Almost all factories now use automated system, where the factory using a control system that can do the work itself and the operator are not too play an active role. With a system that runs automatically is expected to yield a production will increase with the quality of the product that generated the same no difference. Common control system used by the company in the form of PLC (Programmable Logic Controller). The system uses two OMRON CP1H PLC as drivers and integrated with CX-Designer HMI that communicate through one to one NT link and PLC controlled by CX-Programmer through the communication port USB Peripheral and RS-232. Both the input output PLC connected with USB-OPTO-RLY88 which is integrated with visual studio 2017 software using Host Link communication. The result of testing a system that compares the response time between communication port USB Peripheral with RS-232 and parallel ladder diagram with sequential ladder diagram, obtained 400 data of response time when the system was working. The result of data comparison tells that USB Peripheral port has a performance about 15% more efficient compared to the RS-232 port.
现在几乎所有的工厂都使用自动化系统，在自动化系统中，使用可以自己完成工作的控制系统的工厂和操作员都没有发挥积极作用。有了一个自动运行的系统，预计产量将随着产品质量的提高而增加，产生相同的无差异。公司采用PLC（可编程逻辑控制器）形式的通用控制系统。该系统使用两个OMRON CP1H PLC作为驱动器，并与通过一对一NT链路进行通信的CX Designer HMI和由CX Programmer通过通信端口USB外设和RS-232控制的PLC集成。输入输出PLC均与USB-OPTO-RLY88相连，USB-OPTO-RLY88与visual studio 2017软件集成，使用Host Link通信。通过对一个系统的测试，将USB外设与RS-232通信端口之间的响应时间与并行梯形图与时序梯形图进行比较，得到了系统工作时响应时间的400个数据。数据比较的结果告诉USB外设端口的性能比RS-232端口的效率高15%。
{"title":"Implementasi Komunikasi Master – Slave pada PLC OMRON CP1H","authors":"Galuh Purnama Aji, Bakhtiar Alldino Ardi Sumbodo","doi":"10.22146/IJEIS.42950","DOIUrl":"https://doi.org/10.22146/IJEIS.42950","url":null,"abstract":"Almost all factories now use automated system, where the factory using a control system that can do the work itself and the operator are not too play an active role. With a system that runs automatically is expected to yield a production will increase with the quality of the product that generated the same no difference. Common control system used by the company in the form of PLC (Programmable Logic Controller). The system uses two OMRON CP1H PLC as drivers and integrated with CX-Designer HMI that communicate through one to one NT link and PLC controlled by CX-Programmer through the communication port USB Peripheral and RS-232. Both the input output PLC connected with USB-OPTO-RLY88 which is integrated with visual studio 2017 software using Host Link communication. The result of testing a system that compares the response time between communication port USB Peripheral with RS-232 and parallel ladder diagram with sequential ladder diagram, obtained 400 data of response time when the system was working. The result of data comparison tells that USB Peripheral port has a performance about 15% more efficient compared to the RS-232 port.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42545208","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}
A house consists of several rooms with electronic devices inside of it. Each device has a remote control or a button to control it. With technologies have been increasing rapidly, we can control home appliances easily with our Android smartphone. However, thiscan make user uncomfortable to control, if all the devices appear simultaneously in one screen. Therefore,this project aims to develop an automatic configuration remote control system which adapts to the situation of the room to be controlled. Using Bluetooth technology in smartphone for transfering data. Usually to connect to a Bluetooth device,usermanually chooses one of the Bluetooth devicenames from the list that appears from the scan results. Therefore, the focus of this project is to develop a wireless control system that is capable of performing automatic configurationsthat suit the situation of the room. This system usesa localization method that utilizes the signal strength that Bluetooth receives or RSSI Bluetooth.The testing result of the system are able to perform automatic configuration where the system is automatically connected to the nearest room without the need for prior settings and adjust the control menu according to the situation of the room.
