ESTIMASI KONSENTRASI POLUTAN NITROGEN DIOKSIDA (NO2) DAN KARBON MONOKSIDA (CO) DI SURABAYA MENGGUNAKAN METODE ADAPTIVE KALMAN FILTER

Palwaguna, Saka (2024) ESTIMASI KONSENTRASI POLUTAN NITROGEN DIOKSIDA (NO2) DAN KARBON MONOKSIDA (CO) DI SURABAYA MENGGUNAKAN METODE ADAPTIVE KALMAN FILTER. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pencemaran udara menjadi masalah serius di zaman kemajuan teknologi saat ini. Permasalahan ini menjadi masalah yang serius di berbagai negara termasuk di Indonesia. Faktor terjadinya pencemaran udara dibagi menjadi dua jenis, yang pertama yaitu pencemaran akibat sumber alamiah (natural sources) seperti letusan gunung berapi, dan yang kedua berasal dari kegiatan manusia (anthropogenic sources) seperti yang berasal dari asap kendaraan bermotor, emisi pabrik, dan lain-lain. Jumlah kendaraan bermotor meningkat setiap tahunnya. Karena hal tersebut, pencemaran udara semakin meningkat dan kelestarian alam semakin terancam. Peningkatan tersebut berdampak signifikan terhadap meningkatnya konsentrasi karbon monoksida (CO) di udara yang memberikan gangguan pada proses fotosintesis. Selain karbon monoksida (CO), nitrogen dioksida (NO2) juga menjadi salah satu polutan yang berasal dari gas pembuangan kendaraan bermotor yang dapat menimbulkan penurunan fungsi paru-paru, sesak nafas, hingga berujung pada kematian. Berdasarkan pada permasalahan tersebut, untuk mengetahui kualitas udara yang memiliki kadar polutan yang tinggi atau rendah, maka perlu dilakukan estimasi untuk mendeteksi kadar polutan pencemaran udara yang ada di suatu wilayah. Pada penelitian tugas akhir ini, akan dilakukan estimasi polutan dengan menggunakan zat polutan karbon monoksida (CO) dan gas nitrogen dioksida (NO2) menggunakan model Difusi Adveksi dengan metode Adaptive Kalman Filter. Konsentrasi polutan Karbon Monoksida (CO) dengan metode Adaptive Kalman Filter menghasilkan nilai MAPE sebesar 0.02134% yang menyatakan bahwa estimasinya akurat. Kemudian, konsentrasi dari polutan Natrium Dioksida (NO2) dengan metode Adaptive Kalman Filter menghasilkan nilai MAPE sebesar 0.0005129% yang menyatakan bahwa estimasinya akurat.

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Air pollution is a serious problem in this age of technological advancement. This problem has become a serious problem in various countries including Indonesia. The factors of air pollution are divided into two types, the first is pollution due to natural sources (natural sources) such as volcanic eruptions, and the second comes from human activities (anthropogenic sources) such as those from motor vehicle fumes, factory emissions, and others. The number of motorised vehicles increases every year. Because of this, air pollution is increasing and natural sustainability is increasingly threatened. This increase has a significant impact on increasing the concentration of carbon monoxide (CO) in the air, which disrupts the photosynthesis process. In addition to carbon monoxide (CO), nitrogen dioxide (NO2) is also one of the pollutants originating from motor vehicle exhaust gases that can cause decreased lung function, shortness of breath, and lead to death. Based on these problems, to determine the quality of air that has high or low levels of pollutants, it is necessary to estimate to detect the levels of air pollution pollutants in an area. In this final project research, pollutant estimation will be carried out using carbon monoxide (CO) and nitrogen dioxide (NO2) pollutants using the Advection Diffusion model with the Adaptive Kalman Filter method. The concentration of Carbon Monoxide (CO) pollutant with the Adaptive Kalman Filter method produces a MAPE value of 0.02134% which states that the estimation is accurate. Then, the concentration of the pollutant Sodium Dioxide (NO2) with the Adaptive Kalman Filter method produces a MAPE value of 0.0005129% which states that the estimate is accurate.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Pencemaran Udara, Karbon Monoksida, Nitrogen Dioksida, Difusi Adveksi, Adaptive Kalman Filter. Air Pollution, Carbon Monoxide, Nitrogen Dioxide, Diffusion Advection, Adaptive Kalman Filter
Subjects:	Q Science > QA Mathematics > QA371 Differential equations--Numerical solutions Q Science > QA Mathematics > QA401 Mathematical models. Q Science > QA Mathematics > QA402.3 Kalman filtering. Q Science > QA Mathematics > QA911 Fluid dynamics. Hydrodynamics
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Saka Palwaguna
Date Deposited:	06 Aug 2024 08:16
Last Modified:	06 Aug 2024 08:16
URI:	http://repository.its.ac.id/id/eprint/112376

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