Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231925
Nilam Ade Pangestu, R. Sigit, T. Harsono, Manik Retno Wahyunitisari, A. Anwar, Dinda Ayu Yunitasari
The diagnosis of tuberculosis (TB) is done by detecting and counting the number of mycobacterium tuberculosis in a sputum examination done manually using a microscope. It is considered ineffective because it requires a long time and different diagnostic results. To overcome this problem, this paper implements digital image processing. There are 5 processes used on the system. Preprocessing with the RGB to HSV method is used to clarify the color of the image. Segmentation to separate objects from background images using thresholding. Feature extraction to get the value of area, perimeter, and level of roundness of the object. Classification uses fuzzy logic to classify mycobacterium tuberculosis based on features. The next is the process of counting mycobacterium tuberculosis. And the last is the process of classify into IUATLD scale based on the number of mycobacterium tuberculosis. From the results of tests conducted on 15 data, the system show that the level of accuracy, precision, sensitivity and specificity of system in calculate mycobacterium tuberculosis is 89%, 90%, 91.66% and 78.88% respectively. And also level of sensitivity, specificity and accuracy of system in classifying the level of infection is 100%, 80 % and 93% respectively. This system was tested on a microscopic sputum image database of RSUD Dr. Soetomo from a different patient.
{"title":"Classification and Counting of Mycobacterium Tuberculosis from Sputum Microscopic Image using Fuzzy Logic","authors":"Nilam Ade Pangestu, R. Sigit, T. Harsono, Manik Retno Wahyunitisari, A. Anwar, Dinda Ayu Yunitasari","doi":"10.1109/IES50839.2020.9231925","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231925","url":null,"abstract":"The diagnosis of tuberculosis (TB) is done by detecting and counting the number of mycobacterium tuberculosis in a sputum examination done manually using a microscope. It is considered ineffective because it requires a long time and different diagnostic results. To overcome this problem, this paper implements digital image processing. There are 5 processes used on the system. Preprocessing with the RGB to HSV method is used to clarify the color of the image. Segmentation to separate objects from background images using thresholding. Feature extraction to get the value of area, perimeter, and level of roundness of the object. Classification uses fuzzy logic to classify mycobacterium tuberculosis based on features. The next is the process of counting mycobacterium tuberculosis. And the last is the process of classify into IUATLD scale based on the number of mycobacterium tuberculosis. From the results of tests conducted on 15 data, the system show that the level of accuracy, precision, sensitivity and specificity of system in calculate mycobacterium tuberculosis is 89%, 90%, 91.66% and 78.88% respectively. And also level of sensitivity, specificity and accuracy of system in classifying the level of infection is 100%, 80 % and 93% respectively. This system was tested on a microscopic sputum image database of RSUD Dr. Soetomo from a different patient.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127060570","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}
Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231875
A. Fariza, Mu’arifin, Amailina Puspitasari
Surabaya, one of the major cities in Indonesia, is an endemic area for spreading tuberculosis. Surabaya City Health Office in 2018 has found 7,007 cases of tuberculosis which is the highest case in East Java province. This data shows that TB is still a major health problem. TB risk mapping is needed to guide the Public Health Service in TB control planning, for example, the promotion of clean and healthy living behaviors, immunizations, and home visit programs and optimization of TB screening activities. This paper proposes the spatial risk mapping of tuberculosis based on several criteria that become tuberculosis risk factors using a fuzzy method called spatial fuzzy risk mapping. These criteria consist of the number of people with tuberculosis (BTA Positive), population density, unhealthy houses, and health facilities. Fuzzy multi-criteria decision making determines the weight value of each criterion, followed by the ranking process to select the best alternative from the sub-district areas. After fuzzy membership calculation, the sub-district areas area directly classified into 3 index level that is low, medium, and high according to the rule association. The determination of the TB disease risk index covers 31 sub-districts in Surabaya as densely populated urban areas. The risk map is visualized into spatial GIS mapping. In the last 3 years (2013-2015), there were 4 sub-districts are decreasing (12.9%), 6 sub-districts are increasing (19.4%) and the remaining 68.7% did not change. There are 13.33% sub-districts in 2015 that are defined as low risk by the fuzzy risk, but it must be high risk by the Public Health Service. The fuzzy risk index results appropriate with the real condition and it is suitable with the Public Health Service report.
