Village Potential Statistics (PODES): Visualization of Schools in Jambi Province with Statistical Programming (R)

Authors

  • Fadhlul Mubarak UIN Sulthan Thaha Saifuddin Jambi
  • Atilla Aslanargun Department of Statistics, Eskisehir Technical University
  • Vinny Yuliani Sundara Department of Mathematics Education, UIN Sulthan Thaha Saifuddin Jambi, Indonesia

DOI:

https://doi.org/10.30631/demos.v2i2.1333

Keywords:

attractive graphic, percentage, R Programming, Statistics Indonesia (BPS)

Abstract

One of the primary data that can be used in research is village potential statistics (PODES). The data was obtained based on research in a certain period by the Statistics Indonesia (BPS). This study aims to visualize the percentage of schools in each city/district in Jambi Province using R programming based on PODES data in 2014 and 2019. In this study, we not only visualize but also how to build attractive graphics and arrange them starting from windows, graphic size, dimensions, color, horizontal axis, vertical axis, and others. Of course, the graph produced in this study is different from the basic plot found in the R program, although the process carried out is also more complicated. From 2014 to 2019, in general, within a period of 5 years there has been an increase in the number of schools in each city/district in Jambi Province. However, from the university level, the number decreased. In 2014 the number of universities in Jambi City was 32 but in 2019 the number decreased to 24. There are even interesting things in Kerinci and Tebo district. In 2014 there were no universities listed, while in 2019 there were 3. This also affects the percentage of education level in each city/district.

References

Abdollahi, M., Farjad, B., Gupta, A., & Hassan, Q. K. (2022). CMIP6-D&A: An R-based software with GUI for processing climate data available in network common data format. SoftwareX, 18, 101044. https://doi.org/10.1016/j.softx.2022.101044

Atkins, J. W., Stovall, A. E. L., & Silva, C. A. (2022). Open-Source tools in R for forestry and forest ecology. Forest Ecology and Management, 503, 119813. https://doi.org/10.1016/j.foreco.2021.119813

Borisov, D. I. (2022). Analyticity of resolvents of elliptic operators on quantum graphs with small edges. Advances in Mathematics, 397, 108125. https://doi.org/10.1016/j.aim.2021.108125

Diaz-Diaz, F., & Estrada, E. (2022). Time and space generalized diffusion equation on graph/networks. Chaos, Solitons & Fractals, 156, 111791. https://doi.org/10.1016/j.chaos.2022.111791

Fitriansyah, F., & Nuryakin, C. (2021). Desa Digital dan Tingkat Literasi Keuangan Aparatur Desa: Studi Kasus Kabupaten Aceh Tamiang. Jurnal Ekonomi Dan Pembangunan Indonesia, 21(2), 220–234. https://doi.org/10.21002/jepi.v21i2.1076

Hakim, L., Juwita, A. H., Bintariningtyas, S., & Guritno, D. C. (2021). Poverty and SMEs: A New Pradigm of SDGs Development. Optimum: Jurnal Ekonomi Dan Pembangunan, 11(2), 224–231. https://doi.org/10.12928/optimum.v11i2.3943

Hardiyanto, E. (2020). KOMBINASI METODE ENSEMBLE, CFS DAN POHON KEPUTUSAN UNTUK PREDIKSI KINERJA PETUGAS STUDI KASUS: SURVEY PODES BADAN PUSAT STATISTIK. Joutica, 5(1), 337–345. https://doi.org/10.30736/jti.v5i1.390

Hasan, M. F., Fadhil, I., Fahmid, M. M., & Ahmad, T. (2022). Impact of the European Union Regulations on Indonesian Oil Palm Smallholder Farmers. International Journal of Oil Palm, 5(1), 1–15. https://doi.org/10.35876/ijop.v5i1.69

Junaidi, J., Yulmardi, Y., & Hardiani, H. (2020). Food crops-based and horticulture-based villages potential as growth center villages in Jambi Province, Indonesia. Journal of Critical Reviews, 7(9), 514–519. https://doi.org/10.31838/jcr.07.09.102

Kaban, P. A., Nasution, B. I., Caraka, R. E., & Kurniawan, R. (2022). Implementing night light data as auxiliary variable of small area estimation. Communications in Statistics - Theory and Methods, 1–18. https://doi.org/10.1080/03610926.2022.2077963

Li, P., Wei, L.-Q., Pan, Y.-F., & Zhang, Y.-M. (2022). dQTG.seq: A comprehensive R tool for detecting all types of QTLs using extreme phenotype individuals in bi-parental segregation populations. Computational and Structural Biotechnology Journal, 20, 2332–2337. https://doi.org/10.1016/j.csbj.2022.05.009

Paais, L. S. (2021). Keragaman Agama, Etnis, Bahasa, dan Pembangunan Desa. Journal of Regional and Rural Development Planning, 5(2), 77–90. https://doi.org/10.29244/jp2wd.2021.5.2.77-90

Permatasari, N., & Larasati, W. (2022). Perbandingan Metode SAE EBLUP dan SAE HB Pada Pendugaan Area Kecil (Studi Kasus Pendugaan Kemiskinan di Provinsi Jawa Timur). Jurnal Statistika Dan Aplikasinya, 6(1), 96–108. https://doi.org/10.21009/JSA.06109

Saavedra-Nieves, A. (2021). Statistics and game theory: Estimating coalitional values in R software. Operations Research Letters, 49(1), 129–135. https://doi.org/10.1016/j.orl.2020.12.005

Sari, R. K., & Handayani, D. (2020). Pemanfaatan Pelayanan Kesehatan pada Anak Indonesia: Pengaruh Kemiskinan dan Karakteristik Ibu. Media Kesehatan Masyarakat Indonesia, 16(3), 305–316. https://doi.org/10.30597/mkmi.v16i3.9709

Shin, E., Yoo, S., Ju, Y., & Shin, D. (2022). Knowledge graph embedding and reasoning for real-time analytics support of chemical diagnosis from exposure symptoms. Process Safety and Environmental Protection, 157, 92–105. https://doi.org/10.1016/j.psep.2021.11.002

Snitker, G., Moser, J. D., Southerlin, B., & Stewart, C. (2022). Detecting historic tar kilns and tar production sites using high-resolution, aerial LiDAR-derived digital elevation models: Introducing the Tar Kiln Feature Detection workflow (TKFD) using open-access R and FIJI software. Journal of Archaeological Science: Reports, 41, 103340. https://doi.org/10.1016/j.jasrep.2022.103340

Tiddi, I., & Schlobach, S. (2022). Knowledge graphs as tools for explainable machine learning: A survey. Artificial Intelligence, 302, 103627. https://doi.org/10.1016/j.artint.2021.103627

Trilaksono, T., & Sukartini, N. M. (2020). Kaitan Karakteristik Perangkat Desa Dengan Indeks Pembangunan Desa Di Indonesia. Jurnal Sains Sosio Humaniora, 4(1), 194–204. https://doi.org/10.22437/jssh.v4i1.9916

Wang, Z., & Wang, J. (2022). Analytical graphs to describe operating status of industrial alarm variables. Control Engineering Practice, 118, 104961. https://doi.org/10.1016/j.conengprac.2021.104961

Downloads

Published

2022-11-30

Similar Articles

1-10 of 20

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)