Tinjauan Literatur Sistematis terhadap Metode Shape-Based Feature Extraction dalam Computer Vision
DOI:
https://doi.org/10.46576/device.v7i1.8825Keywords:
Shape-Based Feature Extraction, Computer Vision, Systematic Literature Review, Image Processing, Deep LearningAbstract
Penelitian ini bertujuan untuk menganalisis perkembangan metode shape-based feature extraction dalam bidang computer vision melalui pendekatan systematic literature review. Metode yang digunakan mengacu pada prosedur seleksi literatur berbasis PRISMA dengan tahapan identifikasi, penyaringan, kelayakan, dan inklusi terhadap 22 paper yang relevan. Hasil analisis menunjukkan bahwa metode shape-based feature extraction berkembang dari pendekatan tradisional berbasis fitur geometris menuju metode berbasis deep learning seperti convolutional neural network, You Only Look Once, dan vision transformer. Selain itu, ditemukan bahwa pendekatan hybrid yang menggabungkan fitur berbasis bentuk dan pembelajaran mendalam mulai banyak digunakan untuk meningkatkan akurasi dan performa sistem. Meskipun metode deep learning mendominasi, pendekatan shape-based masih memiliki keunggulan dalam hal interpretabilitas dan efisiensi komputasi. Namun demikian, terdapat beberapa gap penelitian, terutama pada pengembangan metode hybrid yang optimal dan peningkatan interpretabilitas model. Kesimpulan dari penelitian ini menunjukkan bahwa integrasi antara metode shape-based dan deep learning menjadi arah penelitian yang menjanjikan di masa depan.
Kata Kunci: Shape-based feature extraction, computer vision, deep learning, systematic literature review, hybrid method
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