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Measuring goat weight on a farm is carried out by two methods. The first method conventionally measures the chest girth (CG) and body length (BL), while the second is performed using a scale. This study aimed to facilitate the CG and BL measurementsto estimate goat weight more efficiently through image processing. The endpoints of CG and BL were determined automatically using binary large object (BLOB) segmentation. The images of 120 goats were taken at three different shooting distances of 50 cm, 70 cm, and 90 cm. The statistical testing of the three scenarios in this study explained that the ideal distance to take pictures is the basis for determining whether or not the size of the circumference and BL is appropriate as a feature for estimating the weight of goats based on image processing. Statistical analysis showed that the features of CG and BL at a distance of 50 cm and 70 cm were not significantly different from the measurements of CG and BL of goats carried out directly (actual data). In comparison, the results of testing the feature prediction data at a distance of 90 cm are significantly different from the actual data. [ABSTRACT FROM AUTHOR]

In this research, a DC motor tested using the L298 motor driver which is controlled by Arduino as the motor speed regulator and using the PWM method as a speed control signal generator for the DC motor. This research has several stages, namely literature study, arduino-based dc motor design, Arduino-based dc motor production, Arduino-based dc motor testing, then analysis of research results and conclusions are drawn. Testing on this Arduino-bas, ed DC motor was done with several experiments, by adjust the motor speed by setting the pwm value in the program listing section then measuring the pwm output on pin 9 of the Arduino Uno board. Then measurements were made on the right and left side of the motor. This experiment was carried out by setting the pwm value from 15 to 255 to determine the difference in the increase in the PWM output voltage and determine the voltage on the motor and determine the condition of the DC motor. The test results show that the initial motor moves when the set value of PWM = 45, PWM voltage = 0.83 V, right motor voltage = 1.53 V, and left motor = 1.75 V.

The high-voltage equipment often experiences disturbances caused by the age of the equipment, installation errors, partial discharge disturbances. Of these kinds of disturbances, partial discharge is one of the most common disturbances in high-voltage equipment which has a percentage level of 80% due to partial disturbance. In this study, a research related to electrical equipment that can detect partial discharge disturbances in these high-voltage equipment was done. The equipment is a spiral antenna, where the spiral antenna has several advantages in detecting partial discharge. In making the first partial discharge, a literature study will be carried out first, after that designing a spiral antenna using CST 2018 software to get the Return loss and VSWR parameters as desired, namely Return Loss less than -10 and VSWR less than 2. From the results of this study Spiral antenna is measured using VNA, the measurement results show the spiral antenna is in accordance with the parameters that have been determined, namely the return loss value = -30.8 and the VSWR value = between 1 to 1.5.

In this work, we propose measuring the propagation speed on the transmission medium in the form of a 2.5mm NYAF cable. The method used is load matching and time difference of arrival (TDA). The load matching method uses the same load as the characteristic impedance of the cable. While the TDA method measures two points on the cable with a capacitive load on the output side. From the results of the study, it was found that on the load impedance method, the average propagation speed on a 2.5mm NYAF cable is 1.9 × 108 m»s or 0.63 c. Meanwhile, in the time difference of arrival (TDA) method, the average propagation speed on a 2.5mm NYAF cable is 0.5 × 108 m»s or 0.17 c.

The results of the distance calculation used to control the movement of the robot, hanse the robot is able to avoid unknown obstacles. This obstacle robot divided into 3 parts, namely Arduino Uno as a controller, L298N driver as a motor/wheel controller and ultrasonic sensor HC-SR04 as a sending and receiving device for ultrasonic signals. The ultrasonic sensor design on the obstacle robot placed at the front of the robot with the obstacle position in front. From the data analysis, the obstacle robot can determine the accuracy level of the detected distance and can stop according to the detected obstacle distance. The test results show that the obstacle robot is less accurate in detecting the obstacles in front of it, as evidenced by the test results that there is an average error of 0.118. The obstacle robot made using an ultrasonic sensor, so it is less accurate in reading the presence of a obstacle in front of it. In the test results, the biggest error is when the robot is at a distance of 30 cm, where the robot stops at a distance of 24 cm so that there is an error of 6 cm that missed.

Indonesia’s climate has suitable for tobacco growth. tobacco plants can live in drylands. Tobacco is planted optimum with a size of 100cm x 70cm. The right size is used to count the amount of tobacco that will be planted according to the area of the land. Technology developed to help the farmer to monitor their crops and yields, by using image processing. Farmers can count easily without having to go to the field to calculate manually. In our research, image processing is used combined with knowledge base data. Images taken using drones, then be processed using image processing using the contour detection method to count the number of a tobacco plant. The results of 80 times system testing, which is using 4 parameters, 20 samples for each parameter. The success rate of the first test was 86.597% with a 13.402% error rate. The second test’s success rate is 87.63% with a 12.306% error rate. of the third test obtained an 87.033% success rate and an error rate of 12.966%. The fourth test results from an 85.431% success rate and 14.56% error rate.

In Indonesia, the use of 3D printers is gaining popularity in industrial processes, because the use of 3D printers in the manufacture of objects takes a relatively shorter time. However, in Indonesia, there is still very little information about the results of the 3D printer process, such as how much strength the object produced from the process, the accuracy and others. In this research, 10 experiments parameters such as current, voltage, power and energy was testing to get electrical power consumption. If time of usage parameter is added, then the results can be used to calculate how much bill you should pay to operating this equipment. The result shown that monitoring the consumption of electrical power used by 3D printer devices can be done using the PZEM-004T sensor with an accuracy level that is read by the sensor of 99.472% at reading the voltage value where the sample value obtained is 207 V AC and the voltage value that is read on the digital multi tester device is 208.1 V AC so that the difference obtained is only 1.1 V AC.