Perancangan Aplikasi Pintar Monitoring Dan Deteksi Anomali BBM Menggunakan Machine Learning Di PT. BDR
DOI:
https://doi.org/10.36050/cfdvkr55Keywords:
Anomaly, Fuel, Machine Learning, Monitoring, Forest Isolation, TransportationAbstract
In the digital era, the need for technology-based monitoring systems is increasing, especially in the transportation and logistics industry. PT BDR faces challenges in reporting and monitoring fuel consumption for operational vehicles due to the manual recording process, making it difficult to detect anomalous data and making monitoring less efficient. This study aims to develop a web-based application capable of automatically monitoring and detecting fuel consumption anomalies by utilizing machine learning and deep learning technologies. The system development method uses the CRISP-DM approach, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and implementation. The Isolation Forest algorithm is used to detect anomalies based on fuel volume data, mileage, and vehicle consumption ratio, while the MobileNetV2-based Content-Based Image Retrieval (CBIR) method is applied to validate the suitability of gas station photos. The trained model is then integrated into the API using the Flask framework, with testing conducted through blackbox and whitebox testing. The test results show that the system is able to detect anomalies with a good level of accuracy and can be used practically by users. The implementation of this application is expected to improve the company's operational efficiency, reduce potential losses due to fuel misappropriation, and support the digitalization of the fuel monitoring process to be more accurate, effective, and integrated.
References
[1] A. Poolkrajang, “(PDF) An Analysis of Effect on Energy Saving of Trucks in Transport and Logistics Business,” ResearchGate, vol. 13, no. 5, 2023, doi: 10.32479/ijeep.14533.
[2] A. Ali et al., “Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review,” Appl. Sci., vol. 12, no. 19, Art. no. 19, Jan. 2022, doi: 10.3390/app12199637.
[3] P. E. Alcaraz et al., “SCS 5th Annual Conference. São Paulo, Brazil, 2022,” Sports, vol. 11, no. 5, Art. no. 5, May 2023, doi: 10.3390/sports11050095.
[4] R. Canal, F. K. Riffel, and G. Gracioli, “Machine Learning for Real-Time Fuel Consumption Prediction and Driving Profile Classification Based on ECU Data,” IEEE Access, vol. 12, pp. 68586–68600, 2024, doi: 10.1109/ACCESS.2024.3400933.
[5] Z. Ding and M. Fei, “An Anomaly Detection Approach Based on Isolation Forest Algorithm for Streaming Data using Sliding Window,” IFAC Proc. Vol., vol. 46, no. 20, pp. 12–17, Jan. 2021, doi: 10.3182/20130902-3-CN-3020.00044.
[6] W. Chen et al., “Deep Learning for Instance Retrieval: A Survey,” Oct. 30, 2022, arXiv: arXiv:2101.11282. doi: 10.48550/arXiv.2101.11282.
[7] Y. Zou, L. Wu, C. Zuo, L. Chen, B. Zhou, and H. Zhang, “White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism,” Biomed. Signal Process. Control, vol. 99, p. 106820, Jan. 2025, doi: 10.1016/j.bspc.2024.106820.
[8] S. Narduzzi, E. Türetken, J.-P. Thiran, and L. A. Dunbar, “Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform,” Aug. 23, 2022, arXiv: arXiv:2208.11011. doi: 10.48550/arXiv.2208.11011.
[9] C. O. Ogolla, “Security in the sixth generation cellular networks: A review,” World J. Adv. Res. Rev., vol. 25, no. 3, Art. no. 3, Mar. 2025, doi: 10.30574/wjarr.2025.25.3.0634.
[10] W. Rizki and M. Darip, “Penggunaan Algoritma Support Vector Machine Untuk mendeteksi Anomali Aktivitas Pengguna Pada Sistem Informasi Keuangan PT. Digidokat Indonesia,” JATI J. Mhs. Tek. Inform., vol. 9, no. 3, Art. no. 3, May 2025, doi: 10.36040/jati.v9i3.13385.
[11] A. Barbado and Ó. Corcho, “Interpretable machine learning models for predicting and explaining vehicle fuel consumption anomalies,” Eng. Appl. Artif. Intell., vol. 115, p. 105222, Oct. 2022, doi: 10.1016/j.engappai.2022.105222.
[12] P. Sharma, “Advancements in OCR: A Deep Learning Algorithm for Enhanced Text Recognition,” Int. J. Inven. Eng. Sci. IJIES, vol. 10, no. 8, Art. no. 8, Sept. 2023, doi: 10.35940/ijies.F4263.0810823.
[13] A. D. Paola, S. Drago, P. Ferraro, and G. L. Re, “Detecting Zero-Day Attacks under Concept Drift: An Online Unsupervised Threat Detection System”.
[14] O. S. Ndibe, “Integrating Machine Learning with Digital Forensics to Enhance Anomaly Detection and Mitigation Strategies,” Int. J. Res. Publ. Rev., vol. 6, no. 5, pp. 365–388, May 2025, doi: 10.55248/gengpi.6.0525.1992.
[15] “Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery.” Accessed: July 26, 2025. [Online]. Available: https://www.mdpi.com/2073-4441/14/1/82
[16] N. Caballé-Cervigón, J. L. Castillo-Sequera, J. A. Gómez-Pulido, J. M. Gómez-Pulido, and M. L. Polo-Luque, “Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review,” Appl. Sci., vol. 10, no. 15, Art. no. 15, Jan. 2020, doi: 10.3390/app10155135.
[17] E. Richardson, R. Trevizani, J. A. Greenbaum, H. Carter, M. Nielsen, and B. Peters, “The receiver operating characteristic curve accurately assesses imbalanced datasets,” Patterns, vol. 5, no. 6, June 2024, doi: 10.1016/j.patter.2024.100994.
[18] M. Darip, “Implementation of Round Robin Algorithm in Public Transportation Scheduling System at Pakupatan Terminal in Serang City-Indonesia,” J. Sisfokom Sist. Inf. Dan Komput., vol. 14, no. 2, Art. no. 2, May 2025, doi: 10.32736/sisfokom.v14i2.2362.
[19] M. Masyhuri and D. Darip, “Analisis Dan Desain Aplikasi Perpustakaan Untuk Transformasi Pembelajaran Di SMK Malnu Menes,” BETRIK, vol. 16, no. 01, pp. 11–24, June 2025, doi: 10.36050/ag0s0c52.
[20] M. Darip and H. Hamdan, “Information System Dokumen Perancangan Perangkat Lunak Pelayanan Laboratorium UPTD Dinas Lingkungan Hidup Kota Serang,” J. Ilm. Media Sisfo, vol. 18, no. 2, Art. no. 2, Oct. 2024, doi: 10.33998/mediasisfo.2024.18.2.1701.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Solehudin, Ahmad Munawir, Wahyu Amaldi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
