Main Article Content

Abstract

PDAM (Regional Water Supply Company) functions to serve the needs of many people's lives by providing quality water for the community. Based on Permenkes no. 492/menkes/per/iv/2010 clean water quality parameters, namely the feasibility of water used in daily life related to physical, chemical and microbiological parameters including Biological Oxygen Demand (BOD), Total Dissolved Solid (TDS), Cloride (Cl), and Nitrates (NO3). The aim of this research is to test the water quality of PDAMs in Jambi Province based on minimum quality standards for clean water Using Geographically Weighted Regression (GWR) with the assumption. The method can be used to model the relationship between the dependent variable and the independent variable has regional influence. The results showed that the BOD values of all regions in Jambi province met except for Merangin, which was 2.1 mg/l with a threshold value of 2.0 mg/l. For other parameters, namely TDS, Cl and NO3, they meet the threshold values. Based on the results of the GWR model, the coefficient of  is 0.669 , this means that there is a relationship between TDS, Cl and NO3 to BOD and is positive.

Keywords

PDAM Water Quality Geographically Weighted Regression (GWR)

Article Details

How to Cite
1.
Yuinanda S, Multahadah C, Marisa H, Abdullah M. Water Quality Model in Jambi Province using Geographically Weighted Regression. EKSAKTA [Internet]. 2023Sep.30 [cited 2024Nov.5];23(03):436-52. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/378

References

  1. Diana, J. S., Szyper, J. P., Batterson, T. R., Boyd, C. E., & Piedrahita, R. H. (2017). Water quality in ponds. Dynamics of pond aquaculture, 53-71.
  2. Blum, H. L., & Knollmueller, R. N. (1975). Planning for health; development and application of social change theory (Vol. 75, No. 8, p. 1388). LWW.
  3. BPPKPD. (2017). Peranan PDAM dalam Meningkatkan PAD.
  4. Ali, S. F., Hassan, F. M., & Abdul-Jabar, R. A. (2017). Water quality assessment by diatoms in Tigris River, Iraq. International Journal of Environment & Water, 6(2), 53-64.
  5. Nelwan, F., Wuisan, E. M., & Tanudjaja, L. (2013). Perencanaan Jaringan Air Bersih Desa Kima Bajo Kecamatan Wori. Jurnal Sipil Statik, 1(10).
  6. Adianto, J., Gabe, R. T., & Djaja, K. (2021). The Challenge of Reclaiming the Commons of the Ciliwung River in Depok City. International Journal of Design Management & Professional Practice, 15(1).
  7. Taghipour Javi, S., Malekmohammadi, B., & Mokhtari, H. (2014). Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain, Iran). Environmental monitoring and assessment, 186, 3123-3138.
  8. Hasan, M. K., Khan, M. R. I., Nesha, M. K., & Happy, M. A. (2014). Analysis of water quality using chemical parameters and metal status of Balu River at Dhaka, Bangladesh. Open J. Water Pollut. Treat, 1(2), 58-74.
  9. Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in multiscale geographically weighted regression. Geographical Analysis, 52(1), 87-106.
  10. Li, Z., & Fotheringham, A. S. (2020). Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), 1378-1397.
  11. O'Sullivan, D. (2003). Geographically weighted regression: the analysis of spatially varying relationships. Geographical analysis, 35(3), 272-275.
  12. Fitriyani, F., Yurinanda, S., & Multahadah, C. (2023). Penerapan Metode Geographically Weighted Regression Pada Tingkat Pencemaran Air Berdasarkan Total Coliform Di Provinsi Jambi. Jurnal Lebesgue: Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, 4(1), 603-613.
  13. Wikurendra, E. A., Syafiuddin, A., Nurika, G., & Elisanti, A. D. (2022). Water quality analysis of pucang river, sidoarjo regency to control water pollution. Environmental Quality Management, 32(1), 133-144.
  14. Yaroshenko, I., Kirsanov, D., Marjanovic, M., Lieberzeit, P. A., Korostynska, O., Mason, A., ... & Legin, A. (2020). Real-time water quality monitoring with chemical sensors. Sensors, 20(12), 3432.
  15. Jaybhaye, R., Nandusekar, P., Awale, M., Paul, D., Kulkarni, U., Jadhav, J., ... & Kamble, P. (2022). Analysis of seasonal variation in surface water quality and water quality index (WQI) of Amba River from Dolvi Region, Maharashtra, India. Arabian Journal of Geosciences, 15(14), 1261.
  16. Suyono, M. S. (2015). Analisis Regresi untuk Penelitian. Deepublish.
  17. Alita, D., Putra, A. D., & Darwis, D. (2021). Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(3), 295-306.
  18. Hasan, M. I. (2013). Analisis Data Statistik Penelitian dengan Statistik.
  19. Ratmono, D. (2017). Analisis Multivariat Dan Ekonometrika Teori, Konsep, Dan Aplikasi Dengan Eviews 10.
  20. Schmidt, A. F., & Finan, C. (2018). Linear regression and the normality assumption. Journal of clinical epidemiology, 98, 146-151.
  21. Cameron, A. C., & Windmeijer, F. A. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of econometrics, 77(2), 329-342.
  22. Hawley, S., Ali, M. S., Berencsi, K., Judge, A., & Prieto-Alhambra, D. (2019). Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study. Clinical epidemiology, 197-205.
  23. Frisch, R., & Waugh, F. V. (1933). Partial time regressions as compared with individual trends. Econometrica: Journal of the Econometric Society, 387-401.
  24. Basuki, A. T., & Prawoto, N. (2016). Analisis regresi dalam penelitian ekonomi dan bisnis.
  25. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.
  26. Hasibuan, D. O., Bekti, R. D., Sutanta, E., & Pradnyana, I. W. J. (2022). Application of the Geographically Weighted Regression Method to the Human Development Index and Visualization on the Tableau Dashboard. IC-ITECHS, 3(1), 39-51.
  27. Cohen, A., & Migliorati, G. (2017). Optimal weighted least-squares methods. The SMAI journal of computational mathematics, 3, 181-203.
  28. LeSage, J. P. (2004). A family of geographically weighted regression models. In Advances in spatial econometrics: methodology, tools and applications (pp. 241-264). Berlin, Heidelberg: Springer Berlin Heidelberg.
  29. Comber, A., Wang, Y., Lü, Y., Zhang, X., & Harris, P. (2018). Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization. Journal of Spatial Information Science, (17), 63-84.
  30. Sasongko, T. B., Arifin, O., & Al Fatta, H. (2019, July). Optimization of hyper parameter bandwidth on naïve Bayes kernel density estimation for the breast cancer classification. In 2019 International Conference on Information and Communications Technology (ICOIACT) (pp. 226-231). IEEE.
  31. Liu, X., Tang, B. H., Li, Z. L., & Shang, G. (2021). Development of Kernel-Driven Models With Fixed Hotspot Width Under a General Modeling Framework in the Thermal Infrared Domain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9187-9195.
  32. Roy, S. K., Manna, S., Song, T., & Bruzzone, L. (2020). Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59(9), 7831-7843.
  33. Goual, H., Yousof, H. M., & Ali, M. M. (2020). Lomax inverse Weibull model: properties, applications, and a modified Chi-squared goodness-of-fit test for validation. Journal of Nonlinear Sciences & Applications (JNSA), 13(6).
  34. Cavanaugh, J. E., & Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews: Computational Statistics, 11(3), e1460.