Model Deteksi Tutupan Lahan di Kecamatan Gunungsitoli Menggunakan Algoritma Decision Tree Berbasis Machine Learning

Authors

  • Amati Eltriman Hulu IPB University
  • Mizero Alexis University of Rwanda

DOI:

https://doi.org/10.62411/tc.v24i3.12955

Abstract

Perkembangan teknologi penginderaan jauh semakin berkembang, integrasi data penginderan jauh dan artificial intelligence-machine learning menjadi pendekatan yang sangat efisien dalam mendeteksi tutupan lahan. Penelitian ini bertujuan untuk untuk membangun model algoritma tutupan lahan menggunakan algoritma decision tree. Data yang digunakan yakni Citra PlanetScope NICFI Level 1 yang diturunkan menjadi beberapa indeks spektral yang terdiri atas Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI),  Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), dan Green-Red Vegetation Index (GRVI). Untuk mengukur setia variabel digunakan Information Gain, Gini Index, dan Gain Ratio. Hasil penelitian menunjukan bahwa SAVI dan NDVI merupakan variabel yang informatif dalam membangun model. Distribusi tutupan lahan di Kecamatan Gunungsitoli didominasi oleh tutupan hutan. Kata Kunci – Decision Tree, Machine Learning, Tutupan Lahan, Gunungsitoli

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Published

2025-08-18

Issue

Vol. 24 No. 3 (2025): Agustus 2025

Section

Articles

License

Copyright (c) 2025 Amati Eltriman Hulu, Mizero Alexis

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