{"title":"Analysis of Brain Wave Activity Realtime Using NeuroSky Sensors With LabVIEW","authors":"D. A. Pratama, Masayu Anisah, Richi Agung Pratama","doi":"10.33387/protk.v10i3.6227","DOIUrl":"https://doi.org/10.33387/protk.v10i3.6227","url":null,"abstract":"The brain is the part of the body that gives us the ability to live, react to all external stimuli, and coordinate our entire body. The human brain constantly generates electrical impulses. These electric currents are often referred to as brain waves. EEG (electroencephalography) is a bioelectrical measurement used in the biomedical field to study the human brain. Through this research, a sensor system will be developed that can detect brain waves non-invasively and transmit signals wirelessly via a Bluetooth connection. The detected EEG signal will be displayed in graphical form using signal parameters. To obtain brain wave signals, sensor electrodes are placed directly on reference points on the surface of the scalp in the front and left ears. The captured brainwave signal will be wirelessly transmitted via USB Bluetooth BLE 4.0. Next, the brainwave signal data will be converted and processed via USB Bluetooth BLE 4.0, which is connected to the USB port on the laptop. Then, the brain wave signal will be displayed in graphical form in real-time and analyzed using LabVIEW software. The results of this study indicate that the monitoring system that works on LabVIEW can display real-time data from the NeuroSky sensor wirelessly, and the type of brain waves and the frequency of the resulting brain waves can vary depending on the condition of the brain at the time","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124071456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design of Automatic Gate Rolling Door Control System Using Rain Drop Sensor","authors":"Afan Taufiqurrohman, Imelda Simanjuntak","doi":"10.33387/protk.v10i3.4895","DOIUrl":"https://doi.org/10.33387/protk.v10i3.4895","url":null,"abstract":"- Rolling doors used by small-scale industrial companies are still operated manually with conventional up, down, and stop button panels. This is the cause of the problem of inefficient transportation mobility that operates and causes product losses due to rainwater splashing that enters the warehouse. Therefore, this study aims to implement an automatic rolling door with a DC motor drive assisted by a raindrop sensor. The research method used is a literature study approach: identifying problems, determining the focus and research objectives, designing and implementing prototype solutions, testing, discussing, and drawing conclusions. Based on system testing, it was found that the response time for reading the rain sensor took 1.19 seconds, and the response time for reading the rain sensor to detect light again was 0.92 seconds. The delay time for the DC motor to rotate to close the gate when it receives a sensor signal in rainy conditions is 1.34 seconds, and the delay time for opening the gate when it receives a sensor signal that is bright again or the sensor is dry is 0.98 seconds. Based on the results of system testing, it was found that the delay time buzzer sounded as a warning sign if someone crossed it for 0.86 seconds. Overall, the test results show that the system is running well according to the functions, and system algorithms are active at the same time.","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124325431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Tamsir, Vivi Rosida, Asmah Akhriana, Indo Intan, St. Amina H.Umar
{"title":"Analytical Hierarchy Process Algorithm for Traffic Sign Improvement Priority","authors":"N. Tamsir, Vivi Rosida, Asmah Akhriana, Indo Intan, St. Amina H.Umar","doi":"10.33387/protk.v10i3.5109","DOIUrl":"https://doi.org/10.33387/protk.v10i3.5109","url":null,"abstract":"- Traffic signs are part of road equipment that is very important for motorists because they can provide direction while on the highway, and if there is damage, repair or replacement must be carried out immediately because it can cause traffic accidents. Data collection for damaged traffic signs is still done using the manual method, so it takes a long time. Therefore, a web-and android-based application was designed that implements the Analytical Hierarchy Process (AHP) algorithm in determining the priority of repair or replacement of traffic signs on the route of South Sulawesi Province. As a result of this research, the public can report the type of damage and its location via Android, and then the officer processes the data so that it displays the type of damage that is a priority for repair or replacement. Implement the Analytical Hierarchy Process algorithm into the application for prioritization of traffic sign improvement using two (two) web-based and Android platforms. System design using UML produces use cases (2 actors, admin, and user) and class diagrams (15 admin classes and 4 user classes). The black box used as a test produced 40 modules, of which all were in line with expectations.","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"1973 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131329650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detection and Analysis of CO, CO2 Exhaust Emissions in Two-Wheeled Motorized Vehicles","authors":"Kusno Suryadi, Burhan Fazzry","doi":"10.33387/protk.v10i3.4497","DOIUrl":"https://doi.org/10.33387/protk.v10i3.4497","url":null,"abstract":"-- CO and CO2 are some of the exhaust gases resulting from the combustion of motor vehicles. The increase in the number of vehicles causes the