Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315377
Muladi, Aripriharta, I. Zaeni, S. Sendari, A. Mahamad, Fahmi, Yusrandi
Coronavirus disease 19 (COVID19) is a disease caused by the new coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has infected almost the entire world with a total of 47.5 million sufferers and a death toll of 1.2 million people so that WHO categorizes it as a global pandemic. The COVID19 case in Indonesia still shows an increasing trend even though various prevention efforts have been made. Proven efforts to reduce the spread of COVID19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home. Several government and private agencies have required employees to report their health conditions via web pages. Real-time and accurate mobile applications can help prevent the spread of COVID19. This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology. The application will collect GPS-based location data, the number of people in the vicinity identified via Bluetooth, and the user's body condition in the form of temperature and oxygen levels in the blood. User data is stored and processed in a real time database in cloud computing which can be accessed through an application on the user's smartphone. The database also stores data on Covid19 sufferers and where they live. The application provides alerts when in a crowd and notifies the status of the region the user is in. Advice is given by the app when the recording of the body condition points to the early symptoms of COVID19.
{"title":"Development of The Personnel Monitoring System Using Mobile Application and Real-Time Database During the COVID19 Pandemic","authors":"Muladi, Aripriharta, I. Zaeni, S. Sendari, A. Mahamad, Fahmi, Yusrandi","doi":"10.1109/ISRITI51436.2020.9315377","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315377","url":null,"abstract":"Coronavirus disease 19 (COVID19) is a disease caused by the new coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has infected almost the entire world with a total of 47.5 million sufferers and a death toll of 1.2 million people so that WHO categorizes it as a global pandemic. The COVID19 case in Indonesia still shows an increasing trend even though various prevention efforts have been made. Proven efforts to reduce the spread of COVID19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home. Several government and private agencies have required employees to report their health conditions via web pages. Real-time and accurate mobile applications can help prevent the spread of COVID19. This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology. The application will collect GPS-based location data, the number of people in the vicinity identified via Bluetooth, and the user's body condition in the form of temperature and oxygen levels in the blood. User data is stored and processed in a real time database in cloud computing which can be accessed through an application on the user's smartphone. The database also stores data on Covid19 sufferers and where they live. The application provides alerts when in a crowd and notifies the status of the region the user is in. Advice is given by the app when the recording of the body condition points to the early symptoms of COVID19.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128983287","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 increasing of nonlinear load has a currently become recurring problem which can worsen the condition of the system. Effect of placement and sizing single tuned filter on power quality of radial distribution system has been investigated to minimum power losses on passive and active system (after DG Placement). Particle Swarm Optimization (PSO) has been used to establish of the objective function. The effectiveness of method on power quality issues have been studied on IEEE 33-bus Standard System using MATLAB. The simulation result verified that using nonlinear load on each case the proposed strategy can be a robust approach to improve performance system on power quality for mitigate harmonic polluted.
{"title":"Single Tuned Filter Planning to Mitigate Harmonic Polluted in Radial Distribution Network Using Particle Swarm Optimization","authors":"Muhira Dzar Faraby, Andi Fitriati, Christiono, Usman, Akhyar Muchtar, Andi Nur Putri","doi":"10.1109/ISRITI51436.2020.9315518","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315518","url":null,"abstract":"The increasing of nonlinear load has a currently become recurring problem which can worsen the condition of the system. Effect of placement and sizing single tuned filter on power quality of radial distribution system has been investigated to minimum power losses on passive and active system (after DG Placement). Particle Swarm Optimization (PSO) has been used to establish of the objective function. The effectiveness of method on power quality issues have been studied on IEEE 33-bus Standard System using MATLAB. The simulation result verified that using nonlinear load on each case the proposed strategy can be a robust approach to improve performance system on power quality for mitigate harmonic polluted.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124213603","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315447
Siddartha Mootha, S. Sridhar, M. S. K. Devi
With the rapid development of the internet, the field of E-Commerce has seen tremendous growth. The easy accessibility of viewing and purchasing products, and having it delivered to your doorstep is what makes E-Commerce extremely successful. E-Commerce also caters to almost every possible field, ranging from electronics to fashion to groceries. The number of visitors on E-Commerce websites is growing at a rapid pace, but the number of purchases remains constant. A novel stacking ensemble system has been proposed, which makes use of Multi-Layer Perceptron's to detect the intention of a user on whether a product would be purchased or not. ‘Online Shoppers Purchasing Intention’ Dataset has been used. The proposed stacking ensemble model achieved an accuracy of 94% in predicting whether a user will purchase a product or not, based on the session details. To evaluate the proposed system, it is compared to over 15 classification algorithms, as well as existing systems that make use of the dataset. The results obtained show that the proposed stacking classifier outperforms the various classification algorithms as well as existing systems that make use of the dataset.
