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Purwanto, Eko
Universitas Duta Bangsa Surakarta
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences

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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Analisis Perbandingan Metode Yolo Dan Faster R-CNN Dalam Deteksi Objek Manusia Pratama, Muhammad Ilham; Nurchim, Nurchim; Purwanto, Eko
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2890

Abstract

Human object detection is an important component in surveillance systems, behavior analysis, and crowd management in public spaces such as stadiums, shopping malls, and terminals. However, the detection process often faces obstacles such as inconsistent lighting, complex backgrounds, and high object density. This study aims to compare the performance of two object detection algorithms, namely YOLOv10 and Faster R-CNN, in detecting humans. The dataset used is uniform and covers a wide range of environmental conditions to ensure fair and objective evaluation. This research involves the stages of data collection, pre-processing, model training, testing, and performance evaluation. The test results show that YOLOv10 has a performance advantage with an mAP50 value of 0.75, higher than that of Faster R-CNN which obtained an AP50 of 0.67. Based on these findings, YOLOv10 is recommended for use in applications that require real-time human detection with a high level of accuracy.Kata kunci: YOLOV10; Faster R-CNN; Object Detection AbstrakDeteksi objek manusia merupakan komponen penting dalam sistem pengawasan, analisis perilaku, dan pengelolaan keramaian di ruang publik seperti stadion, pusat perbelanjaan, dan terminal. Namun, proses deteksi sering menghadapi kendala seperti pencahayaan yang tidak konsisten, latar belakang kompleks, dan kepadatan objek tinggi. Penelitian ini bertujuan buat membandingkan kinerja dua algoritma deteksi objek, yaitu YOLOv10 dan Faster R-CNN, dalam mendeteksi manusia. Dataset yang digunakan bersifat seragam dan mencakup berbagai kondisi lingkungan untuk memastikan evaluasi yang adil dan objektif. Penelitian ini melibatkan tahapan pengumpulan data, pra-pemrosesan, pelatihan model, pengujian, dan evaluasi performa. Hasil pengujian menunjukkan bahwa YOLOv10 memiliki keunggulan performa dengan nilai mAP50 sebesar 0,75, lebih tinggi dibandingkan Faster R-CNN yang memperoleh AP50 sebesar 0,67. Berdasarkan temuan tersebut, YOLOv10 direkomendasikan untuk digunakan dalam aplikasi yang membutuhkan deteksi manusia secara real-time dengan tingkat akurasi tinggi.Kata kunci: YOLOV10; Faster R-CNN; Deteksi Objek 
Co-Authors Abdullah Sajad Afu Ichsan Pradana Aghni Rizqi Ni’mal ‘Abdu Agus Mardiyono Agustina Srirahayu Ajeng Putri Sulistyawati Ali Muhdi Anggit Nurhidayah Ari Zusnan Fahrudin Atina, Vihi Bambang Prasetyo Bangun Pradjadi Cipto Utomo Bangun Prajadi Cipto Utomo Didik Nugroho Dwi Septieni Ema Sagita Desylawati Ery Permana Yudha Feny Ramadhani Feri Setiyono Hasanah, Herliyani Ilham Fahrul Pratama Indah Nofikasari Indra Hastuti Intan Oktaviani Kris Dayanti Marginingsih Marginingsih Maulindar, Joni Moch. Edy Purwanto Norma Puspitasari Novita Widyasari Nurchim Nurchim Nurchim, Nurchim Nurmalitasari Nurmalitasari Otami Amalina Permatasari, Hanifah Pipin Widyaningsih Pratama, Muhammad Ilham Ratna Puspita Indah Sabar Sularno Shandra Isti Kharisma Auliya Alamsyah Sigit Gunawan Singgih Purnomo Softi Ulin Nuha Sopingi Sri Ningrum Sukma Rahmawati Triyono Triyono Velinda Febriana Wiji Lestari Wijiyanto Yusmawan Dwi Suseno