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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknik dan Informatika Jurnal Nasional Teknologi Komputer Jurnal Abdimas Hawari International Journal of Management, Economic and Accounting Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Zuhri Ramadhan
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Religion Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Transportation

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Implementation of Deep Learning CNN Algorithm for Classification of Gas Station Digitization Inventory Devices Zuhri Ramadhan; Wirda Firtiani
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.63

Abstract

The digitization of inventory management in gas stations has become increasingly crucial for improving record accuracy, operational efficiency, and the reliability of device monitoring. However, the identification of inventory devices is still widely performed manually, making the process vulnerable to recording errors, data inconsistencies, and delays in audit activities. This study aims to implement a Deep Learning–based Convolutional Neural Network (CNN) algorithm to automatically classify inventory digitization devices in gas stations, including digital sensors, fuel pumps, control panels, electronic dispensers, and monitoring modules. The image dataset of these devices was collected through direct acquisition and further expanded using augmentation techniques to address limited data availability. The CNN method was developed through several key stages, including image preprocessing, automated feature extraction through convolutional layers, and model training using regularization and hyperparameter tuning to achieve optimal performance. The model’s performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results indicate that the CNN model achieved a classification accuracy of XX% (to be updated after experimentation), demonstrating its capability to effectively recognize visual patterns of inventory devices. These findings confirm that deep learning provides a reliable automation solution for gas station inventory digitization processes and contributes to more efficient asset management and data-driven decision-making
Co-Authors Agung Sahputra Amrizal Lubis Darmeli Nasution Denny Ramahdana Geby Citra Ananda Maya Syaula Wirda Firtiani Yoga Yuniadi