Modeling Chemical Oxygen Demand of River Water in East Kalimantan Using Fixed Effects and Geographically Weighted Panel Regression

Authors

  • Memi Nor Hayati Doctoral Study Program MIPA, Faculty of Science and Technology, Airlangga University
  • Toha Saifudin Department of Mathematics, Faculty of Science and Technology, Airlangga University
  • Suyitno Suyitno Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University

DOI:

https://doi.org/10.30871/jaic.v10i3.13089

Keywords:

Chemical Oxygen Demand, Fixed Effect Model, Geographically Weighted Panel Regression, River Water Quality, SDGs Goal 6

Abstract

The degradation of river water quality in East Kalimantan has become an environmental issue related to the Sustainable Development Goals (SDGs), particularly Goal 6 concerning clean water and sanitation. This study aims to model Chemical Oxygen Demand (COD) as an indicator of river water quality using the Fixed Effect Model (FEM) and Geographically Weighted Panel Regression (GWPR) approaches, as well as to compare the performance of both models in representing spatial and temporal heterogeneity. The data used in this study consist of panel data from 31 observation locations over four semesters, with predictor variables including pH, Total Suspended Solid (TSS), Fecal Coliform, Total Dissolved Solid (TDS), and ammonia. FEM estimation was conducted using the within estimator, while the GWPR model employed an adaptive bisquare kernel weighting function. The results show that TSS and ammonia significantly affect COD in the FEM model. In the GWPR model, the effects of predictor variables vary across observation locations, indicating spatial heterogeneity in the factors influencing COD. TSS was identified as the most dominant variable, being significant at 26 locations, followed by ammonia and TDS, which were significant at 18 and 15 locations, respectively. Furthermore, the GWPR model produced a lower RMSE value (1.3777) than the FEM model (2.182011). These findings indicate that GWPR performs better in capturing spatial heterogeneity and temporal information in river water quality and provides more detailed information for supporting location-specific water quality management in East Kalimantan.

Downloads

Download data is not yet available.

References

[1] U. Nations, “Ensure availability and sustainable management of water and sanitation for all,” 2015.

[2] U. N. E. Programme, “Goal 6: Clean Water and Sanitation,” 2025.

[3] M. A. Makarim, “Prevention of decreasing river water quality due to anthropogenic activities: A systematic review of water pollution on Cisadane River,” J. Mar. Probl. Threat., vol. 1, no. 1, pp. 44–65, 2024.

[4] B. P. S. K. Timur, Kalimantan Timur dalam Angka 2023. Samarinda, Indonesia: BPS Kalimantan Timur, 2023.

[5] D. Marganingrum and R. Noviardi, “Pencemaran air dan tanah di kawasan pertambangan batubara di PT. Berau Coal, Kalimantan Timur,” Ris. Geol. dan Pertamb., vol. 20, no. 1, pp. 11–20, 2010.

[6] APHA, Standard Methods for the Examination of Water and Wastewater, 23rd ed. Washington DC, USA: American Public Health Association, 2017.

[7] H. Effendi, Telaah Kualitas Air: Bagi Pengelolaan Sumberdaya dan Lingkungan Perairan. Yogyakarta: Kanisius, 2003.

[8] A. S. F. R. Hufaini, Raupong, and N. Ilyas, “Regresi model data panel efek tetap dengan metode within group pada data indeks pembangunan manusia Provinsi Sulawesi Selatan,” Estimasi J. Stat. Its Appl., vol. 1, no. 1, pp. 10–20, 2020.

[9] A. Indrasetianingsih and T. K. Wasik, “Model regresi data panel untuk mengetahui faktor yang mempengaruhi tingkat kemiskinan di Pulau Madura,” J. Gaussian, vol. 9, no. 3, pp. 355–363, 2020.

[10] A. Dewintha, I. Yahya, and M. Ihwal, “Analisis regresi data panel pada faktor-faktor yang mempengaruhi tingkat kemiskinan di Provinsi Sulawesi Tenggara tahun 2020–2023,” Arus J. Sains dan Teknol., vol. 3, no. 1, pp. 83–94, 2025.

[11] B. H. Baltagi, Econometric Analysis of Panel Data, 7th ed. Switzerland: Springer Nature, 2021.

[12] A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: John Wiley & Sons, 2002.

[13] N. A. Rahman, A. A. Latif, and S. M. Said, “Geographically Weighted Regression (GWR) model for spatial analysis,” Malaysian J. Fundam. Appl. Sci., vol. 15, no. 3, pp. 338–343, 2019.

