KOMPARASI ALGORITMA SUPPORT VECTOR REGRESSION (SVR) DAN REGRESI LINIER DEKOMPOSISI POLAR (RLDP) UNTUK PREDIKSI KEHIJAUAN VEGETASI MANGROVE DENGAN COMBINED MANGROVE RECOGNITION INDEX (CMRI) (STUDI KASUS : TEPI PANTAI TANARA, SERANG, BANTEN)

SAKINAH, FIDIATUS (2022) KOMPARASI ALGORITMA SUPPORT VECTOR REGRESSION (SVR) DAN REGRESI LINIER DEKOMPOSISI POLAR (RLDP) UNTUK PREDIKSI KEHIJAUAN VEGETASI MANGROVE DENGAN COMBINED MANGROVE RECOGNITION INDEX (CMRI) (STUDI KASUS : TEPI PANTAI TANARA, SERANG, BANTEN). S1 thesis, Universitas Mercu Buana Jakarta-Menteng.

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Abstract

Peran mangrove sangat besar seperti mencegah abrasi pantai, sebagai filter air, wadah budidaya hewan laut, dan lain sebagainya. Saat ini kawasan hutan mangrove mengalami penurunan fungsi utama yang signifikan karena perubahan yang terjadi seperti peralihan kawasan hutan menjadi tambak, pencemaran lingkungan, serta penebangan liar. Pada penelitian ini data diperoleh menggunakan citra landsat 8 OLI untuk mendeteksi perubahan pada Pesisir Tanara, Serang, Banten. Karena masalah ini, maka dilakukan penelitian menggunakan ekstraksi fitur Combined Mangrove Recognition Index (CMRI) dengan komparasi antara algoritma Support Vector Regressor (SVR) dan Regression Linier Dekomposisi Polar (RLDP) untuk mengukur berapa persentase perubahan lahan yang terjadi pada kawasan hutan mangrove tersebut. Untuk penelitian mengambil data pada periode 01 Mei hingga 01 Agustus 2019 dengan clipping sebagai proses prepocessing. Hasil penelitian ini memperoleh nilai vegetasi tertinggi 0.974966 yang mengartikan dalam periode 01 Mei hingga 01 Agustus vegetasi hutan mangrove dalam keadaan paling baik. Kata kunci: CMRI, SVR, Mangrove, Vegetasi and Ekstraksi Function of mangroves is very large such as preventing coastal abrasion, as a water filter, marine animal cultivation containers, etc. Currently, mangrove forest areas have experienced a significant decline in key functions due to changes that occur such as the transition of forest areas into ponds, environmental pollution, and illegal logging. In this study, data were obtained using Landsat 8 OLI imagery to detect changes in Tanara Coast, Serang, Banten. Because of this problem, research was conducted using the extraction of the Combined Mangrove Recognition Index (CMRI) feature extraction that compared support vector regressor (SVR) and polar-resolved linear regression (RLDP) algorithms to determine the extent of land change that occurred in this region. For the study took data in the period 01 May to 01 August 2019 with clipping as a preprocessing process. The results of this study obtained the highest vegetation value of 0.974966 which means that in the period from May 1st to August 1st has the best mangrove vegetation. Key words: CMRI, SVR, Mangroves, vegetation and extraction.

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