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Fitriana, Dea
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Internet Of Things (IoT) For Electric Car Battery Capacity Controller Lianda, Jefri; Afridon, Muhammad; Zamhuri, Zamhuri; Fitriana, Dea; Eviani, Gusti
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

This paper discusses a 58 volt battery voltage control system for electric vehicles using the Blynk application.The research successfully monitors the amplitude of the battery voltage. Additionally, it includes the capability to controlthe battery via an SSR relay, which is connected to the NodeMCU ESP8266. The Blynk application displays both theamplitude and the percentage of the battery voltage. The NodeMCU ESP8266 has demonstrated reliable performance,maintaining a stable connection as long as it is connected to the internet. The average voltage discrepancy shown on theBlynk application is 0.74%. The SSR relay disconnects the power supply to the electric vehicle when the battery voltageapproaches 46.3 volts or when the battery capacity percentage drops to 0.16%. This system ensures that the battery ismanaged effectively, preventing over-discharge and potential damage by disconnecting the load at critical voltage levels
An Iot-Based Forest Fire Prediction System Using Fuzzy Logic Method Lianda, Jefri; Amri, Hikmatul; Adam, Adam; Custer, Johny; Fitriana, Dea
Jurnal Riset Teknologi Pencegahan Pencemaran Industri Vol. 16 No. 2 (2025): November
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21771/jrtppi.2025.v16.no2.p158-167

Abstract

Forest and land fires represent a recurring environmental challenge in Indonesia, especially during the prolonged dry season. These incidents result in significant consequences, including the destruction of ecosystems, threats to human health, and considerable economic disruption. To address this problem, the present research focuses on the development of an Internet of Things (IoT)-based system designed to predict and monitor the risk of forest fires by implementing the Fuzzy Logic method. The prototype integrates several sensors, namely a DHT22 sensor for measuring temperature and humidity, an MQ-2 sensor for detecting gas and smoke concentrations, and a flame sensor for identifying the presence of fire. All sensors are connected to a NodeMCU ESP8266 microcontroller that serves as the core of data processing and wireless communication. The collected sensor data is evaluated using a Fuzzy Logic algorithm, which classifies the fire risk into three distinct levels: “Safe,” “Caution,” and “Hazardous.” Experimental testing demonstrates that the system responds effectively to fluctuations in temperature, humidity, smoke levels, and visible flame in real time, with alerts displayed through a web-based dashboard. The DHT22 sensor exhibits an average error rate between 4.8% and 5% for temperature readings and between 4.1% and 4.5% for humidity measurements. In addition, the flame sensor successfully detects fire sources at distances reaching 300 cm. The outcomes confirm that the system achieves a high degree of reliability and accuracy, thereby providing valuable support for early warning, strengthening preventive strategies, and assisting authorities in mitigating the severe impacts of forest and land fires.
Co-Authors Adam Adam Afridon, M Custer, Johny Eviani, Gusti Hikmatul Amri Lianda, Jefri Zamhuri, Zamhuri