{"title":"Sistem Konfigurasi Otomatis Pada Pengendalian Nirkabel Dengan Pendekatan Context-Aware Pada Rumah Pintar","authors":"Dewinta Nila Hapsari, T. Widodo","doi":"10.22146/IJEIS.57051","DOIUrl":"https://doi.org/10.22146/IJEIS.57051","url":null,"abstract":"A house consists of several rooms with electronic devices inside of it. Each device has a remote control or a button to control it. With technologies have been increasing rapidly, we can control home appliances easily with our Android smartphone. However, thiscan make user uncomfortable to control, if all the devices appear simultaneously in one screen. Therefore,this project aims to develop an automatic configuration remote control system which adapts to the situation of the room to be controlled. Using Bluetooth technology in smartphone for transfering data. Usually to connect to a Bluetooth device,usermanually chooses one of the Bluetooth devicenames from the list that appears from the scan results. Therefore, the focus of this project is to develop a wireless control system that is capable of performing automatic configurationsthat suit the situation of the room. This system usesa localization method that utilizes the signal strength that Bluetooth receives or RSSI Bluetooth.The testing result of the system are able to perform automatic configuration where the system is automatically connected to the nearest room without the need for prior settings and adjust the control menu according to the situation of the room.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47925116","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}
Fatimah Fahurian, H. Yunita, Khozainuz Zuhri, Yodhi Yuniarthe
Entering 2020, the corona virus outbreak is spreading very quickly throughout the world, including Indonesia. This encourages the Indonesian government to make efforts and take handling policies to suppress the global spread of the corona virus or Covid-19. These policies did reduce the number of spreads but created new problems, such as increasing criminal acts and coupled with assimilation rights policies (freeing prisoners) during the Covid-19 pandemic which also resulted in an increase in crime rates, such as theft with weighting, motor vehicle theft, theft accompanied by violence, mugging, to beheading. One of the reasons for the increase in theft cases is the use of the situation when everyone is focused on handling Covid-19 and also the existence of security gaps. The security gap can be minimized, one of which is by developing technological innovation, therefore the author proposes a prototype for the development of a dual security system on two-wheeled vehicles with the latest technology. The results of the study, the system can provide notification information through the WhatsApp Messenger application in real time, as well as vehicle owners can control the vehicle remotely using the android application.
{"title":"Prototipe Sistem Keamanan Ganda Pada Kendaraan Roda Dua Berbasis Android dan WhatsApp Messenger","authors":"Fatimah Fahurian, H. Yunita, Khozainuz Zuhri, Yodhi Yuniarthe","doi":"10.22146/IJEIS.69189","DOIUrl":"https://doi.org/10.22146/IJEIS.69189","url":null,"abstract":" Entering 2020, the corona virus outbreak is spreading very quickly throughout the world, including Indonesia. This encourages the Indonesian government to make efforts and take handling policies to suppress the global spread of the corona virus or Covid-19. These policies did reduce the number of spreads but created new problems, such as increasing criminal acts and coupled with assimilation rights policies (freeing prisoners) during the Covid-19 pandemic which also resulted in an increase in crime rates, such as theft with weighting, motor vehicle theft, theft accompanied by violence, mugging, to beheading. One of the reasons for the increase in theft cases is the use of the situation when everyone is focused on handling Covid-19 and also the existence of security gaps. The security gap can be minimized, one of which is by developing technological innovation, therefore the author proposes a prototype for the development of a dual security system on two-wheeled vehicles with the latest technology. The results of the study, the system can provide notification information through the WhatsApp Messenger application in real time, as well as vehicle owners can control the vehicle remotely using the android application.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42063703","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}
Blood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%.
{"title":"Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra","authors":"Lailis Syafaah, Novendra Setyawan, Y. Hidayat","doi":"10.22146/IJEIS.64049","DOIUrl":"https://doi.org/10.22146/IJEIS.64049","url":null,"abstract":" Blood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43546074","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}
Peanut seed tetrazolium test evaluation is usually by eye and a microscope. This method has a weaknesses in the accuracy of reading the color intensity, and is more subjective. The seeds was observed one by one so that the observation is not effective. To make observations more accurate, efficient, and effective, digital image processing can be applied to the seed viability evaluation. The method can be used was the detection of the Hue, Saturation, and Value color area in reading the red color pattern resulting from tetrazolium test.The result is the system can detect a maximum of 25 seeds with an operational time of 22-25 seconds in one detection. Seed classification is the seeds are predicted to normal, abnormal, and dead. The process of classifying seeds is identified based on the red color pattern resulting from the detection of the area of 4 HSV color ranges, namely red (175,100,20:180,255,255), pink (160, 100,20 : 174,150,255), white 1 (175,0,0 : 180,100,255), and white 2 (0,0,0 : 100,255,255). The results show that the accuracy of the system in reading the total number of seeds is 100% with the detection error of HSV color area is 1.54%.