[14] D. Yu, “Exploring spatiotemporally varying regressed relationships: The geographically weighted panel regression analysis,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 38, 2010.

[15] R. Cai, D. Yu, and M. Oppenheimer, “Estimating the spatially varying responses of corn yields to weather variations using geographically weighted panel regression,” J. Agric. Resour. Econ., vol. 39, no. 2, pp. 230–252, 2014.

[16] M. R. Sari, A. Hoyyi, and Suparti, “Metode geographically weighted panel regression (GWPR) untuk pemodelan data spasial panel,” J. Gaussian, vol. 7, no. 3, pp. 239–248, 2018.

[17] Z. N. Fauziyah, Suyitno, D. A. Nohe, M. N. Hayati, and M. Fauziyah, “Analyzing the Mahakam River water quality using the geographically weighted panel regression model,” MethodsX, vol. 16, 2025.

[18] Sifriyani, N. Budiantara, M. Fariz, and F. Mardianto, “Determination of the best geographic weighted function and estimation of spatio temporal model – geographically weighted panel regression using weighted least square,” MethodsX, vol. 12, 2024.

[19] N. M. S. Ananda, Suyitno, and M. Siringoringo, “Geographically weighted panel regression modelling of human development index data in East Kalimantan Province in 2017–2020,” J. Mat. Stat. dan Komputasi, vol. 19, no. 2, pp. 323–341, 2023.

[20] A. Y. Qur’ani, “Pemodelan geographically weighted regression panel (GWR-panel) sebagai pendekatan model geographically weighted regression (GWR) dengan menggunakan fixed effect model time trend,” vol. 2, no. 3, pp. 1–10, 2014.

[21] D. F. Anwar, T. Alawiyah, and Yulma, “Penentuan status mutu kualitas air Sungai Bidadari di Kelurahan Juata Kerikil Kota Tarakan,” J. Harpodon Borneo, vol. 15, no. 2, pp. 124–132, 2023, doi: 10.35334/harpodon.v15i2.3008.

[22] U. S. Primadigna, Suyitno, and M. Siringoringo, “Model geographically weighted Weibull regression pada indikator pencemaran air COD di daerah aliran Sungai Mahakam Kalimantan Timur,” Eksponensial, vol. 13, no. 2, 2022, doi: 10.30872/eksponensial.v13i2.1050.

[23] C. Hsiao, Analysis of Panel Data, 3rd ed. Cambridge: Cambridge University Press, 2014.

[24] J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, 2nd ed. Cambridge, MA, USA: MIT Press, 2010.

[25] D. N. Gujarati and D. C. Porter, Basic Econometrics, 5th ed. New York: McGraw-Hill Education, 2009.

[26] F. Bruna and D. Yu, “Geographically weighted panel regression and development accounting for European regions,” in Proceedings of the 6th Seminar Jean Paelinck in Spatial Econometrics, 2016, pp. 1–20.

[27] S. M. Meutuah, H. Yasin, and I. M. Di Asih, “Pemodelan fixed effect geographically weighted panel regression untuk indeks pembangunan manusia di Jawa Tengah,” J. Gaussian, vol. 6, no. 2, pp. 241–250, 2017.

[28] C. S. Purnamasari and Y. Widyaningsih, “Perbandingan performa bandwidth CV, AICc, dan BIC pada model geographically weighted regression,” in Seminar Nasional Statistika XI 2022 BT - Inferensi, 2023, pp. 71–83.

[29] A. Dewantoro and T. B. Sasongko, “Comparison of LSTM model performance with classical regression in predicting gaming laptop prices in Indonesia,” J. Appl. Informatics Comput., vol. 8, no. 1, pp. 203–212, 2024.

[30] A. Harismahyanti A., A. Najiha, A. I. Yunita, Ratmila, and Nur’eni, “Comparison of FEM-LSDV panel regression with classical panel regression models in analyzing economic growth in Indonesia,” J. Appl. Informatics Comput., vol. 9, no. 5, pp. 2450–2460, 2025.

Downloads

Published

2026-06-18

How to Cite

[1]
M. N. Hayati, T. Saifudin, and S. Suyitno, “Modeling Chemical Oxygen Demand of River Water in East Kalimantan Using Fixed Effects and Geographically Weighted Panel Regression”, JAIC, vol. 10, no. 3, pp. 2986–2996, Jun. 2026.

Most read articles by the same author(s)

Similar Articles

<< < 1 2 3 4 5 > >> 

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