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All Journal ILKOM Jurnal Ilmiah Bulletin of Social Informatics Theory and Application
Irvan, Mhd
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Computer Science & IT Social Sciences

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Comparative Analysis of Application Layer Protocols in EV Charging Stations: Evaluating HTTP, MQTT, and Websocket Performance Metrics Argeshwara, Dityo Kreshna; Hadi, Mokh. Sholihul; Sendari, Siti; Irvan, Mhd
Bulletin of Social Informatics Theory and Application Vol. 8 No. 1 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i1.664

Abstract

In the burgeoning domain of electric vehicle (EV) technology, the advancement of supportive ecosystems plays a pivotal role. There is a marked global uptrend in the adoption of EVs, necessitating a robust network of EV charging stations. Integral to these stations is the infrastructure and the accompanying systems that govern their operation. With increased utilization, the exigency for expeditious service at these charging points escalates. This study undertakes a comparative analysis of three distinct data communication protocols at the application layer, specifically within the context of EV charging stations. The protocols scrutinized include Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), and Websocket. The benchmark for data transmission in this investigation is the delivery of energy information, adhering to the Open Charge Point Protocol (OCPP), a legally standardized open protocol. The data format employed is JavaScript Object Notation (JSON). Data transmission utilizing the three aforementioned protocols was intercepted and analyzed using Wireshark, a network protocol analyzer. Parameters such as latency (delay), jitter (variability of latency), and throughput (successful data delivery over a communication channel) were meticulously examined and subsequently represented graphically to enhance the interpretability of the network protocol performance. The findings reveal distinct transmission characteristics for each protocol, despite identical data payloads. HTTP exhibited the superior throughput, peaking at 31,621 bits per second (bps) during real-time data transmission. Conversely, MQTT demonstrated the most favorable latency and jitter metrics, both for real-time and periodic data dispatches. Websocket, however, registered the lowest throughput in real-time transmission, at 4,941 bps. These divergences underscore the importance of protocol selection based on specific performance criteria within EV charging station ecosystems.
Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm Sugiono, Bhima Satria Rizki; Hadi, Mokh. Sholihul; Zaeni, Ilham Ari Elbaith; Sujito, Sujito; Irvan, Mhd
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1945.413-426

Abstract

The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions
Co-Authors Argeshwara, Dityo Kreshna Mokh Sholihul Hadi Siti Sendari Sugiono, Bhima Satria Rizki Sujito Sujito Zaeni, Ilham Ari Elbaith