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Journal : Journal Of Artificial Intelligence And Software Engineering

Smart Parking Space Detection Using Advanced Deep Learning Techniques Aguswandi, Lalu Heri; Triwijoyo, Bambang Krismono; Martono, Galih Hendro
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6473

Abstract

This study aims to develop an accurate and efficient empty parking slot detection model to assist users in finding parking spaces. The developed model utilizes YOLOv11 as a pretrained model and demonstrates excellent performance with a precision of 99%, recall of 99%, and a Mean Average Precision (mAP) of 99%. These results validate the model's ability to accurately detect empty parking slots with 100 training epochs. Additionally, the model operates in real-time with a frame rate of 25 frames per second (FPS)
Sentiment Analysis of Service and Facility Satisfaction at Computer Lab of Universitas Bumigora Using Indobert Mundika, Eko; Martono, Galih Hendro; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6798

Abstract

Computer laboratories have a strategic role in supporting the technology-based learning process at Bumigora University. To understand the extent to which the available services and facilities meet students' expectations, this study conducted a sentiment analysis of student reviews using the IndoBERT model, an artificial intelligence-based Natural Language Processing (NLP) approach. Data was obtained from a questionnaire focusing on aspects of laboratory services and facilities, then analyzed to classify opinions into positive, negative, and neutral sentiments. The analysis results show the dominance of positive sentiments, indicating that computer laboratories have generally met student expectations, especially in supporting practicum activities. The IndoBERT model used was able to achieve 85% accuracy, demonstrating its effectiveness in reliably identifying opinion trends. These findings provide a comprehensive picture of student perceptions, and serve as an important basis for managers in formulating strategies to improve the quality of laboratory services and facilities so that a conducive and relevant learning experience can be maintained.