Rancang Bangun Sistem Pendeteksi Api Berbasis IOT (Internet of Things)
DOI:
https://doi.org/10.21009/JEVET.0072.02Keywords:
ESP32, flame sensor, IoT, MQ-6, PZEM-004TAbstract
Abstrak
Penelitian ini bertujuan untuk menghasilkan Alat Sistem Pendeteksi Titik Panas Berbasis IOT (Internet Of Things). Pemantauan dilakukan pada web thinger.Io dan aplikasi telegram. pendeteksi titik api menggunakan sensor Flame, pendeteksi titik gas menggunakan sensor MQ-6, untuk monitoring tegangan, arus, dan daya menggunakan sensor PZEM004T, pendeteksi titik api,gas dan monitoring menggunakan mikrokontroler ESP32.Penelitian ini menggunakan metode rekayasa Teknik (Forward Engeneering). Dimulai dari perancangan, pembuatan, dan pengujian. Adapun tahapan proses pelaksanaan yaitu: tahap perencanaan meliputi studi literatur mengenai penggunaan alat dan bahan material untuk alat display yang akan dibuat; manfaat dan kelebihan alat display yang akan dibuat, tahap perancangan meliputi penuangan ide gagasan dan spesifikasi rancang bangun sistem pendeteksi ttitik panas. Hasil dari penelitian ini menunjukan Alat Sistem Pendeteksi Titik Panas Berbasis IOT (Internet Of Things) bahwa PZEM-004T dapat mengukur tegangan, arus dan daya, sensor Flame dapat mendeteksi api (yang dihasilkan dari korek api), dan sensor MQ-6 dapat mendeteksi gas (yang dihasilkan dari korek gas). Ketika bahaya kebakaran terdeteksi maka buzzer akan aktif sebagai alarm dan hasil pembacaan modul sensor akan dikirimkan notifikasi via Whatsapp dan untuk mendeteksi arus, tegangan dan daya akan dapat di monitoring pada website. Halaman Website dapat dipantau dari jarak jauh, jika terkoneksi dengan internet. Hasil pengujian rata-rata nilai error pada sensor PZEM004T menunjukkan bahwa pada beban puncak (pukul 19.00), kesalahan tegangan sebesar 0,67%, arus sebesar 5,02%, dan daya sebesar 5,6%. Sementara itu, pada pengujian di pagi hari (pukul 10.00), kesalahan tegangan tercatat sebesar 5,1%, arus sebesar 10,25%, dan daya sebesar 7,45%. Kesimpulan dari penelitian Alat Sistem Pendeteksi Titik Panas Berbasis IOT (Internet Of Things) adalah sistem telah berhasil dirancang sesuai dengan tujuan penelitian yang diharapkan oleh peneliti, yaitu menghasilkan Rancang Bangun Sistem Pendeteksi Titik Panas (Api, Gas, Tegangan, Daya, dan Arus) Berbasis IOT (Internet Of Things).
Abstract
This research aims to produce an IOT-Based Hot Spot Detection System Tool (Internet Of Things). Monitoring is carried out on the web thinger. Io and the telegram application. the fire point detector uses the Flame sensor, the gas point detector uses the MQ-6 sensor, for voltage, current, and power monitoring using the PZEM004T sensor, the fire point detector, the gas and monitoring uses the ESP32 microcontroller. This study uses the Forward Engineering method. Starting from design, manufacturing, and testing. The stages of the implementation process are: the planning stage includes a literature study on the use of tools and materials for the display equipment to be made; The benefits and advantages of the display equipment to be made, the design stage includes pouring out ideas and design specifications for the hot spot detection system. The results of this study show that the PZEM-004T can measure voltage, current and power, the Flame sensor can detect fire (generated from matches), and the MQ-6 sensor can detect gases (generated from matches). When a fire hazard is detected, the buzzer will be activated as an alarm and the results of the sensor module reading will be sent a notification via Whatsapp and to detect current, voltage and power will be monitored on the website. Website pages can be monitored remotely, if connected to the internet. The average test results of the error value on the PZEM004T sensor showed that at peak load (at 19.00), the voltage error was 0.67%, the current was 5.02%, and the power was 5.6%. Meanwhile, in the morning test (10.00 am), the voltage error was recorded at 5.1%, the current was 10.25%, and the power was 7.45%. The conclusion of the research of the IOT-Based Hot Spot Detection System Tool (Internet Of Things) is that the system has been successfully designed in accordance with the research objectives expected by the researcher, which is to produce the Design and Construction of an IOT-Based (Internet-Off-Things) Hot Spot Detection System (Fire, Gas, Voltage, Power, and Current).
