Pub Date : 2022-07-30DOI: 10.17529/jre.v18i2.25694
S. Hadiyoso, S. Aulia
Human nail disease is usually ignored since it does not reveal clinical signs that are harmful to one's health. Nail disease, on the other hand, can be an early sign of a health issue. Some types of nail disease can cause infection, injury, or even the loss of the nail itself. It can reduce a person's aesthetics and beauty. Nail disease is very varied, so it is often difficult for clinicians to diagnose because several types have high similarities. Therefore, an automatic nail disease classification method based on nail photos was proposed in this study. The proposed method was based on the VGG-16 neural network architecture with an Adam optimizer. Nail diseases including Koilonychia, Beaus Lines, Leukonychia have been classified in this study. The model in this study is simulated in Python programming. The simulation results show that the highest classification accuracy is 96%, achieved with epoch-10. The transfer learning method based on a neural network simulated in this study is expected to support the clinical diagnosis of nail disease.
{"title":"Classification of Koilonychia, Beaus Lines, and Leukonychia based on Nail Image using Transfer Learning VGG-16","authors":"S. Hadiyoso, S. Aulia","doi":"10.17529/jre.v18i2.25694","DOIUrl":"https://doi.org/10.17529/jre.v18i2.25694","url":null,"abstract":"Human nail disease is usually ignored since it does not reveal clinical signs that are harmful to one's health. Nail disease, on the other hand, can be an early sign of a health issue. Some types of nail disease can cause infection, injury, or even the loss of the nail itself. It can reduce a person's aesthetics and beauty. Nail disease is very varied, so it is often difficult for clinicians to diagnose because several types have high similarities. Therefore, an automatic nail disease classification method based on nail photos was proposed in this study. The proposed method was based on the VGG-16 neural network architecture with an Adam optimizer. Nail diseases including Koilonychia, Beaus Lines, Leukonychia have been classified in this study. The model in this study is simulated in Python programming. The simulation results show that the highest classification accuracy is 96%, achieved with epoch-10. The transfer learning method based on a neural network simulated in this study is expected to support the clinical diagnosis of nail disease.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41979863","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 : 2022-07-30DOI: 10.17529/jre.v18i2.25535
Elieser Tarigan
—Solar energy is a renewable energy source that can be used as a source of electricity using photovoltaic (PV) system to reduce our dependence on fossil energy. This paper discusses an overview of the use of a rooftop PV system in accordance with applicable regulations in Indonesia. Computer simulation was conducted to determine the potential power and output energy of the the rooftop PV system in the city of Surabaya. The simulation was carried out by SolarGIS Pvplanner software. Mathematical equations are derived to estimate the unit price of electric energy for the PV system, and the calculations are done numerically. The simulation results show that the total daily energy average generated from the 3 kWP roof solar PV system in Surabaya is about 13 kWh. Meanwhile, the unit price for PV system electricity is obtained between 0.08 USD - 0.11 USD / kWh.
{"title":"Simulasi Sistem PLTS Atap dan Harga Satuan Energi Listrik Untuk Skala Rumah Tangga di Surabaya","authors":"Elieser Tarigan","doi":"10.17529/jre.v18i2.25535","DOIUrl":"https://doi.org/10.17529/jre.v18i2.25535","url":null,"abstract":"—Solar energy is a renewable energy source that can be used as a source of electricity using photovoltaic (PV) system to reduce our dependence on fossil energy. This paper discusses an overview of the use of a rooftop PV system in accordance with applicable regulations in Indonesia. Computer simulation was conducted to determine the potential power and output energy of the the rooftop PV system in the city of Surabaya. The simulation was carried out by SolarGIS Pvplanner software. Mathematical equations are derived to estimate the unit price of electric energy for the PV system, and the calculations are done numerically. The simulation results show that the total daily energy average generated from the 3 kWP roof solar PV system in Surabaya is about 13 kWh. Meanwhile, the unit price for PV system electricity is obtained between 0.08 USD - 0.11 USD / kWh.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42951679","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 : 2022-07-30DOI: 10.17529/jre.v18i2.25863
F. Setiawan, Yosia Yovie Christian Wibowo, L. H. Pratomo, Slamet Riyadi
—The influence of the industrial revolution 4.0 resulted in very significant changes. Many companies compete to produce robots that facilitate human work, in terms of energy and time in the process of producing goods. One of the robots being developed is the Automated Guided Vehicle (AGV), a vehicle with automatic control. AGV has high accuracy, easy maintenance, and a long operating time. This study discusses the design and implementation of AGV using 2 motors. The front motor using a servo motor is used for steering to turn right and turn left, while the rear motor in the form of a DC motor is used to regulate the speed of the AGV. The AGV movement system is controlled by computer vision. The AGV problem encountered is that the camera reading distance is close, which makes it less efficient in industrial use. This problem can be solved with a camera connected to a raspberry pi capable of capturing text and images from a distance of 100 cm. The use of computer vision makes the AGV robot easy to move. In this study, the accuracy of the movement of the AGV robot to the trajectory pattern has an average angle difference of 3.09°. The difference in the angle indicates a small error so that the AGV can operate optimally. Infield applications, this AGV is used in the manufacturing industry to move goods. Therefore, the use of AGV is needed because it has high accuracy and small error.
