Pemodelan Regresi Weibull Pada Data Kontinu Yang Diklasifikasikan (Studi kasus: Data Indikator Pencemaran Air Dissolved Oxygen Pada DAS Mahakam Kalimantan Timur Tahun 2020)

  • Alfiannur Rizki Sudarman Universitas Mulawarman
  • Suyitno Suyitno Laboratorium Statistika Terapan Fakultas MIPA, Universitas Mulawarman
  • Meiliyani Siringoringo Laboratorium Statistika Ekonomi Bisnis, Fakultas MIPA, Universitas Mulawarman

Abstract

Weibull regression model is a Weibull distribution that is directly influenced by covariates. Weibull regression models discussed in this study are Weibull survival regression model, Weibull hazard regression, and Weibull mean regression. The Weibull regression model in this study was applied to water pollution indicator of dissolved oxygen (DO) data in the Mahakam watershed of East Kalimantan in 2020. The purpose of this study was to obtain a Weibull regression model for water pollution indicator of DO data, to obtain the factors that influence the Weibull regression model, and to interpretation the Weibull regression model of water pollution indicator of DO data. The study’s result is that the Newton-Raphson iterative approach was used to find the approximate of maximum likelihood estimator. Based on the hypothesis testing, it is concluded the factors that influence the water pollution indicator of DO data the Mahakam watershed in 2020 are total suspended solid (TSS), total dissolved solid (TDS), nitrate and ammonia.

Downloads

Download data is not yet available.

References

Casella, G. & Berger, R. L. (2002). Statistical Inference Second Edition. Cambridge: Duxbury Thomson Learning.
Gujarati, D. (2003). Ekonometrika Dasar. Jakarta: Erlangga.
Kutner, M.H., Nachtsheim, C.J., Neter, J. & Li, W. (2005). Applied Linear Statistical Model Fifth Edition. New York: McGraw-Hill.
Meliana, A,. dkk. (2013). “The Comparison of Generalized Poissson Regression and Negative Binomial Regression Methods In Overcoming Overdispersion”. International Journal Of Scientific & Technology. 8(2), 255-258.
Mufidah, A. S & Purhadi. (2016). “Analisis Survival Pada Pasien Demam Berdarah Dengue (DBD) di RSU Haji Surabaya Menggunakan Model Regresi Weibull”. Jurnal Sains dan Seni ITS.5(2), 301-306.
Pawitan, Y. (2001). In All Likelihood. Statistical Modelling and Inference Using Likelihood. England: Clarendon Press-Oxford. Rahmah, S.A., Suyitno & Siringoringo, M. (2021).
“Model Geographically Weighted Weibull Regression pada Indikator Pencemaran Air Biochemical Oxygen Demand di Daerah Aliran Sungai Mahakam”. Jurnal EKSPONENSIAL. 12(2), 119-128. ISSN: 2085-7829.
Rencher, A.C. (2000). Linear Model in Statistics. New York: John Wiley-Sons, Inc.
Rinne, H. (2009). The Weibull Distribution A Handbook. New York: CRC Press Taylor and Francis Group.
Salmin. (2005). “Oksigen Terlarut (DO) dan Kebutuhan Oksigen Biologi (BOD) Sebagai Salah Satu Indikator Untuk Menentukan Kualitas Perairan”. Oseana. 30(3), 21-26. ISSN: 0216-1877.
Sastrawijaya, A. (2009). Pencemaran Lingkungan. Jakarta: Rineka Cipta.
Suyitno. (2017). “Penaksiran Parameter dan Pengujian Hipotesis Model Regresi Weibull Univariat”. Jurnal EKSPONENSIAL. 8(2), 179-184. ISSN: 2085-7829.
Yanti, H., Suyitno & Purnamasari, I. (2022). “Model Hazard Rate Weibull Kesembuhan Pasien Rawat Inap Penyakit Jantung Koroner di RSUD Abdul Wahab Sjahranie Samarinda”. Prosiding Seminar Nasional Matematika, Statistika, dan Aplikasinya. (2), 429-455. ISSN: 2657-232X.
Published
2023-05-31
How to Cite
SUDARMAN, Alfiannur Rizki; SUYITNO, Suyitno; SIRINGORINGO, Meiliyani. Pemodelan Regresi Weibull Pada Data Kontinu Yang Diklasifikasikan (Studi kasus: Data Indikator Pencemaran Air Dissolved Oxygen Pada DAS Mahakam Kalimantan Timur Tahun 2020). EKSPONENSIAL, [S.l.], v. 14, n. 1, p. 47-56, may 2023. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/993>. Date accessed: 13 apr. 2024. doi: https://doi.org/10.30872/eksponensial.v14i1.993.
Section
Articles