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All Journal Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Seffi Rozahana
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Computer Science & IT Electrical & Electronics Engineering

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KLASIFIKASI JENIS POHON MANGROVE BERDASARKAN CITRA DAUN MENGGUNAKAN METODE K-NEAREST NEIGHBOUR (KNN) Irfan Ibrahim; Maulana Fitra Ramadhani; Muhammad Ridho; M. Wisnu Adjie Pramudya; Putri Suci Renita; Apriliani Putri; Nadia Ayu Putri Priyani; Seffi Rozahana; Adinda; 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/h45hyv18

Abstract

Studi ini dilakukan untuk mengimplementasikan algoritma KNN (K-Nearest Neighbour) dalam klasifikasi bakau menggunakan citra daun. Penelitian ini menggunakan 1.550 data citra daun Mangrove dengan menggunakan python dibagi menjadi empat kelas oleh Avicennia alba, Bruguiera gymnorrhiza, Rhizophora apiculata dan Sonneratia alba. Tingkat keberhasilan klasifikasi yang dicapai oleh sistem menggunakan metode K-Nearest Neighbour mencapai 93,75% dengan nilai k = 3. Hasil penelitian ini menunjukkan bahwa model KNN bisa mengklasifikasi jenis Avicennia alba dan Sonneratia alba dengan jelas, namun terdapat sedikit kesalahan dalam spesies Bruguiera gymnorrhiza dan Rhizophora apiculata karena memiliki kemiripan ciri tekstur antara satu dengan yang lain.
SISTEM KLASIFIKASI JENIS KERANG BERDASARKAN CITRA CANGKANG MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) Adinda; Seffi Rozahana; Nadia Ayu Putri Priyani; Apriliani Putri; Irsyad Widiansyah; 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/hdnn8e89

Abstract

This study aims to build an automatic classification system to identify shellfish types based on shell images by applying the Support Vector Machine (SVM) algorithm. This study classifies three types of shellfish, namely blood cockles with the scientific name Anadara granosa, green mussels (Perna viridis), and scallops (Amusium pleuronectes). Image data was obtained from the internet and each class consisted of 150 images, so the total dataset was 450 images. The research stages include image pre-processing to normalize image size and quality, feature extraction to obtain visual information in the form of texture (with GLCM), color (RGB histogram), and shape (Canny edge detection), and classification using SVM. This application is web-based and functions to receive uploaded shellfish images from users and provide automatic shellfish type recognition results. The test results show that the developed SVM model is able to classify shellfish types with high accuracy, reaching 93,83%. This research is expected to contribute to the development of digital shellfish species identification technology to support the fields of fisheries, marine resource conservation, and marine biota research. 
Co-Authors Adinda Apriliani Putri Hayaty, Nurul Irfan Ibrahim Irsyad Widiansyah M. Wisnu Adjie Pramudya Maulana Fitra Ramadhani Muhammad Ridho Nadia Ayu Putri Priyani Putri Suci Renita