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All Journal IJISTECH (International Journal Of Information System & Technology) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Prihandoko, P
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Enhancing Real Time Crowd Counting Using YOLOv8 Integrated with Microservices Architecture for Dynamic Object Detection in High Density Environments Prihandoko, P; Zufari, Faisal; Yuhandri, Y; Irawan, Yuda
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.575

Abstract

This study presents the implementation of the YOLOv8 algorithm to enhance real-time crowd counting on the ngedatedotid application, which aims to provide accurate crowd density information at various locations. The proposed model leverages the advanced capabilities of YOLOv8 in detecting and localizing head-people objects within crowded environments, even in complex visual conditions. The model achieved a mAP of 85%, outperforming previous models such as YOLO V8'S (78.3%) and YOLO V7 (81.9%), demonstrating significant improvements in detection accuracy and localization capabilities. The custom-trained model further exhibited a detection accuracy of up to 95% in specific scenarios, ensuring reliable and real-time feedback to users regarding crowd conditions at various locations. By implementing a microservices architecture integrated with RESTful API communication, the system facilitates efficient data processing and supports a modular approach in system development, enabling seamless updates and scalability. This architecture allows for independent deployment of services, thereby minimizing system downtime and optimizing performance. The integration of YOLOv8 and the custom-trained model has proven to be effective in enhancing real-time monitoring and detection of crowd density, making it a suitable solution for diverse applications that require dynamic and accurate crowd information. The results indicate that the proposed model and system architecture can provide a robust framework for real-time crowd management, which is crucial for business owners, event organizers, and public safety monitoring. Future research should consider exploring newer versions of YOLO, such as YOLO V9-S, and expanding the dataset to address challenges related to varying lighting conditions, occlusions, and object orientations. Optimizing these factors will further improve the model’s accuracy and reliability, setting a new standard for crowd detection systems in public spaces and enhancing the overall user experience.
Optimasi JST Backpropagation dengan Adaptive Learning Rate Dalam Memprediksi Hasil Panen Padi Prihandoko, P; Alkhairi, Putrama
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.887

Abstract

Artificial Neural Networks (ANN) with the Backpropagation algorithm have been widely applied across various domains, including data prediction tasks. However, one of the primary challenges in implementing Backpropagation is the selection of an optimal learning rate. A learning rate that is too high can lead to unstable convergence, while one that is too low can significantly slow down the training process. To address this issue, this study proposes an optimization of Backpropagation using an Adaptive Learning Rate through the implementation of the Adam optimizer. The objective of this research is to analyze the performance comparison between Standard Backpropagation and Backpropagation with the Adam optimizer in predicting rice harvest yields based on rainfall, temperature, and humidity variables. The dataset consists of 100 synthetic samples generated based on a normal distribution to resemble real-world data. The results show that the use of the Adam optimizer improves the performance of the ANN model compared to the Standard Backpropagation method. Model accuracy increased from 92.04% to 92.99%, while the values of loss, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) decreased significantly, indicating that the model optimized with Adam is more stable and yields lower prediction errors. Therefore, Adaptive Learning Rate optimization using the Adam optimizer is proven to be effective in enhancing both the accuracy and efficiency of ANN in data prediction tasks.
Implementation of Convolutional Neural Networks (CNN) for Crowd Counting in Shopping Mall Environments Prihandoko, P; Wulandari, Natasya; Eska, Juna
IJISTECH (International Journal of Information System and Technology) Vol 8, No 4 (2024): The December edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i4.377

Abstract

Accurate crowd counting is crucial in public spaces such as shopping malls to ensure safety and optimize resource management. This article explores the use of Convolutional Neural Networks (CNN), specifically a modified VGG16 architecture, for real-time crowd counting in shopping mall environments. Using a dataset collected from various crowd scenarios, the model was trained and tested using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the proposed model is effective, achieving higher accuracy compared to traditional methods, thanks to advanced feature extraction techniques. This research offers a robust and scalable solution to enhance security and improve crowd management in commercial spaces.
Implementation of Convolutional Neural Networks (CNN) for Crowd Counting in Shopping Mall Environments Prihandoko, P; Wulandari, Natasya; Eska, Juna
IJISTECH (International Journal of Information System and Technology) Vol 8, No 4 (2024): The December edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i4.377

Abstract

Accurate crowd counting is crucial in public spaces such as shopping malls to ensure safety and optimize resource management. This article explores the use of Convolutional Neural Networks (CNN), specifically a modified VGG16 architecture, for real-time crowd counting in shopping mall environments. Using a dataset collected from various crowd scenarios, the model was trained and tested using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the proposed model is effective, achieving higher accuracy compared to traditional methods, thanks to advanced feature extraction techniques. This research offers a robust and scalable solution to enhance security and improve crowd management in commercial spaces.
Co-Authors Alkhairi, Putrama Eska, Juna Wulandari, Natasya Yuda Irawan Yuhandri, Y Zufari, Faisal