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  • Mahasiswa Magister Ilmu Komputer Tingkatkan Akurasi Sistem Pengenalan Wajah dalam Berbagai Kondisi Pencahayaan dengan Memodifikasi Metode Zero-DCE

Mahasiswa Magister Ilmu Komputer Tingkatkan Akurasi Sistem Pengenalan Wajah dalam Berbagai Kondisi Pencahayaan dengan Memodifikasi Metode Zero-DCE

  • Berita S2 Ilmu Komputer, Kegiatan
  • 22 Mei 2025, 06.27
  • Oleh: wimbono
  • 0

Yogyakarta, 21 Mei 2025 – Penelitian terbaru dari Muhammad Kahfi Aulia, mahasiswa Magister Ilmu Komputer Universitas Gadjah Mada, berhasil mengembangkan metode Modified Zero-reference Deep Curve Estimation (Zero-DCE) yang secara signifikan meningkatkan akurasi sistem pengenalan wajah dalam berbagai kondisi pencahayaan. Penelitian yang akan diterbitkan pada IAES International Journal of Artificial Intelligence (IJ-AI), sebuah jurnal internasional bereputasi dengan peringkat Q2, ini menawarkan solusi inovatif untuk mengatasi keterbatasan metode Zero-DCE konvensional.

Metode Zero-DCE sebelumnya dikenal efektif untuk meningkatkan kualitas citra dengan pencahayaan rendah ekstrim, namun sering menyebabkan overexposure pada citra dengan kondisi pencahayaan yang lainnya. Kahfi memodifikasi metode ini dengan menghilangkan salah satu fungsi loss, yaitu spatial consistency loss dan memodifikasi jaringan syaraf tiruannya Zero-DCE, yaitu DCE-Net. Penelitian ini dilanjutkan dengan mengkombinasikannya dengan teknik peningkatan kualitas kontras tradisional, seperti Contrast Stretching (CS), Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Brightness Preserving Dynamic Histogram Equalization (BPDHE) kemudian melakukan studi komparasi untuk menemukan kombinasi yang sesuai. Pendekatan ini terbukti mampu mempertahankan fitur-fitur penting wajah yang sering hilang akibat efek overexposure.

Hasil penelitian menunjukkan bahwa kombinasi Modified Zero-DCE dengan metode tradisional menghasilkan peningkatan kualitas citra yang signifikan. Pada dataset Extended Yale Face Database B, Modified Zero-DCE ditambah CLAHE dengan classifier VGG16 berhasil mencapai akurasi pengenalan wajah yang lebih tinggi dibandingkan pendekatan konvensional. Modified Zero-DCE menunjukkan peningkatan efisiensi komputasi yang signifikan, dengan waktu pemrosesan hanya seperempat dari metode Zero-DCE konvensional, sehingga cocok diimplementasikan untuk sistem pengenalan wajah pada waktu nyata.

Penelitian ini memiliki relevansi kuat dengan Tujuan Pembangunan Berkelanjutan (Sustainable Development Goals/SDG), khususnya SDG ke-9 (Industri, Inovasi, dan Infrastruktur). Dalam konteks SDG 9, inovasi ini mendukung pengembangan infrastruktur digital yang lebih inklusif dengan menyediakan teknologi pengenalan wajah yang dapat bekerja optimal di berbagai kondisi lingkungan. 

 

Author : Muhammad Kahfi, Dyah AT

Editor : Marina

#AI #PengenalanWajah #SDGs9 #MIK

Tags: SDGs SDGs 9: Industri Inovasi dan Infrastruktur

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Universitas Gadjah Mada

Departemen Ilmu Komputer dan Elektronika

Fakultas Matematika dan Ilmu Pengetahuan Alam

Universitas Gadjah Mada

Gedung C, Lantai 4

Sekip Utara Bulaksumur Yogyakarta 55281

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