Literatur Reviu Sistematis: Identifikasi Jenis Ular Berbasis Computer Vision
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Systematic Literature Review ini bertujuan untuk mengidentifikasi algoritma-algoritma yang digunakan dalam identifikasi spesies ular yang menggunakan computer vision, mengevaluasi dataset, tingkat akurasi, faktor-faktor yang memengaruhi akurasi, dan keterbatasan yang dihadapi. Melalui tinjauan literatur sistematis, 20 paper terpilih dari tahun 2019-2023, yang didapat dari berbagai sumber literatur. Penelitian-penelitian tersebut mengeksplorasi berbagai strategi untuk mengatasi tantangan pengenalan objek ular secara otomatis, termasuk peningkatan kinerja model, eksplorasi pendekatan baru, dan penerapan solusi efektif. Hasil dari studi literatur menyoroti pentingnya pemrosesan data yang cermat, pemilihan arsitektur model yang tepat, serta penyesuaian parameter algoritma yang optimal dalam mencapai kinerja maksimal pada model-model yang dikembangkan. Beberapa peneliti juga mengemukakan keterbatasan dalam penelitiannya, seperti kualitas dan jumlah dataset, kompleksitas morfologi ular, dan variasi pose ular. Diperlukan kerja sama lintas disiplin dan berbagi pengetahuan untuk mengatasi tantangan ini dan memajukan bidang identifikasi spesies ular melalui computer vision.
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