Aisyiah, Kurniasari (2014) Pemodelan Konsentrasi Partikel Debu Pada Pencemaran Udara Di Kota Surabaya Dengan Metode Geographically Temporally Weighted Regression. Other thesis, Insititut Teknologi Sepuluh Nopember.
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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).
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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 (Other) |
|---|---|
| Additional Information: | RSSt 519.536 Ais p |
| Uncontrolled Keywords: | Partikel debu (PM10), Regresi, Spasial, Temporal, Dust particle (PM10), Regression, Spatial |
| Subjects: | Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Marsudiyana - |
| Date Deposited: | 02 Jan 2024 08:17 |
| Last Modified: | 02 Jan 2024 08:17 |
| URI: | http://repository.its.ac.id/id/eprint/105345 |
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