{"title":"Perancangan Automated Guided Vehicle Menggunakan Penggerak Motor DC dan Motor Servo Berbasis Raspberry Pi 4","authors":"F. Setiawan, Yosia Yovie Christian Wibowo, L. H. Pratomo, Slamet Riyadi","doi":"10.17529/jre.v18i2.25863","DOIUrl":"https://doi.org/10.17529/jre.v18i2.25863","url":null,"abstract":"—The influence of the industrial revolution 4.0 resulted in very significant changes. Many companies compete to produce robots that facilitate human work, in terms of energy and time in the process of producing goods. One of the robots being developed is the Automated Guided Vehicle (AGV), a vehicle with automatic control. AGV has high accuracy, easy maintenance, and a long operating time. This study discusses the design and implementation of AGV using 2 motors. The front motor using a servo motor is used for steering to turn right and turn left, while the rear motor in the form of a DC motor is used to regulate the speed of the AGV. The AGV movement system is controlled by computer vision. The AGV problem encountered is that the camera reading distance is close, which makes it less efficient in industrial use. This problem can be solved with a camera connected to a raspberry pi capable of capturing text and images from a distance of 100 cm. The use of computer vision makes the AGV robot easy to move. In this study, the accuracy of the movement of the AGV robot to the trajectory pattern has an average angle difference of 3.09°. The difference in the angle indicates a small error so that the AGV can operate optimally. Infield applications, this AGV is used in the manufacturing industry to move goods. Therefore, the use of AGV is needed because it has high accuracy and small error.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44270548","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 : 2022-07-30DOI: 10.17529/jre.v18i2.25244
Adhi Kusuma Negara, H. Manik, S. Susilohadi
{"title":"Rancang Bangun Driver PZT dan Filtering Data Akustik Pada Sonar Aktif","authors":"Adhi Kusuma Negara, H. Manik, S. Susilohadi","doi":"10.17529/jre.v18i2.25244","DOIUrl":"https://doi.org/10.17529/jre.v18i2.25244","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42372396","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}
Abstrak —Sistem deteksi objek bergerak telah banyak digunakan dalam kehidupan sehari-hari. Saat ini penelitian dibidang subtraksi latar masih terus dilakukan untuk mencapai hasil akurasi yang maksimal. Penelitian ini bertujuan untuk memodelkan substraksi latar dari sebuah citra menggunakan nilai mean dengan konsep non overlapping block . Selanjutnya, hasil substraksi latar akan digunakan dalam deteksi objek bergerak berbasis deep learning . Secara spesifik, citra masukan akan dibagi menjadi beberapa blok, kemudian nilai mean dari setiap blok akan dihitung untuk nantinya menghasilkan blok biner ( binary map ). Blok biner yang telah dihasilkan akan dijadikan sebagai masukan pembangkitan model latar ( background modelling ). Model latar bertujuan untuk memisahkan objek bergerak dengan latar yang ada pada citra masukan. Objek bergerak yang dihasilkan (lokalisasi objek) akan dikirimkan ke tahap klasifikasi objek menggunakan deep learning . Dataset yang digunakan dalam penelitian ini adalah CDNet 2014. Hasil penelitian mampu menghasilkan sistem deteksi objek Abstract — Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.
