Sistem Deteksi Dan Peringatan Kebakaran Dini Di Kapal Berbasis Internet Of Things Dengan Metode Fuzzy

Auliarahman, M Fahmi (2023) Sistem Deteksi Dan Peringatan Kebakaran Dini Di Kapal Berbasis Internet Of Things Dengan Metode Fuzzy. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kebakaran kapal merupakan jenis kecelakaan paling sering terjadi, dengan persentase sebesar 32% dalam rentang tahun 2017-2021 berdasarkan data KNKT. Sistem deteksi saat ini terbatas pada sensor tunggal, yang seringkali menghasilkan false alarm. Selain itu sistem peringatan kebakaran saat ini sangat bergantung pada panel kontrol yang mana informasi lokasi kebakaran hanya didapat melalui lokasi fisik panel kontrol. Pada penelitian ini akan dibuat sistem deteksi dan peringatan dini kebakaran di kapal berbasis Internet of Things (IoT). Dengan memanfaatkan Internet of Things, kondisi dapat dipantau dari jarak jauh seperti melalui Smartphone maupun PC. Untuk menyempurnakan sistem sensor tunggal, maka sistem ini menggunakan tiga sensor (IRFlame, MQ2, dan DHT22) yang terhubung dengan Arduino Uno R3 untuk membaca dan mengolah data sensor. Fuzzy logic digunakan untuk menentukan kondisi kebakaran. Hasil penelitian menunjukkan bahwa sistem dapat mengirimkan peringatan melalui buzzer dan VoIP call IFTTT, dengan tingkat akurasi sensor yang memadai. Berdasarkan pengujian yang dilakukan, sistem ini dapat mendeteksi api dengan sensor IR flame, mendeteksi asap dengan sensor MQ2 dan dapat mendeteksi suhu dengan sensor DHT 22 dengan akurasi sensor DHT22 sebesar 99.26%. Sistem juga menyediakan monitoring pembacaan sensor dan kondisi fuzzy melalui dashboard adafruit.io. Pengujian fuzzy logic juga menunjukkan akurasi sebesar 100%. Temuan ini mengindikasikan potensi sistem dalam mendeteksi kebakaran dengan akurat dan memberikan peringatan dini di kapal. Dengan adanya sistem ini, diharapkan dapat meningkatkan keamanan kapal dan mengurangi risiko kebakaran yang berdampak pada keselamatan kru dan penumpang.
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Ship fires are the most frequent type of accident, accounting for 32% of incidents between 2017 and 2021 based on KNKT data. The current detection system is limited to a single sensor, often resulting in false alarms. Additionally, the current fire alarm system heavily relies on control panels, where fire location information is only obtained through the physical location of the control panel. This research aims to develop an early fire detection and warning system for ships based on the Internet of Things (IoT). By leveraging IoT, the system allows for remote monitoring of conditions through smartphones and PCs. To enhance the single-sensor system, three sensors (IRFlame, MQ2, and DHT22) connected to Arduino Uno R3 are utilized to read and process sensor data. Fuzzy logic is employed to determine the fire conditions. Research results demonstrate that the system can deliver warnings through a buzzer and VoIP call IFTTT, with sufficient sensor accuracy. Based on testing, this system can detect fire with an IR flame sensor, detect smoke with an MQ2 sensor and can detect temperature with a DHT 22 sensor with a DHT22 sensor accuracy of 99.26%.The system also provides sensor reading and fuzzy condition monitoring through the adafruit.io dashboard. Fuzzy logic testing also exhibits a high accuracy of 100%. These findings indicate the potential of the system to accurately detect fires and provide early warnings on ships. With the implementation of this system, it is expected to enhance ship safety and reduce the risk of fires, consequently improving the safety of crew members and passengers.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Kebakaran kapal, Internet of Things, Fuzzy, Sistem Deteksi, Sistem Peringatan, Ship fire, Internet of Things, Fuzzy, Detection System, Fire Alarm System
Subjects:	Q Science > QA Mathematics > QA9.64 Fuzzy logic T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors V Naval Science > VK > VK1258 Ships--Fires and fire prevention
Divisions:	Faculty of Marine Technology (MARTECH) > Marine Engineering > 36202-(S1) Undergraduate Thesis
Depositing User:	M Fahmi Auliarahman
Date Deposited:	24 Aug 2023 07:50
Last Modified:	24 Aug 2023 07:50
URI:	http://repository.its.ac.id/id/eprint/101527

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