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This Author published in this journals
All Journal JOURNAL OF ICT APLICATIONS AND SYSTEM Journal of Computer Science Artificial Intelligence and Communications
Theodora MV Nainggolan
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Implementation of YOLOv8 for Object Detection in Urban Traffic Surveillance A Case Study on Vehicles and Pedestrians from CCTV Imagery Saragih, Rusmin; Imeldawaty Gultom; Frans Ikorasaki; Theodora MV Nainggolan
Journal of ICT Applications System Vol 4 No 1 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i1.430

Abstract

Implementation of the YOLOv8 object detection algorithm for enhancing traffic surveillance through accurate identification of multiple road entities, including cars, motorcycles, trucks, and pedestrians. Using a 41-second CCTV video as the primary dataset, the research adopts a deep learning-based training approach via Google Colab to evaluate YOLOv8's performance under real-world urban conditions. The detection model was assessed using key evaluation metrics such as accuracy, precision, recall, and Mean Average Precision (mAP). The experimental results demonstrate that YOLOv8 achieves an overall detection accuracy of 80%, showing reliable performance in identifying vehicles and people despite challenges such as occlusions, varied lighting, and complex backgrounds. However, accuracy variations were observed in cases involving partial visibility and non-optimal camera angles. The findings highlight the potential of YOLOv8 as a robust and scalable solution for real-time traffic object detection, with implications for smart city development and automated traffic management systems. Further improvements are recommended in dataset diversity and model fine-tuning to enhance detection robustness across dynamic traffic scenarios
Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions Damayanti, Fera; Arief Budiman; Siti Sundari; Theodora MV Nainggolan
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 2 (2024): November 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i2.20

Abstract

Credit risk classification plays a crucial role in supporting financial institutions, especially microfinance institutions, in assessing the ability of customers to repay loans. This study aims to develop a credit risk classification model using the Random Forest method, which is known for its accuracy and robustness in handling classification problems. The research uses a dataset obtained from a microfinance institution consisting of various customer attributes such as income, age, loan amount, repayment history, and employment status. The dataset is preprocessed and divided into training and testing sets to evaluate model performance. The Random Forest algorithm is then applied to build a classification model that categorizes customers into three credit risk levels: low, medium, and high. The results show that the Random Forest model achieves a high level of accuracy, with a classification precision of 89%, recall of 87%, and F1-score of 88%. These findings indicate that Random Forest is an effective technique for credit risk classification and can be implemented by microfinance institutions to support better decision-making in credit approval processes. This research also highlights the potential of machine learning techniques in enhancing credit risk management and minimizing non-performing loans.
Co-Authors Arief Budiman Damayanti, Fera frans ikorasaki Gultom, Imeldawaty Rusmin Saragih, Rusmin Siti Sundari