Monitoring volcanic activity this is indispensable for both research and disaster mitigation strategies. Therefore, the presence of volcano monitoring allows the monitoring, data recording and early warning system of volcanic activity from a distance that is real time. Volcanic monitoring system aims to detect any changes in volcanic status. Temperature and humidity become parameters of increased magma activity. Concentration of gasses includes SO2 and CO2, indicates of the depth of magma in volcanic conduit. Magma movement also triggers the earthquake. The instrument that used in this research are temperature and humidity sensor DHT11, SO2 sensor TGS2602, CO2 sensor MG811, and vibration sensor ADXL345. Temperature and humidity sensors used DHT11 sensors that serve to detect temperature and humidity in the air. DHT11 sensors can detect temperature between 0-50°C and humidity between 20-90% RH. SO2 emissions were detected using TGS 2602 sensors that detects SO2 gas between 0-100 ppm. While CO2 gas emissions used MG811 sensors that detect the presence of CO2 gas between 350-10000 ppm. Furthermore, the vibrations were detected using a digital accelerometer ADXL345 that detects vibrations between 0.1-3200 Hz. Data from all sensors processed using arduino promini microcontroller and then sent to ESP8266. ESP8266 is a wifi module that serves as a microcontroller enhancement to connect to wifi and create TCP / IP protocol. This module is also used to create an IoT project so that it can be accessed at any time on the web service page. This system will be assembled solar cells as a source of power supply.

Indonesia is one of the countries that lies in the pacific ring of fire, the highlighted area that known to be active by seismic and volcano activities. Indonesia has a total of 129 active volcanoes that make the land fertile, but also vulnerable to disaster. When a volcanic eruption occurs, the current fixed monitoring system is not fully reliable. On the other hand, monitoring of further volcano activities is critically needed in this situation. Therefore, a volcano monitoring system that can move freely and controlled safely is needed. To solve this problem, a mobile robot that capable of moving in volcano area has been developed. The robot locomotion system is designed with 2 DC motor using 4-wheel drive configuration. Each motor implements a PID Controller to adjust the speed that has been set. In addition, the robot is also equipped with a camera (Logitech C920), vibration sensor (ADXL 345), temperature sensor (DHT 11), carbon dioxide gas sensor (MG-811), and sulphur dioxide gas sensor (TGS 2602) to retrieve volcanic condition data, as its function for volcano monitoring. The microcontroller used to adjust motor control and read sensors data is Nucleo STM32-F466RE, while the mini-PC that being used for integrated data communication and processing is Raspberry PI 3B+. PID Controller has been successfully applied with average deviation of 2.5% for the left motor, and 2.75% for the right motor.

Kelud is one of Indonesian volcano lies between Kediri and Blitar districts of East Java province. This volcano has erupted since 1000 where casualties of 200000 people emerged until the last eruption in 2014. Therefore, it is needed a volcano early warning system to detect the eruption earlier for minimizing the casualties. We have developed an early warning system based on sensor nodes consist of vibration, temperature and gasses (sulfur and carbon dioxide) sensors to monitor the physical parameter of the volcano, drone surveillance, mapping and temperature measurement, and mobile robot consists of the same sensor as in the node for both normal and emergency situations. The system has been tested in Kelud volcano in August 2019. In a normal condition, the system has detected 1 Hz of seismicity, under 1 ppm of sulfur and carbon dioxide, 23-55.3oC of the lake temperature, 32oC of the ground temperature and 23-25oC of the air temperature. The system could be used for 37 hours of full operation for 1 charging cycles of solar cell’s charging process where suitable for dangerous environment application.

Natural disaster often occurs nowadays all around the world result in environmental damage and broken infrastructure which prevent logistics transportation for the evacuees. Therefore, it has been developed a payload transport UAV based on Internet of Things (IoT) for transportation of goods (foods, clothes and medicines) to the disaster location. The transport system consists of a relatively small (52.5 cm, 52.5 cm and 26.5 cm of length, width and height respectively) container (as the main part of the system) lifted by an UAV using a rope, servo motor and a pulley which are controlled by IoT Platform for user friendly access through a smartphone, tablet, laptop or computer. The system is equipped with automatic door and lock system, ultrasonic sensor system for distance measurement and pulley system with 17 N cm torque servo motor 360° for loading and unloading mechanism. Lolin Node (Microcontroller Unit) MCU v3 using Message Queuing Telemetry Transport (MQTT) protocol of IoT for WiFi and GSM networks has been used for the system for control and communication application. Hereinafter, the pulley system has been successfully tested to lift until 9.27 kg of load, 50 cm of distance motion with 6.3 cm/s upward velocity and 11.4 cm/s downward velocity.

Indonesia has 15 big potential forest fires locations which destroyed twice the area annually. Moreover, there are almost 20 fire points for each location which increase gradually according to the satellite images of LAPAN (Lembaga Penerbangan dan Antariksa Nasional - National Institute of Aeronautics and Space of Indonesia). Therefore, some monitoring and early warning system for this disaster are needed to minimize the loss and damage of forest ecosystem, smoke exposure to human respiratory system, aviation business, and some forest related industries. Unmanned Aerial Vehicle (UAV) technology could be proposed as the alternative to solve this problem. The forest is monitored by an UAV, in other hand it could be used for image data acquisition to construct 2D and 3D maps for further analysis of the early warning system. The images of sampling location had been taken by an UAV - DJI Phantom 4 Pro which automatically controlled by a flight plan using Pix4D Capture and processed by Pix4D Mapper. The sampling location took place in different areas: near a building, few km squares of campus area consists of building-vegetation combination, and three forest locations in two active volcanoes. The maps with Ground Sample Distance (GSD) under 3 cm/pixel result in under 1% error for X, Y and Z of 2D and 3D constructed maps. The maps also show an important finding where the movement object could be detected, which potential to be applied for fire evolution detection of a forest fire early warning system.