{"title":"Spatial Fuzzy Risk Mapping for Tuberculosis in Surabaya, Indonesia","authors":"A. Fariza, Mu’arifin, Amailina Puspitasari","doi":"10.1109/IES50839.2020.9231875","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231875","url":null,"abstract":"Surabaya, one of the major cities in Indonesia, is an endemic area for spreading tuberculosis. Surabaya City Health Office in 2018 has found 7,007 cases of tuberculosis which is the highest case in East Java province. This data shows that TB is still a major health problem. TB risk mapping is needed to guide the Public Health Service in TB control planning, for example, the promotion of clean and healthy living behaviors, immunizations, and home visit programs and optimization of TB screening activities. This paper proposes the spatial risk mapping of tuberculosis based on several criteria that become tuberculosis risk factors using a fuzzy method called spatial fuzzy risk mapping. These criteria consist of the number of people with tuberculosis (BTA Positive), population density, unhealthy houses, and health facilities. Fuzzy multi-criteria decision making determines the weight value of each criterion, followed by the ranking process to select the best alternative from the sub-district areas. After fuzzy membership calculation, the sub-district areas area directly classified into 3 index level that is low, medium, and high according to the rule association. The determination of the TB disease risk index covers 31 sub-districts in Surabaya as densely populated urban areas. The risk map is visualized into spatial GIS mapping. In the last 3 years (2013-2015), there were 4 sub-districts are decreasing (12.9%), 6 sub-districts are increasing (19.4%) and the remaining 68.7% did not change. There are 13.33% sub-districts in 2015 that are defined as low risk by the fuzzy risk, but it must be high risk by the Public Health Service. The fuzzy risk index results appropriate with the real condition and it is suitable with the Public Health Service report.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133096800","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}
Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231676
Rika Rokhana, Wiwiet Herulambang, R. Indraswari
Melanoma is the most aggressive of all skin cancers and its incidence has reached epidemic proportions. It is important to distinguish between benign and malignant melanoma as early as possible to increase the chance of recovery. The development of computational technology, especially machine learning and computer vision, made it possible to classify diseases based on their image. Detection of a disease by using image is beneficial because it can be done more easily, cheaply, quickly, and non-invasively than by using biopsy. The use of conventional machine learning and computer vision method makes their classification performance highly affected by the segmentation result of the skin lesion and the features selected for the classification process. The recent development of deep learning algorithm, such as CNN (Convolutional Neural Network), makes it possible to classify images without going through the process of image segmentation and manual features determination and give high performance with enough training data. Therefore, in this research we propose a deep convolutional neural network (CNN) to classify melanoma images into benign and malignant class. The proposed network architecture consists of several sets of convolutional layers and max-pooling layers, followed by a drop out layer and a fully-connected layer. From the experimental results on 352 test images, the proposed network gives the accuracy, sensitivity, and specificity of 84.76%, 91.97%, and 78.71%. The good performance of the built model hopefully can be developed for real application that can assist the expert to make better diagnosis and treatment.
{"title":"Deep Convolutional Neural Network for Melanoma Image Classification","authors":"Rika Rokhana, Wiwiet Herulambang, R. Indraswari","doi":"10.1109/IES50839.2020.9231676","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231676","url":null,"abstract":"Melanoma is the most aggressive of all skin cancers and its incidence has reached epidemic proportions. It is important to distinguish between benign and malignant melanoma as early as possible to increase the chance of recovery. The development of computational technology, especially machine learning and computer vision, made it possible to classify diseases based on their image. Detection of a disease by using image is beneficial because it can be done more easily, cheaply, quickly, and non-invasively than by using biopsy. The use of conventional machine learning and computer vision method makes their classification performance highly affected by the segmentation result of the skin lesion and the features selected for the classification process. The recent development of deep learning algorithm, such as CNN (Convolutional Neural Network), makes it possible to classify images without going through the process of image segmentation and manual features determination and give high performance with enough training data. Therefore, in this research we propose a deep convolutional neural network (CNN) to classify melanoma images into benign and malignant class. The proposed network architecture consists of several sets of convolutional layers and max-pooling layers, followed by a drop out layer and a fully-connected layer. From the experimental results on 352 test images, the proposed network gives the accuracy, sensitivity, and specificity of 84.76%, 91.97%, and 78.71%. The good performance of the built model hopefully can be developed for real application that can assist the expert to make better diagnosis and treatment.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128131700","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}
Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231734
Faris Atoil Haq, B. S. B. Dewantara, Bayu Sandi Marta
ATRACTBOT (Autonomous Trash Can Robot) is a social robot that is equipped with Artificial Intelligence (AI) to carry out its task of collecting waste while still involving humans to raise awareness to dispose of trash in its place. The robot is designed to work indoors, so the ability to map workspaces is needed. In this paper, room mapping is done using eight ultrasonic sensors arranged in such a way that it covers an area of 360 degrees around the robot. The robot moves through the room automatically by using the Braitenberg control method to map the entire room. The experimental results show that the robot succeeded in mapping the room by distinguishing the different color plots for free space and occupied space.