{"title":"Sistem Pendeteksi Viabilitas Benih Kacang Tanah Berdasarkan Luas Area HSV Color","authors":"Haura Fikriyah Hakimah Hakimah, Trisno Yuwono Putro, Sabar Pramono, Eny Widajati","doi":"10.22146/IJEIS.68731","DOIUrl":"https://doi.org/10.22146/IJEIS.68731","url":null,"abstract":"Peanut seed tetrazolium test evaluation is usually by eye and a microscope. This method has a weaknesses in the accuracy of reading the color intensity, and is more subjective. The seeds was observed one by one so that the observation is not effective. To make observations more accurate, efficient, and effective, digital image processing can be applied to the seed viability evaluation. The method can be used was the detection of the Hue, Saturation, and Value color area in reading the red color pattern resulting from tetrazolium test.The result is the system can detect a maximum of 25 seeds with an operational time of 22-25 seconds in one detection. Seed classification is the seeds are predicted to normal, abnormal, and dead. The process of classifying seeds is identified based on the red color pattern resulting from the detection of the area of 4 HSV color ranges, namely red (175,100,20:180,255,255), pink (160, 100,20 : 174,150,255), white 1 (175,0,0 : 180,100,255), and white 2 (0,0,0 : 100,255,255). The results show that the accuracy of the system in reading the total number of seeds is 100% with the detection error of HSV color area is 1.54%.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46944288","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}
Development in image processing is just not focused on the camera sensor and the software. Furthermore, the supporting tools has to be concerned. IP-based cameras are used widely today. Unfortunately, IP Camera has its own limitation. We cannot modificate the system as we want. This paper has introduced a system which has the same function of IP Camera but also can be modificate as we like. The system consists of Odroid XU4 and webcam as a streaming video server. Also, the test result is included in Local network which connected to 31 clients. The result shows that the video has 21,46 fps average on each client. The packet loss is only 1,20%. This means the system works properly and categorized as “very good”.
{"title":"Analisa Karakteristik Single Board Computer sebagai Streaming Video Server","authors":"Muhammad Adlan Hawari, B. Prastowo","doi":"10.22146/IJEIS.53198","DOIUrl":"https://doi.org/10.22146/IJEIS.53198","url":null,"abstract":" Development in image processing is just not focused on the camera sensor and the software. Furthermore, the supporting tools has to be concerned. IP-based cameras are used widely today. Unfortunately, IP Camera has its own limitation. We cannot modificate the system as we want. This paper has introduced a system which has the same function of IP Camera but also can be modificate as we like. The system consists of Odroid XU4 and webcam as a streaming video server. Also, the test result is included in Local network which connected to 31 clients. The result shows that the video has 21,46 fps average on each client. The packet loss is only 1,20%. This means the system works properly and categorized as “very good”.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41904410","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}
Fall cases of elderly people aged 65 or above put their health at risk because it could lead to hip bone fracture, concussion, even death. Immediate help is needed if fall happened which is why an automatic and unobtrusive fall detection system is needed. There are three approaches in fall detection system; wearable, ambience, and vision-based. Wearable approach has the drawback of its obtrusive nature while ambience approach is prone to high false positive value. Vision-based approach is chosen because its unobtrusive nature and low false positive value. This study uses Kinect camera because of its ability on extracting skeletal data. The methods that are used in the fall detection system are AdaBoost method and joint velocity thresholding method. Thresholding method is used as a comparison to AdaBoost method. Both methods use skeletal data from the subject recorded by the Kinect camera. AdaBoost method compares the skeletal data with model that was made before while thresholding method compares the joint velocity value with the threshold value. System test is done using training data, test data, and real-time data. The average accuracy obtained from the system test with AdaBoost method is 91.75% and with thresholding method is 68.22%.
