Pub Date : 2023-03-27DOI: 10.17529/jre.v19i1.28330
Alif Wicaksana Ramadhan, Bima Sena Bayu Dewantara, Setiawardhana Setiawardhana
The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.
{"title":"Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control","authors":"Alif Wicaksana Ramadhan, Bima Sena Bayu Dewantara, Setiawardhana Setiawardhana","doi":"10.17529/jre.v19i1.28330","DOIUrl":"https://doi.org/10.17529/jre.v19i1.28330","url":null,"abstract":"The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46000191","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 : 2023-03-27DOI: 10.17529/jre.v19i1.29111
Nur Wisma Nugraha, Suharayadi Pancono, G. Maulana
Quality products are very influential in creating profits for the company and are also closely related to the level of customer satisfaction. The higher the quality of the products produced by a company, the higher the satisfaction felt by consumers. The biggest challenge in the production process is achieving good quality with a product defect rate close to zero defect. Defects in the product are usually small. This is of course very difficult for workers to inspect each product for a long time. Thus, manual inspection is certainly ineffective and inefficient because humans have a saturation point and get tired if they work for a long time. Previous research on detecting defective objects using image processing has been carried out but has not been able to detect up to the shape and size, while in this study it can detect up to the shape and size. Therefore, to implement an automatic product defect detection system we will use image processing and RFID technology. Image processing is processing on the image using a computer so that the image quality becomes better and produces value information for each color. Image processing techniques consist of image conversion from RGB to grayscale, thresholding (binarization), and morphological operations (segmentation). While RFID is an identification method by using a means called an RFID label or transponder to store and retrieve data remotely This study aims to implement a control system on HMI and also a detection system on defect products using a visual inspection system with the aim of getting the machine effectiveness value. One method to get this value is the Overall Equipment Effectiveness (OEE) method. It is proven by implementing a visual inspection system that gets an accuracy rate of 95.97% to detect rejected products and optimize the OEE presentation value obtained. In this study, the implementation of the production monitoring system was successfully implemented with an average OEE value of 52.49%.
{"title":"Defect Detection System on Stamping Machine Using the Image Processing Method","authors":"Nur Wisma Nugraha, Suharayadi Pancono, G. Maulana","doi":"10.17529/jre.v19i1.29111","DOIUrl":"https://doi.org/10.17529/jre.v19i1.29111","url":null,"abstract":"Quality products are very influential in creating profits for the company and are also closely related to the level of customer satisfaction. The higher the quality of the products produced by a company, the higher the satisfaction felt by consumers. The biggest challenge in the production process is achieving good quality with a product defect rate close to zero defect. Defects in the product are usually small. This is of course very difficult for workers to inspect each product for a long time. Thus, manual inspection is certainly ineffective and inefficient because humans have a saturation point and get tired if they work for a long time. Previous research on detecting defective objects using image processing has been carried out but has not been able to detect up to the shape and size, while in this study it can detect up to the shape and size. Therefore, to implement an automatic product defect detection system we will use image processing and RFID technology. Image processing is processing on the image using a computer so that the image quality becomes better and produces value information for each color. Image processing techniques consist of image conversion from RGB to grayscale, thresholding (binarization), and morphological operations (segmentation). While RFID is an identification method by using a means called an RFID label or transponder to store and retrieve data remotely This study aims to implement a control system on HMI and also a detection system on defect products using a visual inspection system with the aim of getting the machine effectiveness value. One method to get this value is the Overall Equipment Effectiveness (OEE) method. It is proven by implementing a visual inspection system that gets an accuracy rate of 95.97% to detect rejected products and optimize the OEE presentation value obtained. In this study, the implementation of the production monitoring system was successfully implemented with an average OEE value of 52.49%. ","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46363509","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 : 2023-03-27DOI: 10.17529/jre.v19i1.27120
D. O. Anggriawan, E. Wahjono, I. Sudiharto, Anang Budikarso
This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %
{"title":"Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network","authors":"D. O. Anggriawan, E. Wahjono, I. Sudiharto, Anang Budikarso","doi":"10.17529/jre.v19i1.27120","DOIUrl":"https://doi.org/10.17529/jre.v19i1.27120","url":null,"abstract":"This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44411814","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 measurement system is a process that consists of standards, employees, and methods for measuring particular quality characteristics. Measurement System Analysis (MSA) attempts to evaluate a measuring system's precision, accuracy, and consistency so that clients receive high-quality goods. The previous study implements the MSA for machinery and industrial lines, electronics manufacturing, agricultural and poultry, aviation, and even employee monitoring and inspection. Elsewhere, waste management has problems, especially with capacity measurement instruments and weight sensors. This study aims to: (i) build an IoT-based waste management system; and (ii) evaluate the developed system by implementing the MSA technique, focusing on measurement equipment. The Gauge Repeatability and Reproducibility (GRR) Study Type 1, the (GRR) Study, and the Analysis of Variance (ANOVA) are conducted to evaluate the measurement instrument of the waste management system. The study findings that the total variance of the GRR is 20.95 %, and the distinct categories are 6. Thus, as the Automotive Industry Action Group (AIAG) GRR recommendation, the measuring system is marginal (acceptable in certain conditions). Moreover, the ANOVA result indicates that interaction and operators did not affect measurement outcomes because the blue dots remain inside the acceptable range.
