SPATIAL MODELING OF SCHOOL DROPOUT RATES IN UNDERDEVELOPED AREAS OF PAPUA USING GEOGRAPHICALLY WEIGHTED REGRESSION

Harun Al Azies; Hani  Brilianti Rochmanto; Fenny  Fitriani

doi:10.23960/jitet.v13i3.6784

Authors

Harun Al Azies
Hani Brilianti Rochmanto Universitas PGRI Adi Buana
Fenny Fitriani Universitas PGRI Adi Buana

DOI:

https://doi.org/10.23960/jitet.v13i3.6784

Abstract Views: 56 File Views: 56

Keywords:

Disadvantaged Regions, Fixed Gaussian Kernel, Geographically Weighted Regression, School Dropout, Multiple Linear Regression

Abstract

This study examines the factors hypothesized to contribute to school dropout rates in disadvantaged regions of Papua Province and explores potential geographical influences. The primary aims are to derive parameter estimates and statistical tests for the model of underdeveloped regions in Papua using Geographically Weighted Regression (GWR) and to determine the factors influencing school dropout rates in these areas, providing a basis for governmental policy development to mitigate school dropout issues in disadvantaged regions. Findings reveal that the highest dropout rates occur at the junior high school level, with indications of spatial clustering in dropout cases due to heterogeneity among observation sites. This suggests that regions with elevated dropout rates, or conversely low rates, are likely to have neighboring areas with comparable patterns, necessitating the use of spatial regression modeling with a Fixed Gaussian Kernel function. GWR analysis resulted in two clusters based on significant variables, which include the student-teacher ratio at the junior high school level, the student-classroom ratio at the junior high school level, and the elementary school dropout rate (APTs).

Downloads

Download data is not yet available.

References

N. P. Stromquist, “The Learning Generation: Investing in Education for a Changing World; A Report by the International Commission on Financing Global Education Opportunity,” https://doi.org/10.1086/690064, vol. 61, no. 1, pp. 214–217, Feb. 2017, doi: 10.1086/690064.

L. Sumardi, “Why Students Dropout? Case Study of Dropout Attributions in West Nusa Tenggara Province, Indonesia,” Cross-Currents: An International Peer-Reviewed Journal on Humanities & Social Sciences, vol. 6, no. 6, pp. 85–91, Jun. 2020, doi: 10.36344/CCIJHSS.2019.V06I06.006.

S. M. Shahidul and A. H. M. Z. Karim, “FACTORS CONTRIBUTING TO SCHOOL DROPOUT AMONG THE GIRLS: A REVIEW OF LITERATURE,” European Journal of Research and Reflection in Educational Sciences, vol. 3, no. 2, 2015, Accessed: May 28, 2025. [Online]. Available: www.idpublications.org

J. R. Behrman and A. B. Deolalikar, “School Repetition, Dropouts, and the Rates of Return to Schooling: The Case of Indonesia,” Oxf Bull Econ Stat, vol. 53, no. 4, pp. 467–480, 1991, Accessed: May 28, 2025. [Online]. Available: https://ideas.repec.org/a/bla/obuest/v53y1991i4p467-80.html

F. Pastore and K. F. Zimmermann, “Understanding school-to-work transitions,” Int J Manpow, vol. 40, no. 3, pp. 374–378, May 2019, doi: 10.1108/IJM-06-2019-343.

Setyadharma and Andryan, “Upper secondary school dropout : lessons from central Java province, Indonesia : a dissertation presented in partial fulfilment of the requirement for the degree of Doctor of Philosophy in Economics at Massey University, Manawatū, New Zealand,” 2017, Massey University. Accessed: May 28, 2025. [Online]. Available: http://hdl.handle.net/10179/11854

A. Sharma and A. Samantaray, “Demographic Analytical Study of Girl Child Dropout from Schools in India,” International Journal of Engineering Technology Science and Research, vol. 4, no. 10, pp. 921–26, 2017, [Online]. Available: www.ijetsr.com

O. Hetlevik, T. Bøe, and M. Hysing, “GP-diagnosed internalizing and externalizing problems and dropout from secondary school: a cross-sectional study,” Eur J Public Health, vol. 28, no. 3, pp. 474–479, Jun. 2018, doi: 10.1093/EURPUB/CKY026.

M. Cui, C. A. Darling, M. Lucier-Greer, F. D. Fincham, and R. W. May, “Parental Indulgence: Profiles and Relations to College Students’ Emotional and Behavioral Problems,” J Child Fam Stud, vol. 27, no. 8, pp. 2456–2466, Jul. 2018, doi: 10.1007/S10826-018-1076-6/METRICS.

J. E. Lansford, K. A. Dodge, G. S. Pettit, and J. E. Bates, “A Public Health Perspective on School Dropout and Adult Outcomes: A Prospective Study of Risk and Protective Factors From Age 5 to 27 Years,” Journal of Adolescent Health, vol. 58, no. 6, pp. 652–658, Jun. 2016, doi: 10.1016/J.JADOHEALTH.2016.01.014.

UNESCO Institute for Statistic (UIS), “Leaving no one behind: how far on the way to universal primary and secondary education? UIS UNESCO,” 2016.

Kemdikbud, “Data Pokok Pendidikan Dasar dan Menengah Tahun 2015,” Jakarta, 2015.

L. Mubarokah, i N. Budiantara, and M. Ratna, “Pemodelan Angka Putus Sekolah Usia SMP Menggunakan Metode Regresi Nonparametrik Spline di Papua,” Jurnal Sains dan Seni ITS, vol. 5, no. 1, pp. D103–D108, Mar. 2016, doi: 10.12962/J23373520.V5I1.14697.