concentrations of CO and CO2 exhaust gases in the air to increase. To be able to detect the amount of CO gas concentration using the MQ7 sensor and the CO2 sensor, the TGS4161 sensor is used. For the sensor to work properly, it needs a normalization process before it is applied to the testing stage. Based on the test results, the sensor normalization process requires 54 seconds with a maximum CO gas concentration of 4139.7 ppm, or 0.413%, and an output voltage of 4.39 volts. The CO2 exhaust gas test resulted in a maximum gas concentration of 5862.06 ppm, or 0.586%, with an output voltage of 0.51 volts. From several test results, the amount of exhaust gas concentration is dominantly determined by the type of vehicle and engine speed, while the type of fuel does not significantly affect the exhaust gas concentration, with an average percentage difference of 1.2%.","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123234674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Djafar, Abdul Halim, Mustofa Mustofa, L. A. Latif, W. H. Piarah
{"title":"Correlation of Sunlight Intensity and Output Voltage on Collector Plate-based Cascaded Thermoelectric Generator Modules","authors":"Z. Djafar, Abdul Halim, Mustofa Mustofa, L. A. Latif, W. H. Piarah","doi":"10.33387/protk.v10i3.6375","DOIUrl":"https://doi.org/10.33387/protk.v10i3.6375","url":null,"abstract":"The intensity of solar energy is an important factor in viewing the performance of the thermoelectric generator (TEG). Most studies only look at the effect of treatment on the TEG module in the form of cooling mode and its materials. Therefore this study examines the effect of the solar intensity value on the magnitude of the module voltage. On the hot side of the module are placed heat absorber plates of copper, Fe and aluminum plates as well as non collector plates. Modules are cascaded and connected in series as many as 14 modules per plate, so that a total of 56 TEG modules are used. Data collection is carried out simultaneously on all plates. The test results show that the increase in solar intensity is liner with the magnitude of the TEG module voltage or in other words the correlation is positive. The data also shows that copper collector plates produce the highest voltage difference, followed by Ferro, Alumina and no-plates at ∆V 0.871, 0.805; 0.369 and 0.153 V, respectively","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132169782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of the Effect of Magnetic Thickness and Rotating Speed on PMSG 24 Slot 16 Pole Characteristics","authors":"Achiruddin Lubis, Z. Aini","doi":"10.33387/protk.v10i3.6059","DOIUrl":"https://doi.org/10.33387/protk.v10i3.6059","url":null,"abstract":"– Wind energy is one of the alternative energies that can overcome global warming caused by fossil energy. Permanent Magnet Synchronous Generator (PMSG) has a higher efficiency compared to other types of generators. The previous permanent magnet synchronous generator model was only able to produce efficiency at a rotational speed of 500 rpm of 67.30% and at a rotational speed of 1500 rpm of 80.9%, so further research is needed to get a higher efficiency value. This study aims to analyze the effect of magnetic thickness and rotational speed on PMSG characteristics and obtain a higher efficiency value. Using variations in magnetic thickness of 7.5mm, 9 mm, and 10 mm and variations in rotational speed of 500 rpm, 1000 rpm, and 1500 rpm using software based on Finite Element Methode, this study obtained the results of the largest current, voltage, input power, and output power at a magnetic thickness of 10mm with a rotational speed of 1500 rpm of 20.40 A, 204.06 V, 4979.60 W, and 4266.21 W, with the greatest efficiency being in the magnetic thickness of 9mm and 10 rpm of 89.20%.","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124809046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Implementation of Fuzzy Logic in Soil Moisture and Temperature Control System for Araceae Plants Based on LoRa","authors":"Irma Salamah, Suzanzefi Suzanzefi, Shinta Sulistiya Ningrum","doi":"10.33387/protk.v10i3.6390","DOIUrl":"https://doi.org/10.33387/protk.v10i3.6390","url":null,"abstract":"The Araceae plants are highly popular among plant enthusiasts worldwide. The Araceae family has more than 100 different types and thousands of species. Despite ever-evolving times, these plants have a high market value and challenging breeding methods. To achieve optimal quality, it is important to maintain the appropriate humidity and temperature according to the natural conditions of these plants in the tropical rainforest. The Node MCU ESP32 is a processor for instructions from the room temperature sensor, room humidity sensor, and soil moisture sensor. Additionally, this component controls the blower and misting system as output, which will be processed through LoRa technology to transmit monitoring data to the Blynk software. This study utilizes fuzzy logic to categorize room temperature, humidity, soil moisture, and output results for different Araceae plants. LoRa technology is used to send monitoring data efficiently in the data transmission process. During data retrieval using long-range technology, a delay of approximately 5 seconds is known between the receiver and transmitter at a distance of 700 meters. Constraints that cause issues with this long-range technology are influenced by wind, which affects antenna signal strength, and the presence of trees and buildings as obstacles. The monitoring results show an average temperature in normal conditions