Diyanah, Izzatud (2023) Identifikasi Kawasan Permukiman Kumuh Menggunakan Algoritma NDBI dan OBIA dari Citra Sentinel-2 Tahun 2022 (Studi Kasus: Kecamatan Kenjeran Surabaya). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Permukiman kumuh merupakan permukiman yang tidak layak huni karena ketidakteraturan bangunan, tingkat kepadatan bangunan yang tinggi, dan kualitas bangunan serta sarana prasarana yang tidak memenuhi syarat. Identifikasi kawasan permukiman kumuh merupakan proses awal untuk menentukan kawasan kumuh yang menjadi dasar penentuan kebijakan dan penanganan permasalahan kawasan pemukiman kumuh. Perkembangan teknologi penginderaan jauh dapat dimanfaatkan untuk mengidentifikasi kawasan permukiman kumuh melalui pemetaan pola sebaran spasial permukiman kumuh. Pengolahan data citra satelit untuk identifikasi kawasan permukiman kumuh dapat dilakukan dengan berbagai cara, contohnya dengan menggunakan algoritma Normalized Difference Built-Up Index (NDBI) dan Object-Based Image Analysis (OBIA). Penelitian ini bertujuan untuk menerapkan dua teknik tersebut dalam identifikasi kawasan permukiman kumuh menggunakan data penginderaan jauh citra Sentinel-2A di Kecamatan Kenjeran. Kawasan permukiman kumuh yang dapat diidentifikasi menggunakan metode NDBI seluas 198,474 hektar dan metode OBIA seluas 189,396 hektar dari total luas wilayah Kecamatan Kenjeran ±865,666 hektar. Sebesar 22% hingga 23% wilayah di Kecamatan Kenjeran dikategorikan ke dalam kawasan permukiman kumuh. Sebaran kawasan permukiman kumuh di Kecamatan Kenjeran didominasi oleh Kelurahan Tanah Kali Kedinding dan Kelurahan Sidotopo Wetan. Uji akurasi metode NDBI menghasilkan overall accuracy 86% dan kappa accuracy 79%, sementara metode OBIA menghasilkan overall accuracy 89% dan kappa accuracy 83%. Metode OBIA menghasilkan akurasi yang lebih baik dalam mengidentifikasi kawasan permukiman kumuh di Kecamatan Kenjeran.
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Slums are settlements that are uninhabitable due to building irregularities, high building density, and inadequate quality of buildings and infrastructure. The identification of slum areas is the initial process to determine the slum areas that are the basis for determining policies and handling slum area problems. The development of remote sensing technology can be utilized to identify slum areas through mapping the spatial distribution pattern of slums. Processing satellite image data for slum area identification can be done in various ways, for example by using the Normalized Difference Built-Up Index (NDBI) and Object-Based Image Analysis (OBIA) algorithms. This research aims to apply these two techniques in the identification of slum areas using Sentinel-2A remote sensing data in Kenjeran sub-district. The slum area that can be identified using the NDBI method is 198.474 hectares and the OBIA method is 189.396 hectares from the total area of Kenjeran Sub-district of ±865.666 hectares. 22% to 23% of the area in Kenjeran Sub-district is categorized into slum areas. The distribution of slum areas in Kenjeran Sub-district is dominated by Tanah Kali Kedinding Urban Village and Sidotopo Wetan Urban Village. The accuracy test of NDBI method resulted in overall accuracy of 86% and kappa accuracy of 79%, while the OBIA method resulted in an overall accuracy of 89% and kappa accuracy of 83%. The OBIA method produces better accuracy in identifying slum areas in Kenjeran Sub-district.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Normalized Difference Built-Up Index, Object-Based Image Analysis, Penginderaan Jauh, Permukiman Kumuh, Normalized Difference Built-Up Index, Object-Based Image Analysis, Remote Sensing, Slums. |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing H Social Sciences > HN Social history and conditions. Social problems. Social reform > HN690.5 Slums |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Izzatud Diyanah |
| Date Deposited: | 02 Aug 2023 08:41 |
| Last Modified: | 02 Aug 2023 08:41 |
| URI: | http://repository.its.ac.id/id/eprint/100017 |
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