scholarly journals SISTEM PERINGATAN DINI KEBAKARAN HUTAN MENGGUNAKAN MODUL NODEMCU DAN BOT TELEGRAM DENGAN KONSEP INTERNET OF THINGS (IOT)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohamad Jamil ◽  
Hafid Saefudin ◽  
Sarby Marasabessy
Keyword(s):  
Internet Of Things ◽  
Forest Fire ◽  
Early Warning ◽  
Forest Fires ◽  
Early Warning System ◽  
Warning System ◽  
Fire Department ◽  
The Internet ◽  
Living Things ◽  
The Internet Of Things

Forests have an important role in the life of living things. Nowadays forest fires (Karhutla) become a serious problem that can disrupt the symbiosis and life chain of living things. This problem has become a concern for the community, government and the world. Data obtained until August 2019 recorded 328,724 hectares and burned forest land. To overcome this problem, the government has made various efforts in the form of appeals or legal sanctions on actions that threaten forest sustainability whether carried out individually or in groups. Many cases of forest fires are known when a fire has occurred and little can be detected early. Information on the occurrence of many fires was obtained by residents around the location of the fire. To get the help of the fire department, community participation is needed, to contact the fire department so that they can anticipate the fire disaster early. The aim of this research is to develop a forest fire early warning system using the nodemcu module and the Telegram BOT with the Internet of Things (IOT) concept. Based on the test results of the Forest Fire early warning system using the Nodemcu module and the Telegram BOT with the concept of the Internet of Things (IOT) it is very helpful to provide quick information to find out fires that occur in the forest, by using the Internet of Things method, the officer will be able to know the conditions in real time, because this technology is capable of monitoring hardware using internet communication tools such as Telegram so that distance and location are not affected as long as the sensor used detects changes that occur.Keywords: Internet Of Things, Nodemcu, Telegram, Thingspeak, Forest fires

scholarly journals Early Warning System and Monitoring of River Water Quality Based on Internet of Things

2021 ◽  
Vol 3 (1) ◽  
pp. 42-58
Author(s):  
Vito Hafizh Cahaya Putra ◽  
Mokhamad Hendayu ◽  
Purnomo Yustianto
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
River Water ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
River Water Quality ◽  
The Internet ◽  
Acceptance Test ◽  
Average Value

River conditions in Bandung City are currently in critical condition. This study aims to create an early warning system and monitoring of river water quality based on the Internet of Things in the hope that early warnings sent through the telegram application belonging to the Bandung City DLHK officer and the Twitter social media website, can inform the Bandung City DLHK officer that a river is in a polluted condition and the officer can immediately go to the location of river water to carry out mitigation, and give warnings to the community. The research method used using the waterfall method which consists of: needs analysis, system design, implementation, testing, and maintenance with sequential implementation. Data collection methods were carried out in several ways, namely: interviews, giving questionnaires, and literature studies used in this study sourced from books, journals, seminar presentations, and the internet as references in the research conducted. Based on the research that has been carried out, the following test results are obtained: black box testing is carried out in accordance with those contained in the test plan with the results of each test having valid results. The results obtained from the user acceptance test which are calculated using the Likert scale have an average value of 86.94% which fall into the category of strongly agree, and there are three guidelines which are a follow-up to the output of the early warning system that can be carried out either by the Environmental Service. and Cleanliness (DLHK) of Bandung City and the community.

scholarly journals Smart Monitoring and Controlling of COVID 19 using IOT, Big Data, Machine Learning

2020 ◽  
pp. 42-50
Author(s):  
Sonia Verma ◽  
Manoj Kumar Phadwas
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Early Warning System ◽  
Data Analytics ◽  
Disease Outbreaks ◽  
Warning System ◽  
Interconnected Systems ◽  
The Internet ◽  
Smart Sensor ◽  
The Internet Of Things

Our goal is to develop an environment to monitor and controlling a corona virus of 2019 (COVID-19) with I2OT i. e. Intelligent Internet of Things. Analytics have changed the way disease outbreaks are tracked and managed, hence saving lives. Using technology smart sensor, facial recognition and location, existing surveillance cameras to identify, trace, and monitor people that may have contracted the coronavirus. The Internet of Things, a network of interconnected systems and advances in data analytics, artificial intelligence and ubiquitous connectivity can help by providing an early warning system to curb the spread of infectious diseases.

scholarly journals Sistem Peringatan Dini Banjir Sungai Bengawan Solo Menggunakan Teknologi Internet Of Things (IoT)

2018 ◽  
Vol 2 (2) ◽  
pp. 99
Author(s):  
Nanang Maulana Yoeseph ◽  
Arif Purwo Nugroho ◽  
Andri Adi Nugroho ◽  
Agung Eko Saputro
Keyword(s):  
Internet Of Things ◽  
Water Level ◽  
Natural Disaster ◽  
Early Warning System ◽  
Warning System ◽  
Flood Disaster ◽  
The Internet ◽  
The Internet Of Things ◽  
Level Detector ◽  
Flood Warnings

<em>Flood disaster is a natural disaster that causes many losses, both soul and meteriil. Bengawan Solo River is one of the rivers that every year experiencing flood disaster. To improve preparedness for flooding in solo bengawan river, a flood early warning system was created using the Internet of Things (IoT) technology. This system consists of a water level detector, using ultrasonic sensors and arduino uno, and iot servers. Information about the water level of the bengawan solo river can be accessed by users through the website and android. Users will get river water level infromation in real time. Warning level can be set independet for each user. Flood warnings are sent using sms and notification messages for android versions.</em>

scholarly journals Developing models to establish seasonal forest fire early warning system

2021 ◽  
Vol 909 (1) ◽  
pp. 012005
Author(s):  
D E Nuryanto ◽  
R P Pradana ◽  
I D G A Putra ◽  
E Heriyanto ◽  
U A Linarka ◽  
...  
Keyword(s):  
Forest Fire ◽  
Early Warning ◽  
Forest Fires ◽  
Early Warning System ◽  
Model Development ◽  
Warning System ◽  
Climate Index ◽  
Management Decision ◽  
Forest Fire Management ◽  
Modeling Strategy

Abstract During a typically dry season in Sumatra or Kalimantan, the forest fire starts. In 2015, an El Nino year, forest fires in Sumatra and Kalimantan ranked among the worst episodes on record. Understanding the connection between accumulated monthly rainfall and the risk of hotspot occurrence is key to improving forest fire management decision-making. This study addresses model development to predict the number of 6-month fire hotspots, by combining the prediction of rainfall with hotspot patterns. Hotspot data were obtained from the Fire Information for Resources Management System (FIRMS) for the period of 2001–2018. For rainfall prediction, we used the output model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The threshold of more than 10 hotspot events has been used to establish hotspot climatology. To get a threshold for rainfall that can cause forest fires, we used the Pulang Pisau rain station. We applied two rainfall thresholds to determine three categorical forecasts (low, moderate, high) as environment quality indicator. The two thresholds are 100 mm/month for the lower threshold and 130 mm/month for the upper threshold. The verification of the observational data showed an accuracy of > 0.83, which is relatively consistent and persistent with forest fire events. The weakness of this system is that it cannot determine the exact location of the forest fire because the spatial resolution used is 0.25 degrees. The predictions of the monthly climate index values were reasonably good suggesting the potential to be used as an operational tool to predict the number of fire hotspots expected. The seasonal forest fire early warning system is expected to be an effort to anticipate forest fires for the next six months. The modeling strategy presented in this study could be replicated for any fire index in any region, based on predictive rainfall information and patterns of the hotspot.

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