R. A. Rahma and I. M. Arcana, “TINGKAT RISIKO PUTUS SEKOLAH PADA REMAJA DI PROVINSI PAPUA TAHUN 2018,” Seminar Nasional Official Statistics, vol. 2020, no. 1, pp. 672–681, Jan. 2020, doi: 10.34123/SEMNASOFFSTAT.V2020I1.468.

A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression. Chichester UK: John Wiley & Sons, 2002.

R. Hidayat N, B. W. Otok, Z. Mahsyari, S. H. Sa’diyah, and D. A. Fadhila, “Geographically Weighted Regression for Prediction of Underdeveloped Regions in East Java Province Based on Poverty Indicators,” pp. 898–907, Jun. 2019, doi: 10.5220/0007553708980907.

F. Cholid, D. Trishnanti, and H. Al Azies, “Pemetaan Faktor-Faktor yang Mempengaruhi Stunting pada Balita dengan Geographically Weighted Regression (GWR),” SEMNAKes, pp. 156–165, 2019, doi: 10.17605/OSF.IO/9MZU7.

D. R. S. Saputro, R. D. Hastutik, and P. Widyaningsih, “The modeling of human development index (HDI) in Papua - Indonesia using geographically weighted ridge regression (GWRR),” AIP Conf Proc, vol. 2326, no. 1, Feb. 2021, doi: 10.1063/5.0040329/1000572.

H. Al Azies, “Analisis Pengaruh Pengendalian Pencemaran Dan Kerusakan Lingkungan Terhadap Pertumbuhan Ekonomi Di Indonesia Menggunakan Pendekatan Geographically Weighted Regression Principal Components Analysis (GWRPCA),” Prosiding Seminar Nasional Energi, vol. 8, no. 1, pp. 18–27, 2019.

J. T. Kilmer and R. L. Rodríguez, “Ordinary least squares regression is indicated for studies of allometry,” J Evol Biol, vol. 30, no. 1, pp. 4–12, Jan. 2017, doi: 10.1111/JEB.12986.

A. Mohammadinia, A. Alimohammadi, and B. Saeidian, “Efficiency of Geographically Weighted Regression in Modeling Human Leptospirosis Based on Environmental Factors in Gilan Province, Iran,” Geosciences 2017, Vol. 7, Page 136, vol. 7, no. 4, p. 136, Dec. 2017, doi: 10.3390/GEOSCIENCES7040136.

B. Lu, C. Brunsdon, M. Charlton, and P. Harris, “Geographically weighted regression with parameter-specific distance metrics,” International Journal of Geographical Information Science, vol. 31, no. 5, pp. 982–998, May 2017, doi: 10.1080/13658816.2016.1263731.

K. Isbiyantoro, Y. Wilandari, and M. Jurusan Statistika FSM UNDIP, “PERBANDINGAN MODEL PERTUMBUHAN EKONOMI DI JAWA TENGAH DENGAN METODE REGRESI LINIER BERGANDA DAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION,” Jurnal Gaussian, vol. 3, no. 3, pp. 461–469, 2014, doi: 10.14710/J.GAUSS.3.3.461-469.

L. Chao, K. Zhang, Z. Li, Y. Zhu, J. Wang, and Z. Yu, “Geographically weighted regression based methods for merging satellite and gauge precipitation,” J Hydrol (Amst), vol. 558, pp. 275–289, Mar. 2018, doi: 10.1016/J.JHYDROL.2018.01.042.

M. Charlton and S. Fotheringham, “Geographically Weighted Regression,” White Paper. National Centre for Geocomputation, National University of Ireland Maynooth, pp. 1–14, 2009.

S. P. Ganiswari, H. Al Azies, A. Nugraha, A. Luthfiarta, and G. A. Firmansyah, “Data-Driven Modeling of Human Development Index in Eastern Indonesia’s Region Using Gaussian Techniques Empowered by Machine Learning,” Journal of Applied Geospatial Information, vol. 7, no. 2, pp. 1004–1010, Nov. 2023, doi: 10.30871/JAGI.V7I2.6757.

R. Hidayat N, B. W. Otok, Z. Mahsyari, S. H. Sa’diyah, and D. A. Fadhila, “Geographically Weighted Regression for Prediction of Underdeveloped Regions in East Java Province Based on Poverty Indicators,” Proceedings of the 2nd International Conference Postgraduate School, pp. 898–907, Jan. 2018, doi: 10.5220/0007553708980907.

G. A. Firmansyah, J. Zeniarja, H. Al Azies, S. winarno, and S. P. Ganiswari, “Machine Learning-Enhanced Geographically Weighted Regression for Spatial Evaluation of Human Development Index across Western Indonesia,” Journal of Applied Geospatial Information, vol. 7, no. 2, pp. 996–1003, Nov. 2023, doi: 10.30871/JAGI.V7I2.6755.

N. F. T. A. Nugroho and I. Slamet, “Geographically Weighted Regression Model with Kernel Bisquare and Tricube Weighted Function on Poverty Percentage Data in Central Java Province,” J Phys Conf Ser, vol. 1025, no. 1, p. 012099, May 2018, doi: 10.1088/1742-6596/1025/1/012099.

J. A. Yacim and D. G. B. Boshoff, “A comparison of bandwidth and kernel function selection in geographically weighted regression for house valuation,” International Journal of Technology, vol. 10, no. 1, pp. 58–68, 2019, doi: 10.14716/IJTECH.V10I1.975.

A. R. da Silva and F. F. Mendes, “On comparing some algorithms for finding the optimal bandwidth in Geographically Weighted Regression,” Appl Soft Comput, vol. 73, pp. 943–957, Dec. 2018, doi: 10.1016/J.ASOC.2018.09.033.

A. G. Klein et al., “The detection of heteroscedasticity in regression models for psychological data,” Psychol Test Assess Model, vol. 58, no. 4, pp. 567–592, 2016.