References
Ahmad, I., Shahabuddin, S., Sauter, T., Harjula, E., Kumar, T., Meisel, M., Juntti, M., & Ylianttila, M. (2021). The Challenges of Artificial Intelligence in Wireless Networks for the Internet of Things: Exploring Opportunities for Growth. IEEE Industrial Electronics Magazine, 15(1), 16–29. https://doi.org/10.1109/mie.2020.2979272
Awad, M., Abougindia, I. T., Elliethy, A., & Aly, H. A. (2021). Flexible architecture for real-time synchronized processing of multimedia signals. Multimedia Tools and Applications, 80(12), 18531–18551. https://doi.org/10.1007/s11042-021-10575-y
Deng, J., Li, Y., Lü, H.-F., Wang, W.-F., Bai, L., & Shu, C.-M. (2020). Metallurgical analysis of the “cause” arc beads pattern characteristics under different short-circuit currents. Journal of Loss Prevention in the Process Industries, 68, 104339–104339. https://doi.org/10.1016/j.jlp.2020.104339
Harmon, K., Lee, H., Khasraghi, B., Parmar, H., & Walden, E. (2024). Delays in Information Presentation Lead to Brain State Switching, Which Degrades User Performance, and There May Not Be Much We Can Do about It. Management Information Systems Quarterly, 48(1), 273–298. https://doi.org/10.25300/misq/2023/17680
Jiang, J., Wang, C., Roth, T., Nguyen, C., Kamongi, P., Lee, H., & Liu, Y. (2021). Residential House Occupancy Detection: Trust-Based Scheme Using Economic and Privacy-Aware Sensors. IEEE Internet of Things Journal, 9(3), 1938–1950. https://doi.org/10.1109/jiot.2021.3091098
Kaptein, W., El-fallah, A., & Almaktoof, A. M. (2020). Fire detector system with wireless communication for domestic use. Scientific Journal of Applied Sciences of Sabratha University, 3(1), 44–55. https://doi.org/10.47891/sabujas.v3i1.44-55
Kweon, S.-J., Park, J.-H., Park, C.-O., Yoo, H.-J., & Ha, S. (2022). Wireless Kitchen Fire Prevention System Using Electrochemical Carbon Dioxide Gas Sensor for Smart Home. Sensors, 22(11), 3965. https://doi.org/10.3390/s22113965
Mahaveerakannan, R., Anitha, C., Baranitharan, K., Rajan, S., Muthukumar, T., & Rajulu, G. G. (2023). An IoT based forest fire detection system using integration of cat swarm with LSTM model. Computer Communications, 211, 37–45. https://doi.org/10.1016/j.comcom.2023.08.020
Nguyen, M. D., Vu, H. N., Pham, D. C., Choi, B., & Ro, S. (2021). Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks. IEEE Access, 9, 146667–146679. https://doi.org/10.1109/access.2021.3122346
Peace, M., & McCaw, L. (2024). Future fire events are likely to be worse than climate projections indicate – these are some of the reasons why. International Journal of Wildland Fire, 33(7). https://doi.org/10.1071/wf23138
Ramadan, M. N. A., Basmaji, T., Gad, A., Hamdan, H., Akgün, B. T., A.H. Ali, M., Alkhedher, M., & Ghazal, M. (2024). Towards early forest fire detection and prevention using AI-powered drones and the IoT. Internet of Things, 27, 101248–101248. https://doi.org/10.1016/j.iot.2024.101248
Rehman, A., Qureshi, M. A., Ali, T., Irfan, M., Abdullah, S., Yasin, S., Draz, U., Glowacz, A., Nowakowski, G., Alghamdi, A., Alsulami, A. A., & Węgrzyn, M. (2021). Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT). Energies, 14(17), 5500. https://doi.org/10.3390/en14175500
Rehman, A., Saeed, F., Rathore, M. M., Paul, A., & Kang, J. ‐M. (2024). Smart city fire surveillance: A deep state‐space model with intelligent agents. IET Smart Cities, 6(3), 199–210. https://doi.org/10.1049/smc2.12086
Sultan, T., Chowdhury, M. S., Safran, M., Mridha, M. F., & Dey, N. (2024). Deep Learning-Based Multistage Fire Detection System and Emerging Direction. Fire, 7(12), 451. https://doi.org/10.3390/fire7120451
Sun, J., Qi, W., Huang, Y., Xu, C., & Yang, W. (2023). Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going? Fire, 6(6), 228. https://doi.org/10.3390/fire6060228
Tu, D., Wang, E., & Yu, Y. (2023). Design and Implementation of Intelligent Fire Monitoring System Based on Multi-Sensor Data Fusion. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.01715
Waalen, J. (2023). Mobile Health and Preventive Medicine. Medical Clinics of North America, 107(6), 1097–1108. https://doi.org/10.1016/j.mcna.2023.06.003
Yoon, J.-H., Zhao, X., & Yoon, D.-H. (2024). Intelligent Fire Suppression Devices Based on Microcapsules Linked to Sensor Internet of Things. Fire, 7(9), 323–323. https://doi.org/10.3390/fire7090323
