Pub Date : 2024-03-31DOI: 10.25077/jnte.v13n1.1180.2024
Lukman Subekti, Candra Febri Nugraha, Muhammad Arrofiq, Ahmad Adhiim Muthahhari, Budi Eko Prasetyo, Qurrota A’yun
Indonesia, an expansive archipelagic nation with over 17,000 islands, encounters significant challenges in ensuring a sustainable and dependable electricity supply, particularly in its West Papua region. The reliance on diesel fuel for electricity generation in this area poses substantial environmental risks and incurs high costs. A comprehensive research study addressing the environmental and economic challenges associated with diesel dependence in West Papua proposed a shift towards sustainable and cost-effective solutions by advocating for adopting off-grid hybrid power systems. This study targeted Yensawai Village in the Raja Ampat Islands, employing a detailed techno-economic analysis through HOMER Pro to identify the most cost-effective system configurations. The findings indicated that the optimal setup consists of a 160 kW diesel generator, complemented by a 70.1 kW solar photovoltaic (PV) system, a 30 kW inverter, and an 80 kWh battery storage unit. This configuration not only proved to be economically viable by reducing the levelized cost of electricity (CoE) by 15.7%—achieving a CoE of $0.236/kWh compared to the base scenario's $0.280/kWh—but also highlighted the potential for similar benefits across regional systems. By focusing on the economic advantages of hybrid energy configurations, this research contributes significantly to the broader discourse on sustainability and the urgent need to reduce diesel dependence, offering a practical approach to cutting electricity generation costs in remote island communities and advancing sustainability initiatives.
{"title":"Techno-Economic Analysis for Raja Ampat Off-Grid System","authors":"Lukman Subekti, Candra Febri Nugraha, Muhammad Arrofiq, Ahmad Adhiim Muthahhari, Budi Eko Prasetyo, Qurrota A’yun","doi":"10.25077/jnte.v13n1.1180.2024","DOIUrl":"https://doi.org/10.25077/jnte.v13n1.1180.2024","url":null,"abstract":"Indonesia, an expansive archipelagic nation with over 17,000 islands, encounters significant challenges in ensuring a sustainable and dependable electricity supply, particularly in its West Papua region. The reliance on diesel fuel for electricity generation in this area poses substantial environmental risks and incurs high costs. A comprehensive research study addressing the environmental and economic challenges associated with diesel dependence in West Papua proposed a shift towards sustainable and cost-effective solutions by advocating for adopting off-grid hybrid power systems. This study targeted Yensawai Village in the Raja Ampat Islands, employing a detailed techno-economic analysis through HOMER Pro to identify the most cost-effective system configurations. The findings indicated that the optimal setup consists of a 160 kW diesel generator, complemented by a 70.1 kW solar photovoltaic (PV) system, a 30 kW inverter, and an 80 kWh battery storage unit. This configuration not only proved to be economically viable by reducing the levelized cost of electricity (CoE) by 15.7%—achieving a CoE of $0.236/kWh compared to the base scenario's $0.280/kWh—but also highlighted the potential for similar benefits across regional systems. By focusing on the economic advantages of hybrid energy configurations, this research contributes significantly to the broader discourse on sustainability and the urgent need to reduce diesel dependence, offering a practical approach to cutting electricity generation costs in remote island communities and advancing sustainability initiatives.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"17 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140361396","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 : 2024-03-31DOI: 10.25077/jnte.v13n1.1155.2024
Heru Supriyono, Fedrik Fajar Alanro, Agus Supardi
DC motors are widely used as propulsions, including in electric bicycles. The problem faced by students in the DC motor control laboratory working using software simulation is that they do not have practical learning experience using digital instruments. This article aims to develop a DC motor speed control that can be used to learn practical Proportional Integral Derivative (PID) control in the laboratory. The DC motor speed control was developed using a combination of Arduino UNO microcontroller and Matlab software. The PID method was used because it is still broadly studied and applied in industries. The test results showed that the developed trainer can work well with PID variable values that can be entered via the keypad, and DC motor transient responses can be displayed in Matlab. From the experimental results, it was found that the optimal PID variable values were Kp=0.04, Ki=0.05, and Kd=0.004, where the controller produced a low overshoot value, i.e., 0.73% of its set point and a settling time of 10.66 seconds. The test results of using the developed trainer in the Fundamental of Control Engineering laboratory work showed that the developed trainer gave students practical learning experience in designing PID control for DC motor speed by using digital equipment, i.e., microcontroller and actual DC motor as well as analyzing its corresponding transient response in Matlab software environment
{"title":"Development of DC Motor Speed Control Using PID Based on Arduino and Matlab For Laboratory Trainer","authors":"Heru Supriyono, Fedrik Fajar Alanro, Agus Supardi","doi":"10.25077/jnte.v13n1.1155.2024","DOIUrl":"https://doi.org/10.25077/jnte.v13n1.1155.2024","url":null,"abstract":"DC motors are widely used as propulsions, including in electric bicycles. The problem faced by students in the DC motor control laboratory working using software simulation is that they do not have practical learning experience using digital instruments. This article aims to develop a DC motor speed control that can be used to learn practical Proportional Integral Derivative (PID) control in the laboratory. The DC motor speed control was developed using a combination of Arduino UNO microcontroller and Matlab software. The PID method was used because it is still broadly studied and applied in industries. The test results showed that the developed trainer can work well with PID variable values that can be entered via the keypad, and DC motor transient responses can be displayed in Matlab. From the experimental results, it was found that the optimal PID variable values were Kp=0.04, Ki=0.05, and Kd=0.004, where the controller produced a low overshoot value, i.e., 0.73% of its set point and a settling time of 10.66 seconds. The test results of using the developed trainer in the Fundamental of Control Engineering laboratory work showed that the developed trainer gave students practical learning experience in designing PID control for DC motor speed by using digital equipment, i.e., microcontroller and actual DC motor as well as analyzing its corresponding transient response in Matlab software environment","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"33 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358462","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 : 2024-03-31DOI: 10.25077/jnte.v13n1.1191.2024