{"title":"Room Mapping using Ultrasonic Range Sensor on the ATRACBOT (Autonomous Trash Can Robot): A Simulation Approach","authors":"Faris Atoil Haq, B. S. B. Dewantara, Bayu Sandi Marta","doi":"10.1109/IES50839.2020.9231734","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231734","url":null,"abstract":"ATRACTBOT (Autonomous Trash Can Robot) is a social robot that is equipped with Artificial Intelligence (AI) to carry out its task of collecting waste while still involving humans to raise awareness to dispose of trash in its place. The robot is designed to work indoors, so the ability to map workspaces is needed. In this paper, room mapping is done using eight ultrasonic sensors arranged in such a way that it covers an area of 360 degrees around the robot. The robot moves through the room automatically by using the Braitenberg control method to map the entire room. The experimental results show that the robot succeeded in mapping the room by distinguishing the different color plots for free space and occupied space.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132197946","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}
Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231855
E. Purwanto, Mentari Putri Jati, B. Sumantri, Muhammad Rizani Rusli
High-efficiency power electronics devices are necessary for induction motor drives. Moreover, induction motors have high usage rates. One efficient type is the AC-AC matrix converter with the advantage of single-stage conversion only. However, this type of converter has a big challenge when applied to the dynamic speed application on the induction motor because of its complexity. Generally, the type of speed controller which is widely used is the proportional-integral (PI) controller. Nevertheless, when applied in induction motor applications which are nonlinear systems with dynamic speed applications accompanied by complex converters, PI has some disadvantages. On the other hand, fuzzy logic offers the ability to handle nonlinear plants capable of covering the limitations of PI. The combination of these two controllers is called Fuzzy Supervisory Control (FSC). It is the best solution when applied to enhance dynamic performance. From the dynamic speed response simulation, the FSC produces 60% lower average total dynamic performance score than the PI. The lower the score the dynamic speed performance will be better. The performance of the FSC is also robust when handling the disturbance from the system. Based on this study, it can be analyzed that the FSC was able to enhance the dynamic performance of matrix converters fed induction motor drives.
{"title":"Performance Enhancement of Matrix Converter Fed Induction Motor Drives Using Fuzzy Supervisory Controller","authors":"E. Purwanto, Mentari Putri Jati, B. Sumantri, Muhammad Rizani Rusli","doi":"10.1109/IES50839.2020.9231855","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231855","url":null,"abstract":"High-efficiency power electronics devices are necessary for induction motor drives. Moreover, induction motors have high usage rates. One efficient type is the AC-AC matrix converter with the advantage of single-stage conversion only. However, this type of converter has a big challenge when applied to the dynamic speed application on the induction motor because of its complexity. Generally, the type of speed controller which is widely used is the proportional-integral (PI) controller. Nevertheless, when applied in induction motor applications which are nonlinear systems with dynamic speed applications accompanied by complex converters, PI has some disadvantages. On the other hand, fuzzy logic offers the ability to handle nonlinear plants capable of covering the limitations of PI. The combination of these two controllers is called Fuzzy Supervisory Control (FSC). It is the best solution when applied to enhance dynamic performance. From the dynamic speed response simulation, the FSC produces 60% lower average total dynamic performance score than the PI. The lower the score the dynamic speed performance will be better. The performance of the FSC is also robust when handling the disturbance from the system. Based on this study, it can be analyzed that the FSC was able to enhance the dynamic performance of matrix converters fed induction motor drives.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129587108","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}
The Robot Soccer uses the vision system to look for the ball continuously. The quality of vision object detection is the main factor that considered by the robot. Beside the quality, the performance of the detection process also affects the robot performance. The object detection is the heaviest process in entire ERSOW’s robot process. In this paper, we addressed the ways optimizing the vision object detection process that enhanced by the tracking method using Kaiman Filter. The Kaiman filter is also widely used for robotic purposes. The object has been equipped with a local ROI around them to limit the scanning on the entire frame when detection method is running. The local ROI will reduce the computation process and keeping the process in the sufficient resources that processor can handle. The Kaiman filter will predicted the object position and the direction of the object by considered the previous position and the times was taken. The Kaiman filter will lock the object and will follow the object without using detection feature anymore. From the results of tests conducted, the predicted value in several position has showed promising result. The average error on x-axis is 1.425 pixels and in y-axis 1.7226 pixels. This system can also reduce the average computation time from 31.67 Ms into 20.4 Ms. This research is expected to overcome the overwhelmed of the ERSOW’s computation and increased the performance of the robot
