Application of Machine Learning in Predicting Parking Duration Categories to Support Campus Land Occupancy Management

Mochammad Rabee Fathi Al Fikri; Maheswara Putratama; Rafly Surya Ramadhan; Rizqi Adriyanto Adi Putro; Vitri Tundjungsari

doi:10.59934/jaiea.v5i2.2164

Authors

Mochammad Rabee Fathi Al Fikri Universitas Esa Unggul
Maheswara Putratama Universitas Esa Unggul
Rafly Surya Ramadhan Universitas Esa Unggul
Rizqi Adriyanto Adi Putro Universitas Esa Unggul
Vitri Tundjungsari Universitas Esa Unggul

DOI:

https://doi.org/10.59934/jaiea.v5i2.2164

Keywords:

Machine Learning, Random Forest, Parking Duration Prediction, Occupancy Management, Dashboard Monitoring

Abstract

The uncertainty of parking space availability during peak hours is a major obstacle in operational management at Esa Unggul University, Bekasi Campus. Abundant parking transaction data has so far only been stored as administrative archives without being utilized for strategic decision-making. This study aims to build a prediction model for parking duration categories (Short-Term vs. Long-Term) to support proactive occupancy management. Using a dataset of 1,329 valid transaction records, this study compares the performance of Random Forest and Naive Bayes algorithms. Experimental results show that Random Forest is superior in capturing parking behavior patterns with an accuracy reaching 72.18% and a sensitivity level (Recall) of 79.4% in the Long-Term class. Data pattern findings indicate that vehicles entering between 07:00–09:00 WIB have a dominant probability of parking for a long duration. As a managerial implication, the model's prediction results are integrated into a Web-based Dashboard Monitoring prototype that presents occupancy visualization and traffic trends in real-time. This system is expected to assist parking management in implementing more efficient traffic diversion and parking slot allocation strategies

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