Article Sidebar

Submitted
May 28, 2021
Accepted
Jun 29, 2021
Published
Jun 30, 2021
Download
PDF
Statistic
Read Counter : 218 Download : 265

Main Article Content

Abstract

Non-communicable diseases is diseases that are not caused by germs but rather because of physiological or metabolic problems in human body tissues. Usually, this disease occurs due to unhealthy lifestyle. One way to find out how large the spread of non-communicable diseases is by mapping the disease using biplot analysis. Biplot analysis is applied to determine the proximity information between objects, the length of the change vector, the correlation between modifiers, and the value of the change in an object. The study was conducted in 33 provinces with twelve non-communicable diseases. Descriptive analysis of twelve non-communicable diseases averaged the highest joint disease of 10.51 followed by hypertensive disease 8.85 and Stroke 6.42. While the lowest average disease is Heart Failure disease by 0.10 it is still open to research with other methods and also need to add supporting variables

Keywords

Biplot Analysis Non-Communicable Diseases

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Suryowati, K., JP, M. T., & Nasution, N. (2021). Application Biplot Analysis on Mapping of Non-Convertive Diseases in Indonesia. Parameter: Journal of Statistics, 1(2), 11-20. https://doi.org/10.22487/27765660.2021.v1.i2.15518

References

  1. Leleury, Z. A., & Wokanubun, A. E. (2015). Analisis Biplot pada Pemetaan Karakteristik Kemiskinan di Provinsi Maluku. Barekeng: Jurnal ilmu matematika dan terapan, 9(1), 21-31.
  2. Melissa, M. (2012) Pendekatan Analisis Biplot Dalam Mendeskripsikan Posisi Relatif Antar SMA Di Kota Denpasar. E-Jurnal Matematika. Vol. 1, No.1, p 47-51
  3. Noeryanti, Suryowati, K., & Setyawan,Y. (2018). Logistic Regression and Path Analysis Method to AnalyzeFactors influencing Students’ Achievement. IOP Conf. Series: Materials Science and Engineering 335 (2018) 012044 doi:10.1088/1757-899X/335/1/012044
  4. Padang, D. L., Rindengan, A. J., & Kekenusa, J. S. (2019). Analisis Biplot pada Pemetaan Karakteristik Kemiskinan di Provinsi Sulawesi Utara. d'CARTESIAN, 8(1), 49-52.
  5. Putri, S., & Nanang, A. (2020). Pengelompokan Merek Susu Ultra Hight Tempherature Berdasarkan Kandungan Gizi Menggunakan Analisis Biplot..e-journal unp
  6. Setyawan, Y., Noeryanti, Suryowati, K. (2018). Statistika Dasar Dilengkapi dengan Software R. Yogyakarta.
  7. Suryowati, K., & Harmastuti, (2013), Aljabar Linear, Akprind press, Yogyakarta
  8. https://lokadata.beritagar.id/chart/preview/prevalensi-asma-menurut-provinsi-2018-1555042135#. (Diakses pada 13 November 2019)
  9. http://labdata.litbang.kemkes.go.id/images/download/laporan/RKD/2013/RKD_dalam_angka_final.pdf, Riset Kesehatan Dasar (RISKESDAS) 2013 (Diakses pada 23 Februari 2018)
  10. 2019. Manajemen Penyakit Tidak Menular, Jakarta, KEMENKES Direktorat Jendral Pencegahan dan Pengendalian Penyakit.
  11. 2018. Profil Kesehatan Indonesia, Jakarta, Kementerian Kesehatan Indonesia.