Analisa Citra satelit dan Machine Learning untuk Prediksi Abrasi Pantai
Metode
Research Location
Pantai Utara Tangerang
Comparison Data Classification Using ML methods
MARS (Multivariate Adaptive Regression Splines Algorithms)
K-NN
CART (Classification and Regression Trees Aplines Algorithms)
Random Forest
Testing Results of Accuracy and Validation
MNDWI Prediction Abration
NDVI and MSAVI Prediction
MPE
ME
MAPE
MSE
RMSE
Pendahuluan
Tingkat keparahan abrasi pantai
Kondisi abrasi pantai
Indonesia sebagai negara kepulauan memiliki potensi terhadap abrasi pantai
Dataset
Radio Metric Correction
MSAVI
Vegetasi (NDVI)
Kerapatan RTH (SAVI)
MNDWI
VHI
Satelite Imagery Data
Satelit Landsat 8 OLI/TIRS memiliki sensor Onboard
Operational Land Imager (OLI)
Ekstraksi Data dari Citra Landat 8 OLI
Band 9
Band 8
Band 7
Band 6
Band 5
Band 4
Band 3
Band 2
Band 1
Data Preprocessing
www.earthexplorer.usgs.gov
Studi Literatur
Compare and Contrast
Conference Publications
National Journal Publications
MANGROVE MONITORING USING NORMALIZED
DIFFERENCE VEGETATION INDEX (NDVI): CASE STUDY
IN NORTH HALMAHERA, INDONESIA
Perubahan Konversi Lahan
Menggunakan NDVI, EVI, SAVI
dan PCA pada Citra Landsat 8
(Studi Kasus : Kota Salatiga)
Tsunami Vulnerability and Risk
Assessment Using Machine
Learning and Landsat 8
International Journal Publications
Drought Analysis and Forecast Using Landsat-8 Sattelite
Imagery, Standardized Precipitation Index and Time Series
Computer model for tsunami vulnerability
using sentinel 2A and SRTM images
optimized by machine learning
