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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Informatika Ekonomi Bisnis MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Komtekinfo Gotong Royong : Jurnal Pengabdian, Pemberdayaan Dan Penyuluhan Kepada Masyarakat (J-P3KM) Jurnal Informatika Ekonomi Bisnis E-Amal: Jurnal Pengabdian Kepada Masyarakat Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Amalia Shifa Aldila
Universitas Jakarta Internasional
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Social Sciences Other

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Studi Perbandingan Performansi Metode Clustering Berbasis Fitur Citra Multispektral untuk Klasifikasi Tutupan Lahan pada Citra Satelit Resolusi Tinggi Rosanti Rosanti; Amalia Shifa Aldila; Gregorio Antonny Bani
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 2 No. 2 (2024): Maret: Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v2i2.1064

Abstract

Land cover classification using multispectral satellite imagery plays a vital role in environmental monitoring, spatial planning, and sustainable resource management, although the high spectral dimensionality and spatial complexity of such data require an effective clustering method to generate accurate segmentation. This study aims to compare the performance of multispectral spectral feature-based clustering algorithms in high-resolution land cover classification. Multispectral features including reflectance bands, NDVI, NDBI, and NDWI were extracted and normalized prior to implementing three algorithms—K-Means, DBSCAN, and Agglomerative Clustering—where the optimal number of clusters was determined using the Elbow method and Silhouette analysis. Performance evaluation using internal validation metrics showed that K-Means achieved the highest Silhouette Score () and the lowest Davies-Bouldin Index (DBI) (), indicating superior intra-cluster compactness and inter-cluster separation compared to both DBSCAN and Agglomerative Clustering. These findings conclude that centroid-based clustering is more effective for handling structured spectral distributions in high-resolution multispectral imagery.
Co-Authors A Andhika Andhika Andhika, A Andini, Siwi Putri Dwi Setiawan Gregorio Antonny Bani Gunawan, Stella Putri Habibie, Dedi Rahman Juniarto, Antonius Lawrence Adi Supriyono Miranti Andhita Scantya Novita, Wanda Okafor, Ngozi Lilian Prasetyo Hartanto Previana, Cantika Nur Putranto, Kartiko Eko Rosanti Rosanti Sarah Khairunnisa Budiyanto Sujatmiko, Devlin Wen Susanto, Chandra Toar, Yandri Ardolof Toar, Yandri Ardolof B.L. Yuli Fitrianto Zhafirah, Rafdah