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Developing an automatic detection method based on computer vision applied to the moon crescent is an innovative concept that can be further developed. This program will be highly useful for observers during the Moon crescent observation because it can help them recognize objects quickly. This paper proposes an automatic crescent moon detection method based on visual mechanisms and training using the Cascade Classifier algorithm. The stages of this method consist of building Haar structural features, extracting feature samples using Haar structural features, and training 981 images consisting of 654 positive images and 327 negative images using the Cascade Classifier. The results show that the crescent moon detection performance is quite good at detecting the crescent Moon. The developed program can recognize crescent moon objects, although it is limited to relatively large lunar illumination in the range of greater than 10% to less than 50%. Furthermore, our program can be applied in real-time situations.

Indonesia is one of the countries located at the ring of fire which should be monitored to predict the eruption earlier and set the risk zones around with no human involvement especially while eruption taking place. Therefore, in this research, it is used a 4 wheeled mobile robot called PRAWIRA for this purpose. The robot should have the ability to avoid the obstacles in front of it in this area. It has been designed a real-time object detection system for volcano monitoring application using deep learning from the YOLOv5s model for 4 objects (trees, persons, stones, and stairs). It was used 484 images for the dataset after the pre-train process was conducted with several steps: object identification; dataset downloading (Google Chrome Extension and Open Images v6); image labeling (LabeImg); augmentation process (flip, blur, and rotation); and data training for varies epochs and batches by Jupyter Notebook GPU. The preliminary result for this research was presented in the mean average precision (mAP) of YOLOv5s (the smallest version). The first variation (batch = 16, epochs = 100) resulted in mAP_0.5 = 17.9% and mAP_0.5:0.95 = 7.27% with 0.262 hours of training time. The second (batch = 16, epochs = 500) resulted in mAP_0.5 = 25.7% and mAP_0.5:0.95 = 12.3% with 1.296 hours of training time, while the third (batch = 80, epochs = 100) resulted in mAP_0.5 = 17.7% and mAP_0.5:0.95 = 5.63% with 0.232 hours of training time. Furthermore, the last variation (batch = 80, epochs = 500) resulted in mAP_0.5 = 19.5% and mAP_0.5:0.95 = 8.92% with 1.085 hours of training time. Therefore, the second variation is the best result for the model with 14.8 MB of size. Moreover, interfaces for the best model were displayed to show the result of the training.

Improved ground and aerial system technologies enable mapping and monitoring forests and land to mitigate forest fires. UAV plays a role in monitoring by collecting forest area images from the air, which could be processed into 2D and 3D images. They can be analyzed to identify land cover types and objects in forest areas. This image data collection uses the DJI Phantom 4 Pro UAV controlled automatically with a flight plan made with Pix4D Capture, which is then processed using Agisoft. The result of the mapping has an average GSD of 2,03 cm/px. The mapping result shows that the 3D image produced can show objects in various land cover types. Weather related parameters were measured using ground sensors both in forest and plain area. We had successfully gathered forest and plain area images in addition to weather related parameters in Tangkuban Perahu Mountain area.

Discovery of electromagnets has had a great influence on the development of science and technology, which due to the nature of the magnetism that can be arranged so that it can provide practical benefits including electric motors, relays, power generators, and automatic door switches. In this research, an electromagnet the form of copper plate with hole in the middle for iron core. Each plate has an insulator that separates between each plate which is arranged in threads to form a helical coil of copper plate. Value obatained from design electromagnets using copper is 0 to 8 mT, with measurement using Gaussmeter as a reference in the development of sensors using giant magnetoresistance. The implications and application from this research is portable and concise high field in the form of copper plate models. In the future it potential apllication on biomedical and engineering.

We have developed a new volcano early warning system based on sensor node, internet of things (IoT) and UAV to overcome some problems in Indonesian volcano monitoring system. Data (monitored by sensor node) transmission and communication is managed by the IoT while UAV is used for remote sensing data to complete the system to predict the geological status of the volcano. An intelligent-battery UAV is needed for this purpose which manage the power utilization for field application. First stage of the remote sensing process is volcano region mapping for constructing an orthophoto before it is combined with field sensor data. Therefore, we have conducted a laboratory experiment to map some region in ITB, Bandung in different weather as real volcano condition. We have constructed day, night, sunny and cloudy maps of ITB after some data collected through flight and control plan as real volcano environment. Some grid flight plans were chosen for expected result as well as battery saving for each 5 meters data collection. A 3D software has been used for modelling of the orthophoto construction and resulted in 0 - 8 meters error of 20000 - 24000 m2 monitored area. Therefore, this method could be used in real volcano application.

