Tinjauan Literatur Sistematis Tentang Algoritma Deteksi Objek untuk Sistem Otonom: Evaluasi Arsitektur CNN, Tantangan Dataset, dan Strategi Implementasi: Systematic Literature Review on Object Detection Algorithms for Autonomous Systems: Evaluation of CNN Architectures, Dataset Challenges, and Implementation Strategies

Mohammad Yasser Arafat; Muhammad Bagus Andra

doi:10.57152/malcom.v6i2.2584

Authors

Mohammad Yasser Arafat Universitas Nusa Mandiri
Muhammad Bagus Andra Universitas Nusa Mandiri

DOI:

https://doi.org/10.57152/malcom.v6i2.2584

Keywords:

Arsitektur, Convolutional Neural Network, Dataset Challenges, Object Detection, Sistem Otonom

Abstract

Sistem deteksi objek berbasis Convolutional Neural Network (CNN) menjadi komponen penting dalam pengembangan teknologi otonom seperti kendaraan tanpa pengemudi, robot mobile, dan sistem pengawasan cerdas. Penelitian ini melakukan tinjauan literatur sistematis menggunakan kerangka PRISMA 2020 untuk mengevaluasi arsitektur CNN, tantangan dataset, serta strategi implementasi pada sistem dengan keterbatasan komputasi. Dari 30 artikel yang diidentifikasi, hanya enam artikel primer berkualitas tinggi (2021–2025) yang memenuhi kriteria seleksi ketat dari berbagai basis data akademik. Hasil analisis menunjukkan bahwa arsitektur hybrid yang menggabungkan attention mechanism dan multi-scale feature pyramid networks memberikan performa terbaik, dengan model AttenRetina mencapai mAP 0.86 pada dataset KITTI. Tantangan utama dalam dataset meliputi deteksi objek kecil, latar belakang kompleks, oklusi parsial, serta variasi pencahayaan. Masalah ini diatasi melalui penggunaan dynamic loss functions dan teknik data augmentation. Untuk implementasi pada perangkat dengan sumber daya terbatas, arsitektur ringan seperti SSD MobileNetv2 dan YOLOv8-MobileNetV3 terbukti mampu memberikan keseimbangan optimal antara akurasi dan efisiensi. Secara keseluruhan, studi ini menawarkan panduan komprehensif bagi pengembang dalam memilih arsitektur, menyiapkan dataset, dan merancang strategi deployment sesuai kebutuhan aplikasi otonom.

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