{"title":"A Stacking Ensemble of Multi Layer Perceptrons to Predict Online Shoppers' Purchasing Intention","authors":"Siddartha Mootha, S. Sridhar, M. S. K. Devi","doi":"10.1109/ISRITI51436.2020.9315447","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315447","url":null,"abstract":"With the rapid development of the internet, the field of E-Commerce has seen tremendous growth. The easy accessibility of viewing and purchasing products, and having it delivered to your doorstep is what makes E-Commerce extremely successful. E-Commerce also caters to almost every possible field, ranging from electronics to fashion to groceries. The number of visitors on E-Commerce websites is growing at a rapid pace, but the number of purchases remains constant. A novel stacking ensemble system has been proposed, which makes use of Multi-Layer Perceptron's to detect the intention of a user on whether a product would be purchased or not. ‘Online Shoppers Purchasing Intention’ Dataset has been used. The proposed stacking ensemble model achieved an accuracy of 94% in predicting whether a user will purchase a product or not, based on the session details. To evaluate the proposed system, it is compared to over 15 classification algorithms, as well as existing systems that make use of the dataset. The results obtained show that the proposed stacking classifier outperforms the various classification algorithms as well as existing systems that make use of the dataset.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128840321","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 : 2020-12-10DOI: 10.1109/isriti51436.2020.9315521
Diash Firdaus, R. Munadi, Yudha Purwanto
SDN (Software Defined Network) is the future of networking and has attracted great interest as a new paradigm in networking. SDN has centralized control by separating control plane and data plane, it will be very vulnerable to DDoS attacks. To improve security, it requires high detection accuracy and efficiency. To detect DDoS attacks on SDN we propose DDoS detection using Machine Learning with Ensemble Algorithm. At the experimental stage, we used InSDN as a dataset. This study consists of two methodologies. The first step is the clustering and classification method, the clustering and classification method has two stages, the first stage is feature selection and normalization, and the second stage is Ensemble Algorithm clustering and classification. The second step is the detection validation method in SDN using the Mininet emulator. We use Ensemble Algorithm K-means++ and Random Forest to obtain High detection accuracy and efficiency.
{"title":"DDoS Attack Detection in Software Defined Network using Ensemble K-means++ and Random Forest","authors":"Diash Firdaus, R. Munadi, Yudha Purwanto","doi":"10.1109/isriti51436.2020.9315521","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315521","url":null,"abstract":"SDN (Software Defined Network) is the future of networking and has attracted great interest as a new paradigm in networking. SDN has centralized control by separating control plane and data plane, it will be very vulnerable to DDoS attacks. To improve security, it requires high detection accuracy and efficiency. To detect DDoS attacks on SDN we propose DDoS detection using Machine Learning with Ensemble Algorithm. At the experimental stage, we used InSDN as a dataset. This study consists of two methodologies. The first step is the clustering and classification method, the clustering and classification method has two stages, the first stage is feature selection and normalization, and the second stage is Ensemble Algorithm clustering and classification. The second step is the detection validation method in SDN using the Mininet emulator. We use Ensemble Algorithm K-means++ and Random Forest to obtain High detection accuracy and efficiency.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116130570","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315379
Faisal Ridwan Siregar, Wikky Fawwaz Al Maki
In this paper, we present an algorithm of flower classification. The image data used in this study was obtained from the Oxford 102 Flowers dataset. We classified 16368 flower images which were obtained by applying a set of augmentation process on each image in the dataset. The images were segmented by using GrabCut method. Then, a hybrid method of feature extraction was employed to the segmented images. The so-called Moment Invariants was used to extract shape features whereas the Color Moments was employed to extract color features. The proposed hybrid method of feature extraction is proven to be good for declaring objects by considering color, shape, and object area. Further, we implemented Random Forest as the classifier. The proposed algorithm of flower classification provided satisfactory results based on stratified k-fold cross-validation tests where an optimal k value was obtained by using the elbow method. Our experimental results shows that the proposed model yields accuracy of 88,74%.
