Pemanfaatan Teknologi Machine Learning Untuk Klasifikasi Wilayah Risiko Kekeringan di Daerah Istimewa Yogyakarta Menggunakan Citra Landsat 8 Operational Land Imager (OLI)

Abstract

Drought is a natural disaster that occurs slowly and lasts a long time. Bantul and Gunung Kidul Regencies, Special Region of Yogyakarta are also areas affected by high drought risk. This happened because the area was the result of the construction of a cement factory and limestone mining along the Sewu Mountains. Prediction and classification of areas affected by drought can be done more accurately over large areas by extracting vegetation indices through remote sensing imagery. This research was conducted to provide information about the potential risk of drought in the region using Landsat 8 OLI spectral vegetation index data. Prediction or classification of drought potential using Artificial Neural Network (ANN). Vegetation index used in this study is NDVI, TCI, VCI, and VHI. Correlation results between vegetation indices showed the highest correlation occurred between the vegetation index TCI and VHI with the potential for a medium drought of 0.501 and the potential for a high drought of 0.684. Also obtained are the results of the classification of 9 villages that fall into the category of high drought potential (High Risk). Accuracy results and Kappa values indicate that Random Forest is the best method used with a breakdown of values of 99.91% and 99.81%, respectively. Spatial prediction results are performed using Inverse Distance Weighted (IDW) on vegetation index and prediction. Testing of spatial relationships between villages that have the potential for drought is done using Moran's I. analysis.

Klasifikasi Resiko Kerusakan Lahan Akibat Tsunami Menggunakan Citra Landsat 8 Di Kabupaten Bantul

“…Pada penelitian terdahulu yang berjudul " Pemanfaatan Teknologi Machine Learning Untuk Klasifikasi Wilayah Risiko Kekeringan di Daerah Istimewa Yogyakarta Menggunakan Citra Landsat 8 Operational Land Imager (OLI)", menjelaskan bahwa dengan menggunakan Citra Landsat 8 untuk mengklasifikasikan wilayah risiko kekeringan dan mendapatkan hasil klasifikasi wilayah resiko ini memiliki hasil akurasi sebesar 93.88% menggunakan algoritma ANN [2]. Pada penelitian terdahulu yang berjudul " Analisis Index Vegetasi Pesisir Pantai Aceh Pasca Tsunami Menggunakan Citra Satelit Landsat 7 dan Landsat 8 Dengan Metode Clustering Algoritma K-Means", menjelaskan tentang analisis NDVI wilayah pesisir pantai Aceh dengan membandingkan hasil Analisis Indeks Vegetasi Aceh pada saat sebelum, setelah terjadi bencana Tsunami dan keadaan saat ini dengan menggunakan metode Clustering K-Means.…”