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Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Nahdlatul Ulama Sumatera Barat
ISSN : -     EISSN : 29876362     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) adalah jurnal yang diterbitkan oleh Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat yang bertujuan untuk mewadahi penelitian di bidang Teknik Informatika dan Sistem Informasi. Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) adalah jurnal ilmiah dalam bidang teknik informatika dan sistem informasi,seperti : Kecerdasan Buatan, Pemrograman Jaringan, Jaringan Komputer, Teknik Komputer, Ilmu Komputer/Informatika, Sistem Informasi, dan Multi Disiplin Penunjang Domain Penelitian Komputasi, Sistem dan Teknologi Informasi dan Komunikasi, dan lain-lain yang terkait. Artikel ilmiah dimaksud berupa kajian teori (theoritical review) dan kajian empiris dari ilmu terkait, yang dapat dipertanggungjawabkan serta disebarluaskan secara nasional maupun internasional. Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) accepts scientific articles with research scopes on: System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Image processing Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Machine Learning Other relevant study topics
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025): November 2025" : 1 Documents clear
KLASIFIKASI TUTUPAN LAHAN SAWAH DAN KELAPA SAWIT MENGGUNAKAN GLCM DAN K-NEAREST NEIGHBOR PADA CITRA UDARA Nabilah Fitriani; Dano Fadilah Amelya Rizki; Soffiana Agustin
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

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Abstract

This study aims to automatically classify rice field and oil palm land cover based on aerial imagery by utilizing the Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction and the K-Nearest Neighbor (KNN) algorithm as the classification method. The dataset consists of 130 training images and 111 test images. The images were processed through cropping and grayscale conversion, followed by texture feature extraction including contrast, correlation, energy, and homogeneity. These features serve as the foundation for distinguishing the unique texture patterns of each land type. The test results show that the K parameter in KNN significantly affects the classification accuracy, with K=7 achieving the best result of 97.30%. Evaluation using a confusion matrix reinforces the effectiveness of the method in distinguishing the two land cover classes. The combination of GLCM and KNN proves to be both efficient and accurate, with great potential to be applied in automated mapping and monitoring systems, particularly in agricultural and plantation contexts.  

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