{"title":"Hybrid Method for Flower Classification in High Intra-class Variation","authors":"Faisal Ridwan Siregar, Wikky Fawwaz Al Maki","doi":"10.1109/ISRITI51436.2020.9315379","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315379","url":null,"abstract":"In this paper, we present an algorithm of flower classification. The image data used in this study was obtained from the Oxford 102 Flowers dataset. We classified 16368 flower images which were obtained by applying a set of augmentation process on each image in the dataset. The images were segmented by using GrabCut method. Then, a hybrid method of feature extraction was employed to the segmented images. The so-called Moment Invariants was used to extract shape features whereas the Color Moments was employed to extract color features. The proposed hybrid method of feature extraction is proven to be good for declaring objects by considering color, shape, and object area. Further, we implemented Random Forest as the classifier. The proposed algorithm of flower classification provided satisfactory results based on stratified k-fold cross-validation tests where an optimal k value was obtained by using the elbow method. Our experimental results shows that the proposed model yields accuracy of 88,74%.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132855","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315405
Astriany Noer, Farid Armin, Kamirul, Janne Anna Christa Wejay
Autonomous regional navigation system provides independent and accurate positioning services continuously to a selected region. This paper presents the space segment's design algorithm for the Indonesian regional navigation system, which will provide real-time self-reliance positioning, velocity, and timing services to the civilians and more accurate positional service to military personnel. The purpose of this research is to find the optimal satellite constellation of combined Geostationary (GEO) and Geosynchronous (GSO) satellites with 100% coverage throughout Indonesia territory and better positioning service to the user using a minimum number of satellites. The Genetic Algorithm (GA) has been used for finding optimal satellite constellation, which gives the least mean Geometric Dilution of Precision (GDOP) over the selected places in Indonesia. Then the optimal constellation is simulated in System Tool Kit (STK) to verify the performance of the Genetic Algorithm results. The design algorithm's output depicts that the optimized satellite constellation for the Indonesian regional navigation system consists of 3 GEO and 4 GSO satellites. The simulation results in STK indicate that the proposed satellite constellation provides accurate position and navigation services to entire Indonesia with the mean GDOP of less than 4 value.
