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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Carissa Fathinah Balti
IPB University
Automotive Engineering Computer Science & IT Control & Systems Engineering

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People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Ahmad Hasan Faqih Aulia; Carissa Fathinah Balti; Keisyah Zahra Anatasya; Gema Parasti Mindara; Endang Purnama Giri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1933

Abstract

Accurate people counting in dynamic environments remains challenging due to variations in lighting, complex backgrounds, and occlusion. This study proposes a video-based people counting system leveraging a Convolutional Neural Network (CNN) integrated with the YOLOv5 object detection model. The system applies a structured preprocessing pipeline, including frame extraction, normalization, and noise reduction, to enhance data consistency before detection. The model was evaluated using ten real-world campus video sequences to assess detection reliability and counting accuracy. Experimental results demonstrate that the proposed method achieves high precision and recall for real-time detection across diverse scenarios. Performance degradation was observed in frames containing dense crowds or low illumination, indicating limitations under extreme conditions. These findings validate the feasibility of lightweight CNN-based detectors for surveillance and monitoring applications, while highlighting the need for larger datasets and optimized training strategies to improve robustness in more complex environments.
Komparasi Arsitektur Densenet121, MobilenetV2, dan Resnet50 Untuk Klasifikasi Awan Menggunakan Transfer Learning Yashin Al Fauzy Sabara; Carissa Fathinah Balti; Ahmad Hasan Faqih Aulia; Dodik Ariyanto; Faldiena Marcelita; Mayanda Mega Santoni
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/6hwf0s96

Abstract

Cloud image classification plays an important role in supporting atmospheric analysis and the development of artificial intelligence-based weather prediction systems. However, visual similarities among cloud types and variations in lighting conditions remain major challenges in automatic classification tasks. This study aims to compare the performance of three Convolutional Neural Network (CNN) architectures, namely DenseNet121, MobileNetV2, and ResNet50, for cloud image classification using a transfer learning approach. The dataset used in this study was the TJNU Ground-based Cloud Dataset, consisting of seven cloud classes with a total of 19.000 images; after removing the Mixed Clouds category with the total of 18.047 images; after removing. The research stages included pre-processing, data augmentation using the Color Jitter technique, model training through feature extraction and fine-tuning, and evaluation using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results showed that ResNet50 achieved the best performance with an accuracy of 90,58% and an F1-score of 91,38%, followed by DenseNet121 with 89,53% accuracy and MobileNetV2 with 85,10% accuracy. In addition to obtaining the highest classification performance, ResNet50 also demonstrated good computational efficiency with the fastest training time of 20.9 minutes. These findings indicate that architectures based on residual learning are more effective in capturing the visual characteristics of cloud images compared to the other architectures evaluated in this study.
Co-Authors Ahmad Hasan Faqih Aulia Dodik Ariyanto Endang Purnama Giri Gema Parasti Mindara Keisyah Zahra Anatasya Marcelita, Faldiena Mayanda Mega Santoni Yashin Al Fauzy Sabara