LAND USE AND LAND COVER (LULC) CLASSIFICATION WITH MACHINE LEARNING APPROACH USING ORTHOPHOTO DATA
Keywords:
penginderaan jauh, orthophoto, machine learning, LULCAbstract
Penggunaan teknologi penginderaan jauh semakin berkembang, salah satu aplikasinya adalah analisis perubahan penggunaan dan tutupan lahan (LULC). Informasi LULC dibutuhkan untuk berbagai analisis terkait permukaan bumi. Berbagai jenis data digunakan dalam analisis permukaan bumi dengan memanfaatkan data penginderaan jauh. Tujuan dari penelitian ini adalah untuk mengklasifikasikan LULC dengan pendekatan machine learning menggunakan data orthophoto. Lokasi penelitian adalah Desa Tanjung Karang, Mataram, Nusa Tenggara Barat. Metode yang digunakan untuk proses klasifikasi adalah algoritma machine learning yaitu Support Vector Machine (SVM). Dilakukan proses pemisahan band (band slicing) pada data orthophoto yaitu Red, Green, Blue, dan Near Infra Red (NIR). Band Normalized Difference Water Index (NDWI) digunakan untuk analisis badan air yang merupakan refleksi dari band Red dan NIR. Skema klasifikasi klasifikasi yang diterapkan dalam penelitian ini adalah membandingkan klasifikasi antara satu band dan kombinasi band untuk mendapatkan hasil klasifikasi terbaik. Hasil penelitian ini menunjukkan bahwa klasifikasi dengan kombinasi band memiliki akurasi yang lebih baik. Klasifikasi dengan satu band memiliki akurasi rata-rata di bawah 55%, sedangkan kombinasi band memiliki akurasi rata-rata di atas 60%. Hasil klasifikasi dengan nilai akurasi tertinggi adalah kombinasi band R-B-NDWI dengan nilai 71,81%.
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