{"title":"An Implementation of Measurement System Analysis for IoT-Based Waste Management Development","authors":"Heru Wijanarko, Arianysah Saputra, Ika Karlina Laila Nur Suciningtyas, Rifqi Amalya Fatekha","doi":"10.17529/jre.v18i4.26910","DOIUrl":"https://doi.org/10.17529/jre.v18i4.26910","url":null,"abstract":"A measurement system is a process that consists of standards, employees, and methods for measuring particular quality characteristics. Measurement System Analysis (MSA) attempts to evaluate a measuring system's precision, accuracy, and consistency so that clients receive high-quality goods. The previous study implements the MSA for machinery and industrial lines, electronics manufacturing, agricultural and poultry, aviation, and even employee monitoring and inspection. Elsewhere, waste management has problems, especially with capacity measurement instruments and weight sensors. This study aims to: (i) build an IoT-based waste management system; and (ii) evaluate the developed system by implementing the MSA technique, focusing on measurement equipment. The Gauge Repeatability and Reproducibility (GRR) Study Type 1, the (GRR) Study, and the Analysis of Variance (ANOVA) are conducted to evaluate the measurement instrument of the waste management system. The study findings that the total variance of the GRR is 20.95 %, and the distinct categories are 6. Thus, as the Automotive Industry Action Group (AIAG) GRR recommendation, the measuring system is marginal (acceptable in certain conditions). Moreover, the ANOVA result indicates that interaction and operators did not affect measurement outcomes because the blue dots remain inside the acceptable range.","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47528935","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 : 2022-12-01DOI: 10.17529/jre.v18i4.27240
B. Iman, Raay Rafikasitha, Kemalasari Kemalasari
{"title":"Perancangan dan Implementasi Alat Pendeteksi Dini Penyakit Jantung Koroner","authors":"B. Iman, Raay Rafikasitha, Kemalasari Kemalasari","doi":"10.17529/jre.v18i4.27240","DOIUrl":"https://doi.org/10.17529/jre.v18i4.27240","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41940678","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 : 2022-12-01DOI: 10.17529/jre.v18i4.26981
Unang Sunarya, Tita Haryanti
{"title":"Perbandingan Kinerja Algoritma Optimasi pada Metode Random Forest untuk Deteksi Kegagalan Jantung","authors":"Unang Sunarya, Tita Haryanti","doi":"10.17529/jre.v18i4.26981","DOIUrl":"https://doi.org/10.17529/jre.v18i4.26981","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49171432","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 : 2022-12-01DOI: 10.17529/jre.v18i4.27495
F. Setiawan, Padang Ufqi Sutrisno, L. H. Pratomo, Slamet Riyadi
{"title":"Penerapan Algoritma HSV pada Autonomous Car untuk Sistem Self-Driving Berbasis Raspberry Pi 4","authors":"F. Setiawan, Padang Ufqi Sutrisno, L. H. Pratomo, Slamet Riyadi","doi":"10.17529/jre.v18i4.27495","DOIUrl":"https://doi.org/10.17529/jre.v18i4.27495","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43172068","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 : 2022-12-01DOI: 10.17529/jre.v18i4.27238
Kemalasari Kemalasari, Maulida Alvisabrina Ifadah, B. Iman
{"title":"Alat Pendeteksi Kadar Glukosa pada Urine dengan Metode Naive Bayes","authors":"Kemalasari Kemalasari, Maulida Alvisabrina Ifadah, B. Iman","doi":"10.17529/jre.v18i4.27238","DOIUrl":"https://doi.org/10.17529/jre.v18i4.27238","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45879329","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 : 2022-12-01DOI: 10.17529/jre.v18i4.23329
Rudy Gianto
{"title":"Pemodelan Pembangkit Listrik Tenaga Angin yang Berbasis DFIG untuk Analisis Aliran Daya","authors":"Rudy Gianto","doi":"10.17529/jre.v18i4.23329","DOIUrl":"https://doi.org/10.17529/jre.v18i4.23329","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42991377","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 : 2022-12-01DOI: 10.17529/jre.v18i4.27754
Gilang Bonie Wiryawan, Kun Fayakun, Harry Ramza, M. A. Zakariya, Emilia Roza, Dwi Astuti Cahyasiwi
{"title":"Antena-Filter Hairpin dengan Peningkatan Perolehan untuk Aplikasi 5G","authors":"Gilang Bonie Wiryawan, Kun Fayakun, Harry Ramza, M. A. Zakariya, Emilia Roza, Dwi Astuti Cahyasiwi","doi":"10.17529/jre.v18i4.27754","DOIUrl":"https://doi.org/10.17529/jre.v18i4.27754","url":null,"abstract":"","PeriodicalId":30766,"journal":{"name":"Jurnal Rekayasa Elektrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45729048","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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