and an average humidity in wet conditions. At the same time, soil moisture is monitored to maintain normal humidity, resulting in all outputs being off.","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134218888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Electricity Potential by Bioethanol Fuel from Pineapple Skin Waste, Kualu Nanas Village, Kampar Regency, Riau","authors":"Varkhan Bijaksana, Nanda Putri Miefthawati","doi":"10.33387/protk.v10i3.6263","DOIUrl":"https://doi.org/10.33387/protk.v10i3.6263","url":null,"abstract":"Kualu Nenas was a pineapple producer in Riau who produced 4 tons of pineapples a day. This production produced 36 tons of waste per month. This waste created problems for the environment, including odor and methane gas, whereas pineapple peel waste included glucose, which can be used to produce bioethanol. This study aimed to analyze the bioethanol potential of pineapple peel and the potential for electricity and power, calculate the values of TFC and SFC, and determine the efficiency of the fuel mixture, which was tested on an 8 kW generator in 30 minutes. This research uses fermentation and distillation methods, which are simulated by a superpro designer. From the research conducted, the potential for bioethanol was 6,262.63 L/month or 68,871.54 L/year with an ethanol content of 99.9995% and 0.0005% water. The electricity is 75.39 MWh/month for E0, 71.98 MWh/month for E10, and 46.88 MWh/month for E100. The power potential generated is 3.14 MW/month for E0, 2.99 MW/month for E10, and 1.95 MW/month for E100. From testing with an 8 kW generator, the TFCs of E0, E10, and E100 fuels were 0.834, 0.835, and 0.839 liters/hour, respectively. While the SFC of E0, E10, and E100 fuels were 0.1043, 0.1044, and 0.1049 liters/hour, with efficiencies of 50.82%, 52.98%, and 80.95%.","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115448351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Power Performance Evaluation of Standalone Renewable Energy Source Energy Management Using Pass Filter","authors":"I. Sulistiyowati, J. Jamaaluddin, I. Anshory","doi":"10.33387/protk.v10i3.6082","DOIUrl":"https://doi.org/10.33387/protk.v10i3.6082","url":null,"abstract":"Hybrid energy storage systems have shown promise in enhancing solar panel systems reliability and efficiency. However, managing the power distribution balance with multiple energy storage units remains a challenge. This research addresses power distribution balancing during solar irradiation intermittency by employing a first-order filter with lowpass and highpass characteristics. The study aims to investigate energy and power management issues for off-grid electrical systems using this filter. Results from numerical simulations and experiments with a hybrid energy storage setup comprising a battery and supercapacitor show that the first-order filter effectively allocates the first-order signal to the main energy storage and subsequent orders to the supporting energy storage. During increased intermittency, the battery contributes 62 W and stores 387 W, while the supercapacitor contributes 167 W and stores 297 W. Conversely, during reduced intermittency, the battery stores 390 W and contributes 62 W, and the supercapacitor stores 295 W and contributes 164 W. The findings demonstrate the filter's efficacy in optimizing power distribution balance within hybrid energy storage systems. However, using the supercapacitor as the main energy storage does not result in higher power efficiency compared to the battery. In conclusion, the First Order Filter presents a viable solution for addressing power distribution challenges in hybrid energy storage systems, contributing to improved energy and power management for off-grid electrical systems. Further research on cost-effectiveness, maintenance, and environmental impact is warranted for practical implementation","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121629228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimization of Permanent Magnet Synchronous Generator Output Power in Wind Power Plants with ANN Back Propagation","authors":"Sapto Nisworo, Deria Pravitasari, Iis Hamsir Ayub Wahab","doi":"10.33387/protk.v10i2.6040","DOIUrl":"https://doi.org/10.33387/protk.v10i2.6040","url":null,"abstract":"The focus of this research is optimizing a wind power plant using a Permanent Magnet Synchronous Generator (PMSG). The backpropagation method of the artificial neural network system was chosen to optimize the output power of the wind power generator. Based on the simulation results, the backpropagation algorithm of the artificial neural network obtains the output power based on the input variable in the form of changing wind speed. The results show that the best value is learning rate = 0.5, error = 0.0001, max. epoch= 100000, neuron hidden layer = 5. The Mean Square Error (MSE) value obtained is 0.1026 reaching the goal at epoch 14845. The reverse training regretion reaches 0.99917. The optimization results are close to the specified error, which is 0.0001, while what is obtained is 0.0145. The power generated by the wind speed is 10.7 m/s before being optimized using the back propagation neural network method worth 321 watts, while the optimized power results are 409 watts. The difference in the average target power obtained is 88 watts compared to the power of the Artificial Neural Network (ANN). ","PeriodicalId":351870,"journal":{"name":"PROtek : Jurnal Ilmiah Teknik Elektro","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127574135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}