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.

In this research we developed Sm 3+ ion-doped fluoroborotellurite with formula 30B 2 O 3 -(30-x)TeO 2 -10ZnF 2 -30BaO-xSm 2 O 3 where x = 0.0; 0.05; 0.1; 0.5; 1.0; and 1.5 mol%. They developed using the melt-quenching technique. The raw material is melted at 1150°C for 1 hour 30 minutes and annealed at 500°C for 1 hour 30 minutes. The optimum concentration was found at 1.0 mol% of Sm 3+ ion in the glass system. Their physical, optical, photoluminescence properties and lifetime were observed. The absorption spectra show strong absorption peaks at 1232 nm due to 6 H 5/2 → 6 F 7/2 transition. The photoluminescence properties show a strong emission at 600 nm under λ ex =403 nm because of 4 G 5/2 → 6 H 7/2 transition. The CIE 1931 coordinate confirms the orange emission color with 81% efficiency (η). From all results, we can summarize that our glass sample can be developed as a solid-state lighting source in the orange region.

Peatland fires in Indonesia have become a serious problem because they cause huge losses. Geographical factors of peatlands in the Kalimantan contribute to the difficullty of conventional fire fighting. The current solution is water bombing using a helicopter. Water bombing is expensive and has a high risk for the safety of fire fighters/helicopter crew so must be done with the correct calculation to fire position so that firefighting can work effectively and efficiently. Therefore, mathematical calculation of the fire position is very important. The position of the fire can be determined by knowing the rate of spread of the fire (Rate of Spread / ROS). In this study aims to calculate the rate of fire in peatlands along the Trans Kalimantan road between Palangka Raya and Pulang Pisau, Central Kalimantan using the simard’s model. Land surface temperature (LST) data were obtained from the TERRA MODIS satellite which was downloaded from the website https://modis.gsfc.nasa.gov/data/dataprod/index.php. The results of the calculations for the five triangles obtained that the ROS are 0.03m/s, 0.04m/s, 0.04m/s, 0.04m/s and 0.03m/s for R1, R2, R3, R4 and R5 respectively. The average of ROS is 0.037m/s with a direction of 32.57 degrees.

The experiment tool of converting angular velocity into linear velocity has been built to support the physics education in the Physics Department of Tadulako University. In this project, the experimental tool has been designed, manufactured, and tested to convert the angular velocity value to linear velocity based on Arduino ATMega 2560. The stages of this research begin with the design and manufacture: mechanical system, optical sensor circuit, stepper motor circuit, the circuit of LCD and keypad controlled by Arduino ATMega 2560. The next step is the building controlling program as the brain of this instrument. This experimental tool has been tested and working properly. It operates for a rotation speed range of 1 rpm - 112 rpm. The value of converted linear velocity both theoretically and measured value is relatively the same.

Indonesia’s food security compels the government to develop food and land intensification as a long-term national program. It cannot be denied that food productivity is closely related to fertilizer demand considering that Indonesia is an agricultural country. However, to date, 86.5% of Indonesian farmers rely on synthetic fertilizers which are adverse to the environment. On the other hand, Indonesia is the 2 nd largest food waste producer and most of them are still unprocessed. Therefore, a practical solution to overcome this problem is to offer the application of MASARO technology. This technology can convert 1 kg of degradable waste into 10 L of organic liquid fertilizer so that it can increase agricultural productivity while replacing the role of synthetic fertilizers. In the organic liquid fertilizer production process, pH is an important parameter of the entire fermentation process where the value should be in the range of 3.9-4.2. However, the manual process still requires a long production duration, 28 days, because of pH value overshoot. By implementing a pH control system, the duration of organic liquid fertilizer production can be cut by 57% (from 672.8 hours to 288.6 hours) so the effect is proven can provide optimal and effective fermentation.