ISSN / EISSN : 2685-2381 / 2715-2626
Published by: Sekolah Tinggi Teknologi Adisutjipto (10.28989)
Total articles ≅ 32
Latest articles in this journal
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i2.977
Asphalt hot mix manufacture consists of mixer and weighing which is a part of the mixing process which is controlled automatically using PLC at the asphalt mixing plant SPECO TSAP-800AS. All processes in the asphalt mixing plant have used computer-based electromechanical automation, especially the application of PLC control on the Mixer and Weighing section, considering that the mixing process must always be stable and run continuously so it must be controlled automatically using a combination of relays and air dampers. The scale sensor used is a load cell which functions to calculate the weight of solid material from hot bin CB1/1 to CB1/5. The mixer used has a capacity of 800 kilogram per batch. The design of controlling the manufacture of asphalt hot mix at the asphalt mixing plant TSAP-800AS was made using Outseal Studio V2.2 software.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i2.890
In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.893
The work to get data directly from the field for optimizing a network is called drive test. The implementation of drive test by directly down to the field has several obstacles, such as the condition of the terrain is insufficient and risky to be passed by car. Barriers such as traffic congestion, risky environmental conditions and narrow road areas between buildings makes the implementation of drive test by using unmanned aerial vehicle (UAV) or known by drone. In this research, drive test is carried out on 4G LTE Network and uses an Android smartphone that has the G-NetTrack application installed. The Data parameters of the drive Test and QoS are searched. there are Reference Signal Receive Power (RSRP), Reference Signal Receiving Quality (RSRQ), Signal to Noise Ratio (SNR), delay and throughput. This research compares two methods, which are drive test with normal condition and drive test by using a UAV. The result of the drive test with normal condition is obtained an average value of RSRP -90.32 dBm, RSRQ -9.58 dB and SNR 3.99 dB. Whereas in the drive test by using UAV is obtained an average value RSRP -90.8 dBm, RSRQ 9.32 dB and SNR 4.77 dB. The results of this research showed that all parameters in comparison of both methods has meet the standard of Key Performance Indicator (KPI) with small value difference because drive test by using UAV is equals with normal drive test that is to know the real condition of obstacle in field.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.794
Electric power and power factor are two parameters that must be considered because they involve the quality of the energy consumed. In order to be able to analyze these, a microcontroller-based power and power factor meter are needed which can be further developed. In this research, a power and power factor meter based on the ATmega328P microcontroller was developed on the Arduino board. Several algorithms are used to calculate the frequency of the grids, as well as the true-RMS of voltage and current. The simulation results show that this system can measure the power and power factor for input voltages of 100 to 300 volts with a frequency of 45 to 156 Hz for loads up to 5 amperes. The mean calculation average error for linear load is 0.28% for active power and -0.33% for apparent power. Meanwhile, for nonlinear loads, the calculation average error for active power is 1.86% and apparent power is 0.47%.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.786
Earth stations are built to monitor the presence of satellites starting from satellite data, monitoring satellites, and carry out orders and corrections if needed. On the earth station there is a satellite data receiving antenna, the more elevation angle of the current satellite data receiver antenna can affect the time duration of the satellite data. The purpose of this research is to apply the Artificial Neural Network (ANN) method to design a time optimization system for satellite data at the LAPAN Pekayon earth station, East Jakarta. The data used as input is the elevation angle. The benefit of this research is expected to make it easier for operators and technicians to measure the time optimization of satellite data at earth stations. The best training results with learning rate = 0.2, error = 0.0001, max. epoch = 100000, neuron hidden layer = 15. The MSE value obtained is 0.0001 reaching the goal at epoch 68810. Regret the training / training reverse sequence reaches 0.99878. The best test result is to use learning speed 0.2 hidden layer neurons 15 comparison of training data = 54 and test data = 18. The accurate result is exactly the same as the specified error, namely 0.0001. The difference in the average target duration is 3 seconds compared to the ANN target. Artificial Neural Network (ANN) with the back propagation method of training function gradient descent (traingd), was successfully used to an optimization system for satellite data acquisition time at earth stations.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.830
A wheelchair is a tool that can be used to mobilize patients who experience paralysis, especially paralysis in the legs, so they can move from one place to another independently. An electric wheelchair is a type of wheelchair that can be controlled by a patient without having to be controlled by another person. The design of this tool utilizes an ADXL335 accelerometer sensor mounted on the patient's head as a determinant of the direction of wheelchair movement, BTS7960 as a DC motor driver, a motor wiper as the main drive for a wheelchair, and the Atmega328P microcontroller as an input and output processor. Gestures of the patient's head (looking down, looking up, head tilted to the right, tilting left) will produce a different voltage output which will be processed by Atmega328P as a determinant of the direction of motion which will be sent to the BTS790 driver to drive the wipper motor as the main driver of the wheelchair. The method of testing and measurement carried out is by testing the response of the ADXL335 accelerometer sensor with the test results of the ADXL335 accelerometer sensor having an accuracy of determining the direction of motion of 100%. The results of testing the average wheelchair speed of 2.3 km / hour with a patient weight of 40-60 kg, and the test results of battery endurance in a wheelchair of 5.07 hours with a patient weight of 40-70 kg with a 12V18Ah battery.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.914