{"title":"Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning","authors":"Ilal Mahdi, Kahlil Muchtar, Fitri Arnia, Tiara Ernita","doi":"10.17529/jre.v18i2.25224","DOIUrl":"https://doi.org/10.17529/jre.v18i2.25224","url":null,"abstract":"Abstrak —Sistem deteksi objek bergerak telah banyak digunakan dalam kehidupan sehari-hari. Saat ini penelitian dibidang subtraksi latar masih terus dilakukan untuk mencapai hasil akurasi yang maksimal. Penelitian ini bertujuan untuk memodelkan substraksi latar dari sebuah citra menggunakan nilai mean dengan konsep non overlapping block . Selanjutnya, hasil substraksi latar akan digunakan dalam deteksi objek bergerak berbasis deep learning . Secara spesifik, citra masukan akan dibagi menjadi beberapa blok, kemudian nilai mean dari setiap blok akan dihitung untuk nantinya menghasilkan blok biner ( binary map ). Blok biner yang telah dihasilkan akan dijadikan sebagai masukan pembangkitan model latar ( background modelling ). Model latar bertujuan untuk memisahkan objek bergerak dengan latar yang ada pada citra masukan. Objek bergerak yang dihasilkan (lokalisasi objek) akan dikirimkan ke tahap klasifikasi objek menggunakan deep learning . Dataset yang digunakan dalam penelitian ini adalah CDNet 2014. Hasil penelitian mampu menghasilkan sistem deteksi objek Abstract — Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47271951","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 : 2022-07-30DOI: 10.17529/jre.v18i2.25758
E. Dewi, Gema Ramadhan, Robinsar Parlindungan, Lenny Iryani, Trisno Yuwono
Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.
{"title":"Measurement of Ankle Brachial Index with Oscillometric Method for Early Detection of Peripheral Artery Disease","authors":"E. Dewi, Gema Ramadhan, Robinsar Parlindungan, Lenny Iryani, Trisno Yuwono","doi":"10.17529/jre.v18i2.25758","DOIUrl":"https://doi.org/10.17529/jre.v18i2.25758","url":null,"abstract":"Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49641476","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 : 2022-07-30DOI: 10.17529/jre.v18i2.24047
Junta Zeniarja, Abu Salam, Farda Alan Ma'ruf
— Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.
{"title":"Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa","authors":"Junta Zeniarja, Abu Salam, Farda Alan Ma'ruf","doi":"10.17529/jre.v18i2.24047","DOIUrl":"https://doi.org/10.17529/jre.v18i2.24047","url":null,"abstract":"— Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48390323","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 : 2022-04-19DOI: 10.17529/jre.v18i1.24919
Ahmad Fauzi Firmansyah, A. Gunawan, I. Sulistijono, Denny Hanurawan
—Density is a measure of the mass of each unit volume of an object; the higher the density of an object, the greater the mass of each volume. The density value can be used to distinguish the characteristics of lubricating oils that are prone to contamination with solid or liquid particles. The density value is also affected by changes in temperature; the higher the temperature of the lubricating oil, the smaller the density value. The regulations in force in Indonesia with the ASTM D1298-12b standard density test method state that the measurement uses a temperature of 15℃. In this study, the density measurement value was obtained at a temperature of 28℃ so it required a value conversion using the ASTM 53B table about the density correction factor. The technique of testing the material without damaging the test object using an ultrasonic sensor is used to measure the density value of motorcycle lubricating oil. Measurements are made by transmitting a 3 MHz ultrasonic trigger signal that can penetrate each medium with different characteristics. The received echo signal produces information about the distance between the medium, the speed of sound, and the acoustic impedance. The results of the measurement of 11 samples of motorcycle lubricating oil both in new and used conditions using the acoustic impedance method resulted in an accuracy of 93,6% or 0,058 kg/dm 3 when compared to the value measured using a pycnometer. The MPX-2-C sample measurement showed the lowest error of 0,41% or 0,004 kg/dm 3 .
