Analisis Komparatif K-Means Dan Fuzzy C-Means Untuk Klasifikasi Citra Tanah Pada Penentuan Kesesuaian Lahan
Keywords:
Fuzzy C-Means, K-Means, Soil image classification, land suitabilityAbstract
Klasifikasi citra tanah adalah langkah esensial untuk mendukung pemetaan kesesuaian lahan pertanian dengan cara yang efisien dan tepat. Metode tradisional yang masih mengandalkan survei lapangan sering kali memakan waktu dan sumber daya yang banyak, sehingga dibutuhkan pendekatan yang menggunakan pemrosesan citra digital. Penelitian ini bertujuan untuk menilai performa algoritma K-Means dan Fuzzy C-Means (FCM) dalam mengelompokkan citra tanah untuk menentukan tingkat kecocokan lahan pertanian. Data yang digunakan mencakup citra multispektral dari Sentinel-2, model digital elevasi (DEM), serta indeks vegetasi NDVI. Langkah-langkah penelitian mencakup pra-pemrosesan citra, penerapan algoritma pengelompokan, dan evaluasi hasil menggunakan parameter Silhouette Coefficient serta Davies-Bouldin Index.
Temuan penelitian menunjukkan bahwa algoritma Fuzzy C-Means menghasilkan klasifikasi yang lebih halus dan lebih sesuai dengan variasi alami tanah, jika dibandingkan dengan K-Means, yang menghasilkan batas kelas yang lebih jelas namun kurang adaptif terhadap transisi antara tipe lahan. Berdasarkan analisis ini, metode Fuzzy C-Means dianggap lebih efektif dalam menggambarkan karakteristik spasial tanah dan direkomendasikan sebagai pendekatan yang tepat untuk pemetaan kesesuaian lahan pertanian yang berbasis citra satelit.
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Copyright (c) 2026 Adzkiya’ Haibatul Ulya, Aswin Rosadi, Erie Kresna Andana (Author)
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