{"title":"Design of Optimal Satellite Constellation for Indonesian Regional Navigation System based on GEO and GSO Satellites","authors":"Astriany Noer, Farid Armin, Kamirul, Janne Anna Christa Wejay","doi":"10.1109/ISRITI51436.2020.9315405","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315405","url":null,"abstract":"Autonomous regional navigation system provides independent and accurate positioning services continuously to a selected region. This paper presents the space segment's design algorithm for the Indonesian regional navigation system, which will provide real-time self-reliance positioning, velocity, and timing services to the civilians and more accurate positional service to military personnel. The purpose of this research is to find the optimal satellite constellation of combined Geostationary (GEO) and Geosynchronous (GSO) satellites with 100% coverage throughout Indonesia territory and better positioning service to the user using a minimum number of satellites. The Genetic Algorithm (GA) has been used for finding optimal satellite constellation, which gives the least mean Geometric Dilution of Precision (GDOP) over the selected places in Indonesia. Then the optimal constellation is simulated in System Tool Kit (STK) to verify the performance of the Genetic Algorithm results. The design algorithm's output depicts that the optimized satellite constellation for the Indonesian regional navigation system consists of 3 GEO and 4 GSO satellites. The simulation results in STK indicate that the proposed satellite constellation provides accurate position and navigation services to entire Indonesia with the mean GDOP of less than 4 value.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114167684","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315345
Timotius Witono, S. Yazid
One of the vital parts of the Internet topology structure of a country is its national borders with other countries, this is the outermost part of a country's Internet topology. The purpose of this study is to analyze the national borders of Indonesia's Internet topology at the autonomous system (AS) level, in the period 2010 to 2019. The main processes involved in the research include collecting datasets from various sources, mapping the Indonesia's Internet topology borders in graph representation, then analyzing them. The three main components analyzed are Indonesia's AS which acts as a client (AS-ID Border), non Indonesia's AS which acts as a provider (AS Provider), and the customer to provider interconnection (C2P Border-Provider) between the two. The results of the analysis show that the role of an AS-ID Border has grown over time. The analysis also shows that there are four AS-ID Border that are dominant in the number of C2P Border-Provider in the last ten years, namely: AS7713 (ID), AS4761 (ID), AS17451 (ID), and AS23947 (ID). Meanwhile, for AS Provider, United States (US) and Singapore (SG) are the two countries that have AS Provider with the highest number of C2P Border-Provider at the national borders of Indonesia's Internet topology in the last ten years.
{"title":"Analysis of Indonesia's Internet Topology Borders at the Autonomous System Level","authors":"Timotius Witono, S. Yazid","doi":"10.1109/ISRITI51436.2020.9315345","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315345","url":null,"abstract":"One of the vital parts of the Internet topology structure of a country is its national borders with other countries, this is the outermost part of a country's Internet topology. The purpose of this study is to analyze the national borders of Indonesia's Internet topology at the autonomous system (AS) level, in the period 2010 to 2019. The main processes involved in the research include collecting datasets from various sources, mapping the Indonesia's Internet topology borders in graph representation, then analyzing them. The three main components analyzed are Indonesia's AS which acts as a client (AS-ID Border), non Indonesia's AS which acts as a provider (AS Provider), and the customer to provider interconnection (C2P Border-Provider) between the two. The results of the analysis show that the role of an AS-ID Border has grown over time. The analysis also shows that there are four AS-ID Border that are dominant in the number of C2P Border-Provider in the last ten years, namely: AS7713 (ID), AS4761 (ID), AS17451 (ID), and AS23947 (ID). Meanwhile, for AS Provider, United States (US) and Singapore (SG) are the two countries that have AS Provider with the highest number of C2P Border-Provider at the national borders of Indonesia's Internet topology in the last ten years.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114089854","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315452
A.A. Mahersatillah, Z. Zainuddin, Y. Yusran
One of the important things in a self-driving car (SDC), also known as an autonomous vehicle (AV) is detecting the road so that it remains in the right lane. Therefore this paper aims to be able to detect roads, especially unstructured roads based on the results of the HSV color space segmentation on the road, then produce car position information from the center of the lane (center offset) which is a parameter in making the decision to move the car's steering wheel to return to the center of the lane. In marking the edge of the roadside, the method used is Hough transform based on the resulting edge line using an edge detector, then the coordinates of the left and right curb lines which represent the width of the road. The results of this paper indicate that the system's ability to distinguish between road and non-road areas in several sections with an average percentage of 99.59% for accuracy, 99.49% for precision, and 98.84% for recall and the system's ability to mark the left and right edge of the roadside is very good with an average percentage reaches 99.27% and the percentage error and accuracy obtained in providing information on the position of the car from the center (center offset) based on the actual value and prediction results are 16.05% and 84.14%.
