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All Journal Jurnal Laut Khatulistiwa Jurnal Bangkit Indonesia
Jordy, Roy
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Physics

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Struktur Komunitas Mangrove di Bagian Barat Pulau Cempedak Kecamatan Kendawangan Kalimantan Barat Jordy, Roy; Nurrahman, Yusuf Arief; Apriansyah, Apriansyah
Jurnal Laut Khatulistiwa Vol 8, No 2 (2025): July
Publisher : Dept. Marine Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/lkuntan.v8i2.94200

Abstract

Hutan mangrove merupakan kawasan hutan yang masih terpengaruhi oleh pasang surut air laut. Pulau Cempedak memiliki beberapa ekosistem pesisir, salah satunya yaitu hutan mangrove. Hutan mangrove di Pulau Cempedak tersebar dibeberapa kawasan yaitu bagian Barat. Penelitian ini bertujuan untuk mengetahui bagaimana struktur komunitas mangrove yang berada di Bagian Barat Pulau Cempedak, Kecamatan Kendawangan, Kabupaten Ketapang, Kalimantan Barat. Metode yang digunakan dalam penelitian ini yaitu kuadran transek dengan masing "“ masing kategori pertumbuhan yaitu tingkat pohon (20m x 20m), tiang (10m x 10m), pancang (5m x 5m) dan semai (2m x 2m). Terdapat lima jenis mangrove yang ditemukan yaitu Bruguiera gymnorrhiza, Avicennia lanata, Bruguiera cylindrica, Xylocarpus granatum dan Heritiera littoralis. Jenis mangrove yang mendominasi yaitu Bruguiera gymnorrhiza total individu 80.000 individu/ha dan ditemukan diseluruh Stasiun dan seluruh kategori pertumbuhan dan nilai H"™ yang didapatkan berkisaran 0-1,42.
Perbandingan Metode Ekstraksi Fitur LBP, GLCM, dan Canny dalam Klasifikasi Penyakit Daun Padi dengan KNN Jordy, Roy; Ariatmanto, Dhani
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.452

Abstract

Accurate and timely identification of rice leaf diseases plays a crucial role in supporting early disease control efforts in agriculture. This study aims to compare the performance of three image feature extraction methods—Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Canny Edge Detection—in classifying three types of rice leaf diseases: Bacterial leaf blight, Brown spot, and Leaf smut. Each method was evaluated based on its confusion matrix as well as key performance metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LBP achieved the highest classification performance with an accuracy of 92.06%, followed by GLCM at 78.57% and Canny at 66.67%. In addition to accuracy, LBP also outperformed the other methods across all evaluation metrics. These findings indicate that the local texture features captured by LBP are more effective in distinguishing disease types compared to the global texture features from GLCM and edge-based features from Canny. Therefore, LBP is recommended as a superior feature extraction method for automated classification systems of rice leaf diseases based on digital imagery.
Co-Authors . Apriansyah DHANI ARIATMANTO Yusuf Arief Nurrahman