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All Journal Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Kirana Putri Fercia
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Computer Science & IT Electrical & Electronics Engineering

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Klasifikasi Jenis Lamun Menggunakan Ekstraksi Fitur GLCM dan Algoritma K-Nearest Neighbor (KNN) M. Mudaffarsyah; Muhammad Azza Al Kausar; Obi Luter Sihombing; Halta Putra Ash Sidiq; Kirana Putri Fercia; Nurul Hayaty
Sustainable Vol 13 No 2 (2024): Jurnal Sustainable : Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/nzpd9n52

Abstract

Seagrass is a type of flowering plant (Angiospermae) that grows fully submerged in shallow coastal waters and estuaries, playing a vital role in marine ecosystems. Currently, seagrass species identification is still performed manually by experts, which is time-consuming, costly, and labor-intensive. To support more efficient conservation and ecological monitoring, an automated, fast, and accurate method is needed. This study proposes the combination of the K-Nearest Neighbors (KNN) algorithm for classification and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. The seagrass image data was obtained from the Roboflow website, and the value of k used in KNN was set to 3. Feature extraction using GLCM was conducted at angles of 0°, 45°, 90°, and 135°. The results showed the highest accuracy at k=3, with 77.42% accuracy on training data and 73.33% on testing data. Therefore, the combination of KNN and GLCM has proven capable of providing fairly accurate results in identifying seagrass species.
Co-Authors Halta Putra Ash Sidiq Hayaty, Nurul M. Mudaffarsyah Muhammad Azza Al Kausar Obi Luter Sihombing