{"title":"Unstructured Road Detection and Steering Assist Based on HSV Color Space Segmentation for Autonomous Car","authors":"A.A. Mahersatillah, Z. Zainuddin, Y. Yusran","doi":"10.1109/ISRITI51436.2020.9315452","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315452","url":null,"abstract":"One of the important things in a self-driving car (SDC), also known as an autonomous vehicle (AV) is detecting the road so that it remains in the right lane. Therefore this paper aims to be able to detect roads, especially unstructured roads based on the results of the HSV color space segmentation on the road, then produce car position information from the center of the lane (center offset) which is a parameter in making the decision to move the car's steering wheel to return to the center of the lane. In marking the edge of the roadside, the method used is Hough transform based on the resulting edge line using an edge detector, then the coordinates of the left and right curb lines which represent the width of the road. The results of this paper indicate that the system's ability to distinguish between road and non-road areas in several sections with an average percentage of 99.59% for accuracy, 99.49% for precision, and 98.84% for recall and the system's ability to mark the left and right edge of the roadside is very good with an average percentage reaches 99.27% and the percentage error and accuracy obtained in providing information on the position of the car from the center (center offset) based on the actual value and prediction results are 16.05% and 84.14%.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127527933","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315393
Badr El Khalyly, A. Belangour
Continuous scalability of applications operating in the Internet of Things domain has raised the issue of continuous integration. Users and operators involved in the establishment of connected object ecosystems want to have the ability to change and modify their applications without having to stop the function of the physical components in order to deploy their various applications that run these objects. In this article we propose a continuous integration and scaling approach based on Kubernetes and object virtualization to deploy different types of appropriate microservices.
{"title":"A Kubernetes Algorithm for scaling Virtual Objects","authors":"Badr El Khalyly, A. Belangour","doi":"10.1109/ISRITI51436.2020.9315393","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315393","url":null,"abstract":"Continuous scalability of applications operating in the Internet of Things domain has raised the issue of continuous integration. Users and operators involved in the establishment of connected object ecosystems want to have the ability to change and modify their applications without having to stop the function of the physical components in order to deploy their various applications that run these objects. In this article we propose a continuous integration and scaling approach based on Kubernetes and object virtualization to deploy different types of appropriate microservices.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774530","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 : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315387
K. Indriawati, B. L. Widjiantoro, Nanang Rifa'i Rachman
In this research, speed sensorless control is designed for induction motors by simulation. When the induction motor operates using the speed sensorless technique, the speed sensor is eliminated. Information about speed is obtained from an estimation algorithm based on the voltage and current entering the motor. In this study the estimation algorithm used is a Disturbance Observer. While the control method to control the speed of the induction motor in this study uses the Direct Torque Control (DTC) method. So the purpose in this research is to build a speed sensorless control system for induction motors with a disturbance observer as speed estimator and a DTC control scheme. The speed sensorless control system that has been designed can work well for the speed range of 50 rpm - 300 rpm, while for speeds of 350 rpm or speeds above 300 rpm the system is less able to work properly. The system produces a steady state error response for all variations of the set point in the speed range of 50 rpm - 300 rpm none of which is above 5%.
{"title":"Disturbance Observer-Based Speed Estimator for Controlling Speed Sensorless Induction Motor","authors":"K. Indriawati, B. L. Widjiantoro, Nanang Rifa'i Rachman","doi":"10.1109/ISRITI51436.2020.9315387","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315387","url":null,"abstract":"In this research, speed sensorless control is designed for induction motors by simulation. When the induction motor operates using the speed sensorless technique, the speed sensor is eliminated. Information about speed is obtained from an estimation algorithm based on the voltage and current entering the motor. In this study the estimation algorithm used is a Disturbance Observer. While the control method to control the speed of the induction motor in this study uses the Direct Torque Control (DTC) method. So the purpose in this research is to build a speed sensorless control system for induction motors with a disturbance observer as speed estimator and a DTC control scheme. The speed sensorless control system that has been designed can work well for the speed range of 50 rpm - 300 rpm, while for speeds of 350 rpm or speeds above 300 rpm the system is less able to work properly. The system produces a steady state error response for all variations of the set point in the speed range of 50 rpm - 300 rpm none of which is above 5%.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128127246","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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