Based on data from The Institute for Health Metrics and Evaluation (IHME) (2016), it shows that deaths in the world caused by heart and blood vessel diseases reached 17.7 million people or around 32.26 percent of total deaths in the world. The representation of heart health can be seen from the number of heart rate (HR) and oxygen saturation (SpO2). The purpose of this study is to determine the condition of the human body through the number of heartbeats and SpO2 using the MAX30100 sensor and to be able to find out the location of the patient via GPS Adafruit Ultimate Breakout. GPS calibration uses two calculation methods, namely using the Haversine formula and using the distance measurement feature on Google Earth. The method is then compared to get the difference between the coordinates obtained by the smartphone GPS as a reference with the patient's GPS (Adafruit Ultimate Breakout). The HR measurement calibration on the MAX30100 sensor is then compared with the pulse on the left wrist for accuracy. The value of the sensor is calculated every time when it detects a beat/pulse, while the manual count is calculated every 60 seconds. Therefore, the value on the sensor is taken using an average of 10 data to find the accuracy value. The results of this study indicate the level of coordinate accuracy obtained by GPS Adafruit is not more than 5 meters. The average HR accuracy value is 98,23 percent and the SpO2 calibration results get an accuracy of 98,99 percent. The waiting time required for the GPS to receive coordinates from the satellite with the GPS condition uncovered by the casing is about 7 to 13 minutes, while when the casing is closed the GPS cannot get the coordinates. MAX30100 can work optimally and obtain accurate values when the patient is in a relaxed position and does not do too much movement.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.884
Peak Flow Meter (PFM) is a tool to measure the amount of air flow in the airway (PFR) and to detect asthma. The output value of PFR can be influenced by several factors, such as age, respiratory muscle strength, height and gender. In this research, airway measurements are used to measure the condition of patients suffering from asthma. The author aims to make this tool so that it can find out how to design and make a peak flow meter output sound tool, measure the peak current and can know how the MPXV7002DP sensor works in regulating output in the form of sound. The method used by the author is to design or make a tool peak flow meter output sound. This MPXV7002DP sensor works when the sensor receives air blows from the flow sensor which automatically reads the highest air pressure from the breath. The test results using the VT Mobile Medical Gas Flow Analyzer prove that the largest percentage error is 2.4%, with the blowing rate on the Peak Flow Meter is 64.0 lpm and the blowing rate on VT mobile is 62.50 lpm. Therefore, this tool can be said to be very certain to detect asthma. Then it can be concluded that the peak flow meter is feasible and meets the specified requirements.Peak Flow Meter (PFM) is a tool to measure the amount of air flow in the airway (PFR) and to detect asthma. The value of PFR can be influenced by several factors such as age, respiratory muscle strength, height and gender. Airway measurements are used to measure the condition of patients suffering from asthma. The author aims to make this tool so that it can find out how to design and make a peak flow meter output sound tool, measure the peak current and can know how the MPXV7002DP sensor works in regulating output in the form of sound. The method used by the author is to design or make a tool peak flow meter output sound. This MPXV7002DP sensor works when the sensor receives air blows from the flow sensor which automatically reads the highest air pressure from the breath. The test results using the VT Mobile Medical Gas Flow Analyzer prove that the largest percentage error is 2.4%, with the blowing rate on the Peak Flow Meter is 64.0 lpm and the blowing rate on VT mobile is 62.50 lpm, so this tool can be said to be very certain to detect asthma. Then it can be concluded that the peak flow meter is feasible and meets the specified requirements.
AVITEC, Volume 3; https://doi.org/10.28989/avitec.v3i1.909
Rotation angle estimates are often required and applied to the dynamics of spacecraft, UAVs, robots, underwater vehicles, and other systems before control. IMU is an electronic module that is used as an angle estimation tool but has noise that can reduce the accuracy of the estimation. This study aims to develop an estimation model for the angle of rotation of a rigid body based on the IMU-gyroscope sensor on a smartphone using a Kalman filter. The estimation model is developed in a simple dynamic equation of motion in state-space. Kalman filters are designed based on system dynamics models to reduce noise in sensor data and improve measurement estimation results. Simulations are carried out with software to investigate the accuracy of the developed estimation algorithm. Experiments were carried out on several smartphone rotations during the roll, pitch, and yaw. Then, the experimental data obtained is analyzed for accuracy by comparing the built-in algorithms on smartphones. Based on the experimental results, the accuracy rate of estimation angle is 94% before going through the Kalman filter and an accuracy level of above 98% after going through the Kalman filter for every rotation on the x-axis, y-axis, and z-axis.
AVITEC, Volume 2; https://doi.org/10.28989/avitec.v2i2.670
In LTE Advanced technology there are two methods used in the duplexing process, there are frequency division duplex (FDD) wherein this duplexing concept communication is divided based on the frequency and the other is time division duplex (TDD) where communication is divided based on the time. Duplexing using the TDD method has advantages of handling data-based services that the majority have Non-Guarantee Bit Rate (N-GBR) characteristics because most of these services do not require a minimum bit rate to be able to work and this is an advantages because nowadays people like to use data-based services. So in this LTE Advanced network planning using the TDD method, frequency 2300 MHz for TD-LTE advanced, and parameters that to be the main focus are throughputs, reference signal received power (RSRP), reference signal strength indicator (RSSI), carrier to interference noise ratio (CINR), and block error rate (BLER). And the result of the simulations from TD-LTE Advanced planning are the mean of throughput value is 3,5 Mbps, mean of RSRP value is -110,8 dBm, mean of RSSI value is -72,36 dBm, mean of CINR value is 4,81 dB, and mean of BLER value is 0,07%.