Ikhsan, Yusuf Nur Fanani (2023) Analisis Prediksi Penurunan Tanah Menggunakan Metode Artificial Neural Network di Desa Kedungbanteng dan Banjarasri, Sidoarjo. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penurunan tanah menjadi salah satu fenomena yang terjadi di berbagai kota besar, yang salah satunya di Desa Kedungbanteng dan Banjarasri, kabupaten Sidoarjo. Salah satu faktor yang mendukung penurunan tanah di Desa Kedungbanteng dan Banjarasri adalah aktivitas geologis seperti penambangan dan perminyakan. Teknologi yang digunakan dalam monitoring penurunan tanah adalah time-series InSAR. Meskipun demikian, time-series InSAR mampu digunakan untuk mengobservasi penurunan tanah, hasil deformasi time-series masih jarang digunakan untuk prediksi penurunan tanah. Salah satu metode yang digunakan untuk prediksi adalah jaringan saraf (neural network). Berdasarkan pada hasil trendline prediksi, pola trendline dari model menunjukkan adanya penurunan secara temporal. Secara spasial, pergerakan daerah penurunan tanah cenderung menuju ke arah barat yaitu kawasan Porong. Hasil prediksi model yang didapatkan pada bulan Januari 2021 dengan penurunan tanah berkisar -501,246 – 241,411 mm, bulan Maret 2021 dengan rentang -510,74 hingga 234,434 mm, bulan Mei 2021 dengan rentang -541,157 – 276,46 mm, bulan Juli 2021 dengan rentang -550,467 – 270,119 mm, bulan Oktober 2021 dengan rentang -577,178 – 257,552 mm dan bulan Desember 2021 dengan rentang -589,107 – 245,002 mm. Hasil model prediksi dilakukan Uji akurasi menggunakan RMSE didapatkan hasil sebesar 39 mm. Berdasarkan hasil uji korelasi yang dilakukan, didapatkan bahwa korelasi dari setiap hasil prediksi pada setiap titik menunjukkan adanya korelasi yang kuat. Berdasarkan dari uji-t yang dilakukan, Hasil menunjukkan adanya hubungan antara data validasi dengan dengan data prediksi.
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Land subsidence is a phenomenon that occurs in various big cities, one of which is in the villages of Kedungbanteng and Banjarasri, Sidoarjo district. One of the factors that support land subsidence in Kedungbanteng and Banjarasri villages is geological activity such as mining and oil. The technology used in monitoring land subsidence is the InSAR time-series. Even so, the InSAR time-series can be used to observe land subsidence, deformation time-series results are rarely used to predict land subsidence. One of the methods used for prediction is a neural network. Based on the predicted trendline results, the trendline pattern of the model shows a temporal decline. Spatially, the movement of land subsidence tends towards the west, namely the Porong area. The model prediction results were obtained in January 2021 with land subsidence ranging from -501.246 – 241.411 mm, March 2021 with a range of -510.74 to 234.434 mm, May 2021 with a range of -541.157 – 276.46 mm, July 2021 with a range -550.467 – 270.119 mm, in October 2021 with a range of -577.178 – 257.552 mm and in December 2021 with a range of -589.107 – 245.002 mm. The results of the prediction model were carried out. An accuracy test using RMSE obtained results of 39 mm. based on the results of the correlation test conducted, it was found that the correlation of each predicted result at each point indicated a strong correlation. Based on the t-test conducted, all data acquisition times show a relationship between validation data and predictive data.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSG 622.24 Ikh a-1 2023 |
| Uncontrolled Keywords: | Artificial neural network, Penurunan tanah, Phyton, Time-series InSAR |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QE Geology > QE598 Land subsidence |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Ikhsan Yusuf Nur Fanani |
| Date Deposited: | 02 Aug 2023 04:35 |
| Last Modified: | 21 Dec 2023 03:37 |
| URI: | http://repository.its.ac.id/id/eprint/100859 |
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