACM
SIGIR Conference on Research and Development in
Information Retrieval (pp. 1029-1032). DOI:
