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All Journal Building of Informatics, Technology and Science BEES: Bulletin of Electrical and Electronics Engineering
Ali Nurdin
Politeknik Negeri Sriwijaya, Palembang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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System Early Warning Sebagai Peringatan Dini Untuk Smart Home Ali Nurdin; Lindawati Lindawati; Aden Jaya Kusuma
BEES: Bulletin of Electrical and Electronics Engineering Vol 1 No 1 (2020): BESS July 2020
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (713.702 KB)

Abstract

Floods, fires and gas leaks that occur often cause problems that can result in losses that are not small in value. The absence of an early warning system when floods, fires and gas leaks make people less alert. In this study designed a flood detection system, fire detection and leak detection in gases that work automatically based on a microcontroller. The water level monitoring system is carried out by using an HC-SR04 type ultrasonic sensor that will read the water level, the fire monitoring system is carried out by using a DHT11 type temperature sensor that will detect the occurrence of fire while the gas leak monitoring system is carried out by using a gas sensor type MQ-9 will find out the gas leak. If all three sensors detect parameters such as rising water levels, rising temperatures and high gas concentrations, the system will sound the buzzer as an early warning sign. This system connected to the Internet of Things (IoT) which will display data in real time displayed using the web-based IoT Platform, Mapid. With this system, it is expected that the public can be more alert to floods, fires and gas leaks.
Implementasi Support Vector Machine Pada Alat Monitoring Kecelakaan Dengan Intelligent Transport System Syifa Amira Zahrah; Ade Silvia Handayani; Ali Nurdin
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1974

Abstract

The implementation of intelligent transportation systems will produce a large amount of data. The resulting data is critical in the design and implementation of ITS in the transportation system. This study discusses the performance of the Support Vector Machine algorithm on an accident monitoring tool by utilizing the Intelligent Transportation System that works in real-time using an Android-based application. This experiment simulates accident monitoring with a multisensor accident monitoring device. Multisensor technology consists of MPU 6050 sensor, sound sensor, vibration sensor, and camera. In an experiment, the measured variables are location, slope, accuracy, and time of the traffic accident monitoring system. The results of monitoring traffic accidents in testing using the Support Vector Machine algorithm can work well by classifying data based on the type of accident.
Co-Authors Ade Silvia Handayani Aden Jaya Kusuma Lindawati Lindawati Syifa Amira Zahrah