MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER

Authors

  • ADHIYAKSA PRANANDA Hasanuddin University
  • Siswanto Siswanto Hasanuddin University https://orcid.org/0000-0003-1934-5343
  • Sri Astuti Thamrin Hasanuddin University
  • A. Muh. Amil Siddik Hasanuddin University

DOI:

https://doi.org/10.25077/jmua.13.2.131-141.2024

Keywords:

classification, COVID-19, learning vector quantization, vaccination

Abstract

In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus. Indonesia is one of the countries that is aggressively implementing the COVID-19 vaccination. The vaccination process which has been carried out from February 2021 until the end of 2021 has covered approximately 160 million people or 76.83% of the target set by the government. Vaccine recipients have criteria to be able to get vaccinated to avoid side effects or complications. So it is necessary to classify groups that can receive vaccines and also delay vaccination. This research aims to determine the performance of the learning vector quantization classification method. Learning vector quantization method classification produces 95% accuracy, 97% precision, and 96% sensitivity. From these performance measurements, it can be concluded that the learning vector quantization method is very good and can be used in the classification of COVID-19 vaccination recipients at the Parumpanai Public Health Center, East Luwu Regency.

References

Makmun, A., Hazhiyah, S. F., 2020, Tinjauan Terkait Pengembangan Vaksin Covid 19, Molucca Medica, 2020 : 52 – 59

Febriyanti, N., Choliq, M. I., and Mukti, A. W., 2021, Hubungan tingkat pengetahuan dan kesediaan vaksinasi covid-19 pada warga kelurahan dukuh menanggal kota surabaya, SNHRP, 2021 : 36 – 42

Wang, J., Jing, R., Lai, X., Zhang, H., Lyu, Y., Knoll, M. D., and Fang, H., 2020, Acceptance of COVID-19 Vaccination during the COVID-19 Pandemic in China, Vaccines, 8(3) : 482

Haynes, B.F., Corey, L., Fernandes, P., Gilbert, P.B., Hotez, P.J., Rao, S., Santos, M.R., Schuitemaker, H., Watson, M. and Arvin, A., 2020, Prospects for a safe COVID-19 vaccine, Science translational medicine, 12(568): eabe0948

Ichsan, D.S., Hafid, F., Ramadhan, K. and Taqwin, T., 2021, Determinan kesediaan masyarakat menerima vaksinasi Covid-19 di Sulawesi Tengah, Poltekita: Jurnal Ilmu Kesehatan, 15(1) : 1 – 11

Soares, P., Rocha, J.V., Moniz, M., Gama, A., Laires, P.A., Pedro, A.R., Dias, S., Leite, A. and Nunes, C., 2021, Factors associated with COVID-19 vaccine hesitancy, Vaccines, 9(3): 300

Han, J., Pei, J. and Tong, H., 2022, Data mining: concepts and techniques, Morgan kaufmann.

Tomia, S., Leleury, Z.A. and Aulele, S.N., 2017, Perbandingan Metode Jaringan Saraf Tiruan Backpropagation Dan Learning Vector Quantization Dalam Deteksi Hama Pengerek Batang, BAREKENG: Jurnal Ilmu Matematika dan Terapan, 11(1): 13 – 26

Heo, S. and Lee, J.H., 2018, Fault detection and classification using artificial neural networks, IFAC-PapersOnLine, 51(18): 470 – 475

Jo, J.M., 2019, Effectiveness of normalization pre-processing of big data to the machine learning performance, The Journal of the Korea institute of electronic communication sciences, 14(3): 547 – 552

Khan, S.A and Rana, Z.A., 2019, Evaluating Performance of Software Defect Prediction Models Using Area Under Precision-recall Curve (AUC-PR),2nd International Conference on Advancements in Computational Sciences (ICACS),pp. 1-6

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Published

30-04-2024

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Articles