ANALISIS PERUBAHAN TUTUPAN LAHAN DI KAWASAN PIK 2 TAHUN 2013 - 2024 MENGGUNAKAN CITRA LANDSAT DAN KLASIFIKASI ISO CLUSTER
DOI:
https://doi.org/10.37792/jukanti.v8i2.1867Keywords:
citra Landsat, ISO Cluster, klasifikasi unsupervised, NDBI, Perubahan tutupan lahan, PIK 2Abstract
ABSTRAK
Penelitian ini bertujuan untuk menganalisis perubahan tutupan lahan di Kawasan Pantai Indah Kapuk 2 (PIK 2) pada tahun 2013, 2019, dan 2024 menggunakan citra satelit Landsat dan pendekatan klasifikasi unsupervised ISO Cluster. Analisis spasial dilakukan dengan menghitung indeks NDBI (Normalized Difference Built-up Index) untuk memetakan area terbangun. Hasil penelitian menunjukkan bahwa kategori bangunan meningkat secara signifikan dari 15.88 km2 pada tahun 2013 menjadi 31.24 km2 pada tahun 2024 (kenaikan +18.57%). Sebaliknya, lahan terbuka menurun dari 21.07 km2 menjadi 10.25 km2 (penurunan -13.09%). Selain itu, dinamika vegetasi ringan dan vegetasi padat yang menunjukkan bagaimana perubahan lingkungan akibat pembangunan. Evaluasi akurasi klasifikasi menghasilkan nilai Overall Accuracy sebesar 86% dan Kappa Coefficient sebesar 76% yang menunjukkan tingkat keandalan tinggi dalam proses klasifikasi. Secara keseluruhan penelitian ini menunjukkan bahwa SIG dan penginderaan jauh adalah alat yang efektif untuk memantau perubahan tutupan lahan dan membantu pengambilan keputusan dalam perencanaan kawasan pesisir berkelanjutan.
Kata kunci : citra Landsat, ISO Cluster, klasifikasi unsupervised, NDBI, Perubahan tutupan lahan, PIK 2
ABSTRACT
This research aims to analyze land cover change in Pantai Indah Kapuk 2 (PIK 2) area in 2013, 2019, and 2024. This research uses Landsat satellite imagery and the ISO Cluster unsupervised classification approach. Spatial analysis was conducted by calculating the NDBI index (Normalized Difference Built-up Index) to map the built-up area. The results showed that the built-up category increased significantly from 15.88 km2 in 2013 to 31.24 km2 in 2024 (+18.57%). Conversely, open land decreased from 21.07 km2 to 10.25 km2 (-13.09%). In addition, the dynamics of light vegetation and dense vegetation show how the environment changes due to development. Evaluation of classification accuracy resulted in an Overall Accuracy value of 86% and a Kappa Coefficient of 76%, indicating a high level of reliability in the classification process. Overall, this study confirm that GIS and remote sensing are effective tools to monitor land cover change and assist decision-making in sustainable coastal area planning.
Keywords: ISO cluster, land cover change, landsat imagery, NDBI, PIK 2, unsupervised classification
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