Aisyiah, Kurniasari (2014) Pemodelan Konsentrasi Partikel Debu (Pm10) Pada Pencemaran Udara Di Kota Surabaya Dengan Metode Geographically-Temporally Weighted Regression. Undergraduate thesis, Institut Teknologi Sepuluh Nopember Surabaya.
Preview |
Text
1310100045-Undergraduate_Thesis.pdf - Published Version Download (2MB) | Preview |
Abstract
Konsentrasi partikel debu (PM10) di Kota Surabaya menempati
urutan pertama di Jawa Timur. Hal ini karena aktifitas penduduk
Kota Surabaya yang tinggi menyebabkan polusi udara. Partikel
debu (PM10) merupakan salah satu polutan yang apabila terhisap
langsung ke dalam paru-paru dan mengendap di alveoli dapat
membahayakan sistem pernafasan. Dalam pemantauan kualitas
udara, seringkali peralatan pengukur konsentrasi partikel debu
(PM10) mengalami kerusakan, sehingga data polutan tersebut
tidak terukur atau tidak tersedia (missing). Mengingat pentingnya
data tersebut, maka perlu dilakukan pendugaan data konsentrasi
partikel debu (PM10) pada lokasi yang tidak terukur. Salah satu
metode yang digunakan adalah Geographically-Temporally
Weighted Regression (GTWR)untuk memprediksi konsentrasi
partikel debu (PM10) dengan menggunakan parameter
meteorologi. Konsentrasi partikel debu bergantung pada lokasi
dan waktu. Hasil penelitian menyimpulkan bahwa kondisi
pencemaran udara di Kota Surabaya pada tahun 2010 masih
dinyatakan baik, artinya bernilai di bawah ambang batas. Hasil
prediksi dengan metode GTWR lebih akurat daripada regresi
nonspasial. GTWR dapat mengakomodasi adanya pengaruh
heterogenitas spasial dan temporal pada konsentrasi partikel
debu (PM10)
=============================================================================================
The concentration of dust particles (PM10) in Surabaya is the
first ranks in East Java. This is because the activity of the
population of Surabaya which causes air pollution. Dust particles
(PM10) is one pollutant that when inhaled directly into the lungs
and settles in the alveoli may be harmful to the respiratory
system. In air quality monitoring, measuring equipment
concentration of dust particles (PM10) is often broken, so the
data is not measured pollutant or not available (missing). Given
the importance of these data, it is necessary to estimate the
concentration data of dust particles (PM10) in locations that are
not measurable. One method used is Geographically-Temporally
Weighted Regression (GTWR)to predict the concentration of dust
particles (PM10) using meteorological parameters. The
concentration of dust particles depends on location and time. The
study concluded that the condition of the air pollution in the city
of Surabaya in 2010 was declared good, meaning valued below
the threshold. The results of the prediction method GTWR is more
accurate than the regression non-spatial. GTWR can
accommodate the influence of spatial and temporal heterogeneity
in the concentration of dust particles (PM10)
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.536 Ais p |
| Uncontrolled Keywords: | Partikel debu (PM10), Regresi, Spasial, Temporal |
| Subjects: | Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | EKO BUDI RAHARJO |
| Date Deposited: | 03 Nov 2020 05:49 |
| Last Modified: | 03 Nov 2020 05:49 |
| URI: | http://repository.its.ac.id/id/eprint/82218 |
Actions (login required)
 |
View Item |