Journal Information
ISSN / EISSN : 2685-2381 / 2715-2626
Current Publisher: SENATIK (10.28989)
Total articles ≅ 30
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Lasmadi Lasmadi, Freddy Kurniawan, Muhammad Irfan Pamungkas
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.909

Abstract:
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.
Patrisius Kusi Olla, Wilia Azhar
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.884

Abstract:
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.
Faisal Ahmad Ilham Nuari, Uke Kurniawan Usman, At Hanuranto
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.893

Abstract:
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.
Tiar Prilian, Iyus Rusmana, Trie Handayani
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.830

Abstract:
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.
Wilda Noer Agustianingsih, Freddy Kurniawan, Paulus Setiawan
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.794

The publisher has not yet granted permission to display this abstract.
Anis Maghfirotul Habibah, Ibrahim Nawawi, Ika Setyowati
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.786

The publisher has not yet granted permission to display this abstract.
Almira Budiyanto, Ardymas Jati Putu Mardana
Published: 17 November 2020
AVITEC, Volume 3; doi:10.28989/avitec.v3i1.914

Abstract:
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.
Aldy Mohamad, Purnawarman Musa
Published: 3 August 2020
AVITEC, Volume 2; doi:10.28989/avitec.v2i2.712

Abstract:
Technology is growing from year to year even day to day, this has made the increasing need for infrastructure that supports especially in aspects of computer networks. The increasing number of traffic that is burdening the router or switch encourages the increasing number of nodes to network devices with the aim of reducing and dividing the burden on network traffic. The need for traffic management and control is very important because with the increasing number of network devices and the higher traffic, making a network administrator need more time to handle if there are problems in the network. This research is trying to implement open vSwitch technology on low-cost raspberry pi devices. And by applying the traffic shaping and traffic rate methods by utilizing the traffic control feature on Linux, and then try to divide the amount of traffic received by network devices so that the traffic load becomes controlled. The results of this study, show the results of successful implementation and traffic management work well.
Muhammad Ari Roma Wicaksono, Freddy Kurniawan, Lasmadi Lasmadi
Published: 3 August 2020
AVITEC, Volume 2; doi:10.28989/avitec.v2i2.752

Abstract:
This study aims to develop a Kalman filter algorithm in order to reduce the accelerometer sensor noise as effectively as possible. The accelerometer sensor is one part of the Inertial Measurement Unit (IMU) used to find the displacement distance of an object. The method used is modeling the system to model the accelerometer system to form mathematical equations. Then the state space method is used to change the system modeling to the form of matrix operations so that the process of the data calculating to the Kalman Filter algorithm is not too difficult. It also uses the threshold algorithm to detect the sensor's condition at rest. The present study had good results, which of the four experiments obtained with an average accuracy of 93%. The threshold algorithm successfully reduces measurement errors when the sensor is at rest or static so that the measurement results more accurate. The developed algorithm can also detect the sensor to move forward or backward.
Farobi Widia Nanda, Freddy Kurniawan, Paulus Setiawan
Published: 3 August 2020
AVITEC, Volume 2; doi:10.28989/avitec.v2i2.734

Abstract:
The analog AC-voltmeter usually can only measure the ideal-sinusoid voltage with narrow frequency range. Meanwhile, in fact the grid voltage is often not in the form of an ideal sinusoidal. To be able to measure a non-sinusoidal AC voltage with a wide range of frequency, a true-RMS voltmeter is needed. The research designed a true RMS measuring system using an ATmega 328P microcontroller. The input voltage is converted to pulse using Schmit triger and fed to the microcontroller’s external interrupt pin to calculate the input signal frequency. Meanwhile the microcontroller’s ADC sampled the input signal with a frequency of 128 times the signal’s frequency. RMS voltage calculations are performed using arithmetic operations for 16 and 32 bit integer variables. The test results show that the system can measure voltages with zero errors from 100 to 275 volts with a frequency of 50 Hz. The system can also measure voltages with zero errors at 220 volt with frequencies from 40 Hz to 150 Hz. However, this system can still be used to measure voltages ranging from 25 volts to 300 volts at frequencies from 35 Hz to 195 Hz with an average error of 0.21%. During RMS voltage calculation, the microcontroller’s CPU usage was 13.35%, so that this system can be further developed.
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