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Prototipe Alat Monitoring Suhu dan Sistem Kontrol pada Trainer Refrigerasi Berbasis Raspberry Pi 3 B+ Arman, Muhammad; Ayu, Wirenda Sekar; Sugiyarto, Sugiyarto; Pratikto, Pratikto; Hakim, Kamal Amrizal; Sandra, Dinara Safina; Syarief, Eliana
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.1

Abstract

A refrigeration trainer is a working model of an existing system intended to support student learning in the characterization of refrigeration systems, especially their temperature profile. There are many ways to monitor temperature in refrigeration trainers, such as by implementing a temperature monitoring and control system based on Raspberry Pi 3 B +. This project uses the DS18B20 sensor and Raspberry Pi 3 B+ microprocessor. This system has various features such as temperature monitoring, on/off control for fans and compressors, online and offline data storage, and remote control. After 4 hours of testing, the prototype looked stable without any sudden increase or decrease in temperature values for all measurement point parameters. In addition, the error percentage value which is less than 10% shows that the DS18B20 temperature sensor used provides the best accuracy value and is considered adequate for this prototype.
Rancang Bangun Particle Counter untuk Monitoring Konsentrasi PM1, PM2.5 dan PM10 di Udara Berbasis IoT Falahuddin, Muhamad Anda; Puloh, Asep; Sumeru, Sumeru; Arman, Muhammad; Ayu, Wirenda Sekar; Susilawati, Susilawati
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 2 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.2.3

Abstract

Poor air quality is a serious health and environmental issue. Microscopic particles such as PM1, PM2.5, and PM10 cause respiratory disorders and other health problems. Therefore, accurate and continuous air quality monitoring is crucial to mitigate the impacts of air pollution. This research aims to design an Internet of Things (IoT)--based particle counter capable of real-time air quality monitoring and reporting via an online platform. The system utilizes a PMS5003 sensor to measure PM1, PM2.5, and PM10 concentrations precisely. Data from the sensor is processed by an ESP8266 microcontroller connected to the internet, enabling direct data transmission to an online platform for further analysis and visualization. Testing is done by creating a 1x1x1 meter testing chamber to simulate various environmental conditions and validate the device's performance. Results show that the particle counter provides accurate data, with an error rate of less than 10% compared to standard devices. The device demonstrates reliable operation across different environmental conditions, showcasing its robustness in practical applications. This IoT-based particle counter offers an innovative solution for effective and efficient air quality monitoring. It is expected to significantly contribute to human health protection efforts and minimize the adverse environmental impacts of air pollution.
UNJUK KERJA PERFORMA SISTEM MONITORING KONSENTRASI PM1, PM2.5, PM10, CO DAN CO2 DI DALAM RUANGAN BERBASIS INTERNET OF THINGS (IOT) Muhamad Anda Falahuddin; Wirenda Sekar Ayu; Muhammad Arman; Susilawati
Scientific Journal of Mechanical Engineering Kinematika Vol 10 No 2 (2025): SJME Kinematika Desember 2025
Publisher : Mechanical Engineering Department, Faculty of Engineering, Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/sjmekinematika.v10i2.443

Abstract

This research aims to develop an indoor air quality monitoring device that includes particulate matter (PM1, PM2.5, PM10), carbon monoxide (CO), and carbon dioxide (CO2) based on the Internet of Things (IoT). The device is designed to detect particle and gas concentrations accurately and in real-time, thereby helping users improve indoor air quality. The research method involves developing the device design using particulate matter sensors PMS5003, gas sensors MQ-7 and MH-Z19, temperature and humidity sensors DHT11, and ESP8266 microcontroller to process data. The data from sensor measurements are displayed visually using graphs on the ThingSpeak dashboard. The results show that the developed monitoring device can detect particle and gas concentrations with measurement deviation percentages of 16.34% (PM2.5), 7.71% (PM10), 24.90% (CO2), 3.40% (temperature), and 5.67% (humidity). Meanwhile, for CO gas measurement, further calibration of the used sensor is required