{"title":"Sistem Deteksi Orang Jatuh Dengan Menggunakan Sensor Kamera Kinect Dengan Metode AdaBoost","authors":"Satria Perwira, M. I. A. Timur, Agus Harjoko","doi":"10.22146/IJEIS.49974","DOIUrl":"https://doi.org/10.22146/IJEIS.49974","url":null,"abstract":"Fall cases of elderly people aged 65 or above put their health at risk because it could lead to hip bone fracture, concussion, even death. Immediate help is needed if fall happened which is why an automatic and unobtrusive fall detection system is needed. There are three approaches in fall detection system; wearable, ambience, and vision-based. Wearable approach has the drawback of its obtrusive nature while ambience approach is prone to high false positive value. Vision-based approach is chosen because its unobtrusive nature and low false positive value. This study uses Kinect camera because of its ability on extracting skeletal data. The methods that are used in the fall detection system are AdaBoost method and joint velocity thresholding method. Thresholding method is used as a comparison to AdaBoost method. Both methods use skeletal data from the subject recorded by the Kinect camera. AdaBoost method compares the skeletal data with model that was made before while thresholding method compares the joint velocity value with the threshold value. System test is done using training data, test data, and real-time data. The average accuracy obtained from the system test with AdaBoost method is 91.75% and with thresholding method is 68.22%.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":"15 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41311826","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}
JK Aditya Christya Buditama, Catur Atmaji, A. E. Putra
Javanese is an Indonesian culture which needs to be preserved, but many Javanese students make mistakes in the pronunciation of Javanese letters and find it difficult to analyze errors by human teachers because of the limited time and subjective assessment, so a system is needed to detect incorrect pronunciation of Javanese letters. Mispronunciation detection system has been widely applied in foreign languages, but the system has not been implemented for Javanese carakan letters. This research develops the Javanese letters mispronunciation detection system using Back-Propagation Artificial Neural Networks (BP-ANN). The dataset is obtained from the recorded pronunciation of hanacaraka texts by 24 speakers with 5 repetitions. ALNS method then used to automatically segment the signal into syllables. ANN-PB use statistical value of Mel-Frequency Cepstral Coefficient (MFCC) method with 7 and 14 coefficients. 10-Fold Cross Validation is used to validate and test the system. The Javanese mispronunciation detection using 7MFCC coefficients produces the highest accuracy of 80,07%. While the Javanese mispronunciation detection using 14 MFCC coefficients produces an accuracy of 82.36% at the highest.
{"title":"Deteksi Kesalahan Pengucapan Huruf Jawa Carakan dengan Jaringan Syaraf Tiruan Perambatan Balik","authors":"JK Aditya Christya Buditama, Catur Atmaji, A. E. Putra","doi":"10.22146/IJEIS.53437","DOIUrl":"https://doi.org/10.22146/IJEIS.53437","url":null,"abstract":"Javanese is an Indonesian culture which needs to be preserved, but many Javanese students make mistakes in the pronunciation of Javanese letters and find it difficult to analyze errors by human teachers because of the limited time and subjective assessment, so a system is needed to detect incorrect pronunciation of Javanese letters. Mispronunciation detection system has been widely applied in foreign languages, but the system has not been implemented for Javanese carakan letters. This research develops the Javanese letters mispronunciation detection system using Back-Propagation Artificial Neural Networks (BP-ANN). The dataset is obtained from the recorded pronunciation of hanacaraka texts by 24 speakers with 5 repetitions. ALNS method then used to automatically segment the signal into syllables. ANN-PB use statistical value of Mel-Frequency Cepstral Coefficient (MFCC) method with 7 and 14 coefficients. 10-Fold Cross Validation is used to validate and test the system. The Javanese mispronunciation detection using 7MFCC coefficients produces the highest accuracy of 80,07%. While the Javanese mispronunciation detection using 14 MFCC coefficients produces an accuracy of 82.36% at the highest.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48588592","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}
Bulk cooking oil is a food ingredient that is widely used by the public. The seller uses used bulk cooking oil to be mixed with new bulk cooking oil or even mixes it with harmful ingredients. Lab tests are needed to determine the quality of cooking oil, but the lab test requires a long time and can also damage the content of the cooking oil so it cannot be reused. The focus of this research is the creation of a system that can detect the purity of cooking oil by utilizing the wave velocity measurement method without damaging the shape and nature of the cooking oil.Wave velocity measurements are carried out by propagating ultrasonic waves on objects with a wave frequency of 40 kHz. The value of the duration of the wave propagation time at a distance of 19.4 cm is sampled and used for the calculation of wave velocity. The results of these wave velocity calculations are used to determine the purity level of cooking oil. Then the results of the purity level obtained were analyzed with an approach to the value of fluid viscosity. The results of the wave velocity values show that the waves propagate faster if the purity level of cooking oil is higher and produces a positive correlation with R2=0.9784. The results of the analysis conducted with the approach to fluid viscosity also showed a positive correlation with R2= 0.9999. The average wave velocity in pure bulk cooking oil is 1174.90 m/s.
