PEMETAAN TINGKAT KERUSAKAN BANGUNAN PASCA KEBAKARAN PERMUKIMAN DI KELURAHAN KOTA BAMBU UTARA, JAKARTA BARAT.

NAMBI HANCA HAYANA, . (2025) PEMETAAN TINGKAT KERUSAKAN BANGUNAN PASCA KEBAKARAN PERMUKIMAN DI KELURAHAN KOTA BAMBU UTARA, JAKARTA BARAT. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

	Text COVER,KATA PENGANTAR, DAFTAR ISI.pdf Download (2MB)
	Text BAB 1 PENDAHULUAN.pdf Download (1MB)
	Text BAB 2 LANDASAN TEORI.pdf Restricted to Registered users only Download (3MB) | Request a copy
	Text BAB 3 METODOLOGI.pdf Restricted to Registered users only Download (2MB) | Request a copy
	Text BAB 4 HASIL DAN PEMBAHASAN.pdf Restricted to Registered users only Download (2MB) | Request a copy
	Text BAB 5 KESIMPULAN DAN SARAN.pdf Restricted to Registered users only Download (607kB) | Request a copy
	Text FILE DAFTAR PUSTAKA.pdf Download (1MB)
	Text LAMPIRAN DAN DAFTAR RIWAYAT HIDUP.pdf Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Kebakaran permukiman merupakan ancaman utama bagi provinsi DKI Jakarta setelah itu diikuti kejadian banjir dan pohon tumbang. Peristiwa kebakaran yang terjadi pada tanggal 17 Maret 2024 mengakibatkan 95 rumah warga, dan 63 kepala keluarga dengan jumlah 190 jiwa terdampak. Penelitian ini bertujuan untuk memetakan tingkat kerusakan bangunan pasca kebakaran menggunakan metode Object Based Image Analysis (OBIA) berbasis citra udara yang diperoleh melalui drone. Metode yang digunakan merupakan Object Based Image Analysis (OBIA) dengan memanfaatkan pendekatan pada proses klasifikasi memperhitungkan aspek spektral dan aspek spasial yang dihasilkan oleh citra yang digunakan. Analisis dilakukan melalui tahapan segmentasi citra menggunakan algoritma Multiresolution Segmentation, dilanjutkan klasifikasi berbasis algoritma Support Vector Machine (SVM), serta validasi akurasi menggunakan matriks kontingensi. Hasil penelitian menunjukkan bahwa metode OBIA berhasil mengidentifikasi empat kategori kerusakan bangunan yakni hancur, rusak, rusak ringan, dan tidak kerusakan. Berdasarkan pada hasil penelitian ini disimpulkan bahwa penerapan metode OBIA (Object Based Image Analysis) dalam mengidentifikasi tingkat kerusakan bangunan pasca kebakaran permukiman didapatkan overall accuracy 75,20%. Hasil klasifikasi yang sudah dilakukan cukup baik sesuai dengan kondisi di lapangan yang sebenarnya serta mendukung kajian kebutuhan pasca bencana (Jitupasna) secara cepat dan tepat. Kata kunci : Kebakaran, Kerusakan, Bangunan, OBIA ***** Residential fires are a major threat to the DKI Jakarta province, followed by incidents of flooding and fallen trees. The fire incident that occurred on March 17, 2024, affected 95 houses and 63 families, impacting a total of 190 individuals. This research aims to map the level of building damage after the fire using the Object Based Image Analysis (OBIA) method based on aerial images obtained through drones. The method used is Object Based Image Analysis (OBIA), utilizing an approach in the classification process that considers spectral and spatial aspects produced by the images used. The analysis is carried out through stages of image segmentation using the Multiresolution Segmentation algorithm, followed by classification based on the Support Vector Machine (SVM) algorithm, and accuracy validation using a contingency matrix. The research results indicate that the OBIA method successfully identified four categories of building damage, namely destroyed, damaged, lightly damaged, and no damage. Based on these research results, it is concluded that the application of the Object Based Image Analysis (OBIA) method in identifying the level of building damage after a fire in residential areas achieved an overall accuracy of 75.20%. The classification results obtained are quite good in accordance with the actual conditions in the field and support the assessment of post-disaster needs (Jitupasna) quickly and accurately. Keyword : Fire, Damage, Building, OBIA

Item Type:	Thesis (Sarjana)
Additional Information:	1). Dr. Sucahyanto, M.Si. ; 2). Fauzi Ramadhoan A'Rachman, S.Pd., M.Sc.
Subjects:	Geografi, Antropologi > Geografi
Divisions:	FIS > S1 Geografi
Depositing User:	Users 29328 not found.
Date Deposited:	06 Aug 2025 05:44
Last Modified:	06 Aug 2025 05:44
URI:	http://repository.unj.ac.id/id/eprint/58344

Actions (login required)

View Item