{"title":"Dynamic Local Ball Tracking in Middle Size League Robot Soccer ERSOW based on Kaiman Filter","authors":"M. Bachtiar, Iwan Kurnianto Wibowo, Rangga Dikarinata, Renardi Adryantoro Priambudi, Khoirul Anwar","doi":"10.1109/IES50839.2020.9231877","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231877","url":null,"abstract":"The Robot Soccer uses the vision system to look for the ball continuously. The quality of vision object detection is the main factor that considered by the robot. Beside the quality, the performance of the detection process also affects the robot performance. The object detection is the heaviest process in entire ERSOW’s robot process. In this paper, we addressed the ways optimizing the vision object detection process that enhanced by the tracking method using Kaiman Filter. The Kaiman filter is also widely used for robotic purposes. The object has been equipped with a local ROI around them to limit the scanning on the entire frame when detection method is running. The local ROI will reduce the computation process and keeping the process in the sufficient resources that processor can handle. The Kaiman filter will predicted the object position and the direction of the object by considered the previous position and the times was taken. The Kaiman filter will lock the object and will follow the object without using detection feature anymore. From the results of tests conducted, the predicted value in several position has showed promising result. The average error on x-axis is 1.425 pixels and in y-axis 1.7226 pixels. This system can also reduce the average computation time from 31.67 Ms into 20.4 Ms. This research is expected to overcome the overwhelmed of the ERSOW’s computation and increased the performance of the robot","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125409385","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}
Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231651
Nur Khomairoh, R. Sigit, T. Harsono, Y. Hernaningsih, A. Anwar
Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively.
{"title":"Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image","authors":"Nur Khomairoh, R. Sigit, T. Harsono, Y. Hernaningsih, A. Anwar","doi":"10.1109/IES50839.2020.9231651","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231651","url":null,"abstract":"Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116564925","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}
Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231774
Muhammad Alifudin Fahmi, I. Sudiharto, I. Ferdiansyah
The increasing need for electrical energy at the rate of an era, to meet the increase in the use of many alternative energy such as solar energy. The availability solar energy will never run out and solar energy can also be used as an alternative energy that can convert to electrical energy. Solar energy has a fluctuating nature where there is always a change in the amount of energy over time. By maximizing the utilization of solar panel energy can be achieved by the existence of methods such as MPPT (Maximum Power Point Tracking). Particle Swarm Optimization (PSO) is an algorithm that can be used as an MPPT, where PSO will learn every irradiation change that occurs and get maximum power which will then be used as a source for the battery charger. In this paper, using a hybrid power system that uses a source from PV and the grid 220Vac PLN. The sources obtained from the PLN grid will be used as a backup source. Using the Particle Swarm Optimization method as MPPT is able to get power of 198.85 Watt with efficiencies above 95% in the hybrid power system for battery chargers, and the presence of the PLN Grid as a backup source, when the PV system does not meet the load power requirements.
{"title":"Particle Swarm Optimization Implementation as MPPT on Hybrid Power System","authors":"Muhammad Alifudin Fahmi, I. Sudiharto, I. Ferdiansyah","doi":"10.1109/IES50839.2020.9231774","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231774","url":null,"abstract":"The increasing need for electrical energy at the rate of an era, to meet the increase in the use of many alternative energy such as solar energy. The availability solar energy will never run out and solar energy can also be used as an alternative energy that can convert to electrical energy. Solar energy has a fluctuating nature where there is always a change in the amount of energy over time. By maximizing the utilization of solar panel energy can be achieved by the existence of methods such as MPPT (Maximum Power Point Tracking). Particle Swarm Optimization (PSO) is an algorithm that can be used as an MPPT, where PSO will learn every irradiation change that occurs and get maximum power which will then be used as a source for the battery charger. In this paper, using a hybrid power system that uses a source from PV and the grid 220Vac PLN. The sources obtained from the PLN grid will be used as a backup source. Using the Particle Swarm Optimization method as MPPT is able to get power of 198.85 Watt with efficiencies above 95% in the hybrid power system for battery chargers, and the presence of the PLN Grid as a backup source, when the PV system does not meet the load power requirements.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114747417","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}
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