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All Journal Jurnal Informatika CommIT (Communication & Information Technology) Jurnal Tekno Insentif Jurnal Telematika Jurnal Teknik Informatika (JUTIF)
Ken Ratri Retno Wardani
Departemen Teknik Informatika, Institut Teknologi Harapan Bangsa
Computer Science & IT Electrical & Electronics Engineering

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You Only Look Once v5 and Long Short-Term Memory Implementation for Crowd Anomaly Detection Wardani, Ken Ratri Retno; Chrisandy, Nicholas; Martina, Inge; Heryanto, Hery
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4224

Abstract

In Indonesia, 116,000 traffic accidents and 370,747 workplace accidents occurred in 2023, emphasizing the urgent need for effective surveillance systems for monitoring crowded areas such as public sidewalks, roads, workplaces, and school hallways. This study introduces a novel approach combining You Only Look Once v5 (YOLOv5) and Long Short-Term Memory (LSTM) networks for crowd anomaly detection. Unlike traditional methods, this hybrid framework utilizes YOLOv5 for precise feature extraction from video frames and LSTM to capture temporal dependencies for detecting anomalous behaviors. The dataset used includes scenes from the Crowd Anomaly Detection UML Dataset, consisting of a 1-minute and 11-second video extracted into 852 images. Hyperparameter tuning was conducted for epochs and learning rates in the YOLOv5 model, as well as for epochs and units in the LSTM model. The proposed framework achieved remarkable results, with 98% accuracy, 100% precision, and 86% F1-Score. However, improvements in class distribution within the training data could enhance model performance further. These findings demonstrate the potential of the proposed method for real-world applications in improving public safety and effective anomaly detection. This research proves that the proposed method which uses separate feature extraction method before detecting anomaly provides a better result in crowd anomaly detection.
Analisis Pengaruh Karakteristik Masukan Teks terhadap Kinerja MiniLMv2-L6-H384 dan BERT-Base-Uncased pada Quora Question Pairs Ken Ratri Wardani; Inge Martina; Jimmy Fong Xin Wern
Jurnal Telematika Vol. 20 No. 2 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i2.775

Abstract

Knowledge distillation merupakan teknik untuk menyederhanakan model bahasa besar menjadi model yang lebih ringkas dengan tetap mempertahankan akurasi. Bidirectional encoder representations from transformers (BERT) menawarkan kinerja kuat namun memerlukan sumber daya komputasi besar, sedangkan mini language model (MiniLM) memiliki ukuran model lima kali lebih kecil. Penelitian ini bertujuan membandingkan kinerja kedua model tersebut pada dataset quora question pairs dengan fokus pada pengaruh sequence length dan kelangkaan token (token rarity) terhadap akurasi klasifikasi. Kedua model dilatih menggunakan parameter pelatihan yang identik. Hasil pengujian menunjukkan BERT mencapai akurasi 91,22% dan F1-score 88,17%, sedikit lebih unggul dari MiniLM yang mencapai akurasi 90,12% dan F1-score 86,73%. Namun, MiniLM memberikan kecepatan inferensi 5,3 kali lebih cepat. Temuan ini memberikan panduan empiris untuk optimalisasi model untuk lingkungan dengan keterbatasan sumber daya komputasi atau kebutuhan respons real-time, di mana efisiensi MiniLM dapat diterima dengan sedikit penurunan akurasi. Penelitian mendatang disarankan untuk mengeksplorasi sistem hibrida yang mengalihkan tugas kompleks ke model besar dan tugas umum ke model yang lebih kecil.
Co-Authors Agaputra, Muhammad Dendy Angelico, Jovin Chrisandy, Nicholas Cristanto, Johanes Edwin Edwin Gunadarma, Adhika Hery Heryanto Inge Martina Jimmy Fong Xin Wern Jovin Angelico Laurentius Leonardi Martina, Inge Nina Yuliana Ria Chaniago Siswanto, Elisafina The Houw Liong Wijaya, Jayme Yeremia