{"title":"Pengukuran Nilai Densitas pada Minyak Pelumas Sepeda Motor dengan Gelombang Ultrasonik","authors":"Ahmad Fauzi Firmansyah, A. Gunawan, I. Sulistijono, Denny Hanurawan","doi":"10.17529/jre.v18i1.24919","DOIUrl":"https://doi.org/10.17529/jre.v18i1.24919","url":null,"abstract":"—Density is a measure of the mass of each unit volume of an object; the higher the density of an object, the greater the mass of each volume. The density value can be used to distinguish the characteristics of lubricating oils that are prone to contamination with solid or liquid particles. The density value is also affected by changes in temperature; the higher the temperature of the lubricating oil, the smaller the density value. The regulations in force in Indonesia with the ASTM D1298-12b standard density test method state that the measurement uses a temperature of 15℃. In this study, the density measurement value was obtained at a temperature of 28℃ so it required a value conversion using the ASTM 53B table about the density correction factor. The technique of testing the material without damaging the test object using an ultrasonic sensor is used to measure the density value of motorcycle lubricating oil. Measurements are made by transmitting a 3 MHz ultrasonic trigger signal that can penetrate each medium with different characteristics. The received echo signal produces information about the distance between the medium, the speed of sound, and the acoustic impedance. The results of the measurement of 11 samples of motorcycle lubricating oil both in new and used conditions using the acoustic impedance method resulted in an accuracy of 93,6% or 0,058 kg/dm 3 when compared to the value measured using a pycnometer. The MPX-2-C sample measurement showed the lowest error of 0,41% or 0,004 kg/dm 3 .","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44170148","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 : 2022-04-19DOI: 10.17529/jre.v18i1.22224
Ahmad Fauji, Arief Goeritno, Lucky Hardian, Bayu Arief Prakoso
—This research was motivated by a number of shortcomings in previous similar studies, mainly related to the selection of sensors, the selection of application for operation, and the absence of backup power in the system, so that manufacturing and development were carried out for the acquisition of an embedded device as a control unit. The availability of this control unit is part of the smarthome system based on the Internet of Things (IoT) for gateway controllers, via smartphones with a one-time password mechanism. The research objectives include (i) the manufacture of control units and programming based on Arduino IDE and (ii) verification and validation tests. The realization of the control unit is carried out through assembling a number of electronic devices, making motherboards, re-functionalizing of the miniature gates, and integrated wiring equipped with embedded programs. The performance of the control unit is measured by providing verification tests in the form of simulations based on the Proteus application and validation tests assisted by the Telegram Bot application when conditions are given to the gate when it is opened, closed, or the lock is in a lock/unlocked state. The performance of the control unit developed, in the form of increasing the speed of the gate opening and closing process, implementing one-time passwords for operating security, and the availability of internal backup power. Recommendations for further research, more emphasis is placed on the creation of various control units that are integrated into the smarthome system platform.
{"title":"Embedded Device pada Smarthome System Berbasis IoT untuk Pengoperasian Pintu Gerbang Terkendali melalui Smartphone","authors":"Ahmad Fauji, Arief Goeritno, Lucky Hardian, Bayu Arief Prakoso","doi":"10.17529/jre.v18i1.22224","DOIUrl":"https://doi.org/10.17529/jre.v18i1.22224","url":null,"abstract":"—This research was motivated by a number of shortcomings in previous similar studies, mainly related to the selection of sensors, the selection of application for operation, and the absence of backup power in the system, so that manufacturing and development were carried out for the acquisition of an embedded device as a control unit. The availability of this control unit is part of the smarthome system based on the Internet of Things (IoT) for gateway controllers, via smartphones with a one-time password mechanism. The research objectives include (i) the manufacture of control units and programming based on Arduino IDE and (ii) verification and validation tests. The realization of the control unit is carried out through assembling a number of electronic devices, making motherboards, re-functionalizing of the miniature gates, and integrated wiring equipped with embedded programs. The performance of the control unit is measured by providing verification tests in the form of simulations based on the Proteus application and validation tests assisted by the Telegram Bot application when conditions are given to the gate when it is opened, closed, or the lock is in a lock/unlocked state. The performance of the control unit developed, in the form of increasing the speed of the gate opening and closing process, implementing one-time passwords for operating security, and the availability of internal backup power. Recommendations for further research, more emphasis is placed on the creation of various control units that are integrated into the smarthome system platform.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46098076","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 : 2022-04-19DOI: 10.17529/jre.v18i1.23255
Farrel Fahrozi, S. Hadiyoso, Y. S. Hariyani
Breast cancer is one of the non-contagious diseases that tends to increase every year. This disease occurs almost entirely in women, but can also occur in men. One way to detect this disease is by observing mammography images. However, mammography images often tend to be blurry with low quality so that it is possible to detect them incorrectly. Therefore, in this study, automatic classification of breast cancer on mammographic images was carried out using the Convolutional Neural Network (CNN). This proposed system uses the VGG16 architecture with a transfer learning system. The proposed system is then optimized using Adam optimizers and RMSprop optimizers. The results of system testing for normal, benign, and malignant classifications obtained an accuracy value of 80% - 90% with the highest accuracy achieved using Adam's optimizers. With this proposed system, it is hoped that it can help in the clinical diagnosis of breast cancer.
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