Model Geographically Weighted Weibull Regression Pada Indikator Pencemaran Air COD  di Daerah Aliran Sungai Mahakam Kalimantan Timur

Ullimaz Sam Primadigna; Suyitno Suyitno; Meiliyani Siringoringo

doi:10.30872/eksponensial.v13i2.1050

Ullimaz Sam Primadigna Laboratorium Statistika Terapan FMIPA Universitas Mulawarman
Suyitno Suyitno Laboratorium Statistika Terapan FMIPA Universitas Mulawarman
Meiliyani Siringoringo Laboratorium Statistika Ekonomi dan Bisnis, FMIPA Universitas Mulawarman

DOI: https://doi.org/10.30872/eksponensial.v13i2.1050

Abstract

The Geographically Weighted Weibull Regression (GWWR) model is a Weibull regression model applied to spatial data. Parameter estimation is carried out at each observation location using spatial weighting. This study aimed to determine the GWWR model on the Chemical Oxygen Demand (COD) water pollution indicator data and to obtain the factors that influence COD in the Mahakam watershed. The parameter estimation method was Maximum Likelihood Estimation (MLE). Spatial weighting in parameter estimation has been determined using the adaptive tricube weighting function and the criteria for determining the optimum bandwidth was Generalized Cross-Validation (GCV). The research sample was 20 location points of the Mahakam river determined by the Environmental Department of East Kalimantan Province. The results showed that the factors that influence COD locally was temperature, while the factors that influence globally were temperature, Total Suspended Solids (TSS), and Fecal Coli.

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References

Fotheringham, A.S. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. England: John Wiley & Sons.
Gujarati, D. (2003). Basic Econometrics 4th Edition. New York: McGraw-Hill Inc.
Hosmer, D.W., Lemeshow, S., & May, S. (2008), Applied Survival Analysis. Regression Modeling of Time-to-Event Data. New Jersey: John Wiley.
Khuri, A.I. (2003), Advanced Calculus with Applications in Statistics, 2nd Edition. New Jersey: John Wiley & Sons, Inc.
Lawless, J.F. (2003). Statistical Model and Methods for Lifetime Data. 2nd Edition. New Jersey: John Wiley & Sons.
Nugroho, A. (2006). Bioindikator Kualitas Air. Jakarta: Universitas Trisakti.
Otaya, L.G. (2016). Distribusi Probabilitas Weibull dan Aplikasinya. Jurnal Manajemen Pendidikan Islam, 4(2), 44-66.
Pawitan, Y. (2001). In All Likelihood. Statistical Modelling and Inference Using Likelihood. Sweden: Clarendon Press-Oxford.
Peraturan Pemerintah Republik Indonesia Nomor 82 Tahun 2001 Tentang Pengelolaan Kualitas Air dan Pengendalian Pencemaran Air.
Rinne, H. (2009). The Weibull Distribution A Handbook. London: CRC Press Taylor and Francis Group.
Rencher, A.C. (2000). Linear Model in Statistics. New York: John Wiley & Sons, Inc.
Suyitno. (2017). Penaksiran Parameter dan Pengujian Hipotesis Model Regresi Weibull Univariat. Jurnal Eksponensial, 8 (2), 179-183.
Suyitno. (2017). Model Geographically Weighted Multivariate Weibull Regression (Disertasi). ITS Surabaya.
Suyitno & Sari, N W W. (2019). Parameter Estimation of Mixed Geographically Weighted Weibull Regression Model. Journal of Physic, 1-13, DOI: 10.1088/1742-6596/1277/1/012046.