{"title":"Sistem Deteksi Kemurnian Minyak Goreng Dengan Menggunakan Metode Gelombang Ultrasonik","authors":"Asriawan Pasca Ramadhan, A. Rouf","doi":"10.22146/IJEIS.53096","DOIUrl":"https://doi.org/10.22146/IJEIS.53096","url":null,"abstract":"Bulk cooking oil is a food ingredient that is widely used by the public. The seller uses used bulk cooking oil to be mixed with new bulk cooking oil or even mixes it with harmful ingredients. Lab tests are needed to determine the quality of cooking oil, but the lab test requires a long time and can also damage the content of the cooking oil so it cannot be reused. The focus of this research is the creation of a system that can detect the purity of cooking oil by utilizing the wave velocity measurement method without damaging the shape and nature of the cooking oil.Wave velocity measurements are carried out by propagating ultrasonic waves on objects with a wave frequency of 40 kHz. The value of the duration of the wave propagation time at a distance of 19.4 cm is sampled and used for the calculation of wave velocity. The results of these wave velocity calculations are used to determine the purity level of cooking oil. Then the results of the purity level obtained were analyzed with an approach to the value of fluid viscosity. The results of the wave velocity values show that the waves propagate faster if the purity level of cooking oil is higher and produces a positive correlation with R2=0.9784. The results of the analysis conducted with the approach to fluid viscosity also showed a positive correlation with R2= 0.9999. The average wave velocity in pure bulk cooking oil is 1174.90 m/s.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48739507","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 rapid advancement of technology following the number of potholes on the streets that need to be inspected have led people to develop technology that can inspect pothole using a detection system. Digital image processing is a method used by some people to detect potholes by using its colour as the main extracted feature, after that the field of machine learning and deep learning approaches have been studied and developed in terms of detection, one of which is the ssd-mobilenet. In this study three types of dataset were used, they were obtained secondarily from various sources, namely the normal dataset, the dashboard dataset, and the closeup dataset. These three datasets will also be combined and varied in the amount of the training data with an increment of 500 data train so that various model results are obtained. The results obtained are the detection bounding boxes and also the confusion matrix score of each model dataset, where the normal dataset gets an accuracy score of 56%, the dashboard dataset gets 50% and the closeup dataset gets 76%.
{"title":"Pendeteksian Lubang Pada Jalanan Menggunakan Metode SSD-MobileNet","authors":"Ivan Besando Pakpahan, Ika Candra Dewi","doi":"10.22146/IJEIS.60157","DOIUrl":"https://doi.org/10.22146/IJEIS.60157","url":null,"abstract":"The rapid advancement of technology following the number of potholes on the streets that need to be inspected have led people to develop technology that can inspect pothole using a detection system. Digital image processing is a method used by some people to detect potholes by using its colour as the main extracted feature, after that the field of machine learning and deep learning approaches have been studied and developed in terms of detection, one of which is the ssd-mobilenet. In this study three types of dataset were used, they were obtained secondarily from various sources, namely the normal dataset, the dashboard dataset, and the closeup dataset. These three datasets will also be combined and varied in the amount of the training data with an increment of 500 data train so that various model results are obtained. The results obtained are the detection bounding boxes and also the confusion matrix score of each model dataset, where the normal dataset gets an accuracy score of 56%, the dashboard dataset gets 50% and the closeup dataset gets 76%.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46278526","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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