Herlambang Sigit Pramono, Vando Gusti Al Hakim, Faris Alfianto
Natural disasters like earthquakes frequently cause building collapses, trapping many victims under dense rubble. The first 72 hours are crucial for locating survivors, but the dangers of secondary collapse hinder direct access. Teleoperated robots can provide vital visual data to aid rescue efforts, though many prototypes remain constrained by high complexity, cost, and minimal customizability. This work investigates developing an Internet of Things (IoT) integrated disaster response robot that delivers accessible and remotely controllable capabilities for victim identification in hazardous collapse sites. Requirements analysis was conducted through a literature review and first responder interviews to determine the critical capabilities needed. The robot was designed using 3D modeling software and assembled using 3D printed and off-the-shelf components. It features remote-controllable movement, real-time video feed, geopositioning, and remote lighting toggling. Rigorous lab tests validated core functionalities, including camera image acquisition, Bluetooth communication ranges up to 10 meters, and comparable GPS coordinate accuracy to a smartphone. Further field experiments showcased the robot's ability to transmit smooth video signals over distances up to 12 meters and its adeptness at navigating complex terrains, evidenced by its proficient left/right panning and ability to surmount obstacles. An affordable Internet-of-Things integrated disaster robot tailored to victim identification was successfully designed, prototyped, and tested. This robot aids search and rescue operations by delivering visual and spatial data about hard-to-reach victims during the critical hours after disaster strikes. This confirms strong potential, accessibility, and customizability for professional and volunteer urban search and rescue teams across environments and economic constraints.
{"title":"IoT-Based Disaster Response Robot for Victim Identification in Building Collapses","authors":"Herlambang Sigit Pramono, Vando Gusti Al Hakim, Faris Alfianto","doi":"10.25077/jnte.v13n1.1191.2024","DOIUrl":"https://doi.org/10.25077/jnte.v13n1.1191.2024","url":null,"abstract":"Natural disasters like earthquakes frequently cause building collapses, trapping many victims under dense rubble. The first 72 hours are crucial for locating survivors, but the dangers of secondary collapse hinder direct access. Teleoperated robots can provide vital visual data to aid rescue efforts, though many prototypes remain constrained by high complexity, cost, and minimal customizability. This work investigates developing an Internet of Things (IoT) integrated disaster response robot that delivers accessible and remotely controllable capabilities for victim identification in hazardous collapse sites. Requirements analysis was conducted through a literature review and first responder interviews to determine the critical capabilities needed. The robot was designed using 3D modeling software and assembled using 3D printed and off-the-shelf components. It features remote-controllable movement, real-time video feed, geopositioning, and remote lighting toggling. Rigorous lab tests validated core functionalities, including camera image acquisition, Bluetooth communication ranges up to 10 meters, and comparable GPS coordinate accuracy to a smartphone. Further field experiments showcased the robot's ability to transmit smooth video signals over distances up to 12 meters and its adeptness at navigating complex terrains, evidenced by its proficient left/right panning and ability to surmount obstacles. An affordable Internet-of-Things integrated disaster robot tailored to victim identification was successfully designed, prototyped, and tested. This robot aids search and rescue operations by delivering visual and spatial data about hard-to-reach victims during the critical hours after disaster strikes. This confirms strong potential, accessibility, and customizability for professional and volunteer urban search and rescue teams across environments and economic constraints.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140361195","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 : 2024-03-29DOI: 10.25077/jnte.v13n1.1148.2024
Elvira Sukma Wahyuni, Alvita Widya, Kustiawan Putri, Nisa Agustin Pratiwi Pelu, Firdaus, I. A. Wiraagni
Wounds result from physical violence that damages the continuity of body tissues and are frequently observed in forensic medicine and medicolegal science. In forensic medicine and medicolegal science, wounds play a significant role in creating a medicolegal examination and report (VeR) for deceased individuals and living victims. However, research findings indicate that the quality of clinical forensic descriptive results in VeR needs to improve in several hospitals in Indonesia. Meanwhile, high-quality VeR results are crucial in determining penalties for perpetrators in court, and poor VeR results can hinder the legal process. The application of information technology in medicine has yielded numerous tools that can assist experts in carrying out their duties. Likewise, clinical forensics, a generally conservative forensic pathology practice, can be enhanced through image-processing techniques and machine learning. Digital technology support for forensic cases has been available previously, such as in forensic photography; however, its application still needs improvement, and further development is required. This study applied a Yolo V4-based machine learning and image processing algorithm to classify and detect types of wounds. This algorithm was chosen for its high speed and accuracy in classification and detection tasks. The research results showed that the learning model's performance, measured in accuracy, precision, recall, and average F1 score, reached 92%. Usability testing showed that the system performed well and could be helpful with minor improvements.
伤口源于身体暴力，破坏了身体组织的连续性，在法医学和法医科学中经常被观察到。在法医学和法医科学中，伤口在为死者和活着的受害者制作法医检查和报告（VeR）中发挥着重要作用。然而，研究结果表明，印尼几家医院在 VeR 方面的临床法医描述结果质量有待提高。同时，高质量的法医鉴定结果对于在法庭上确定对犯罪者的处罚至关重要，而糟糕的法医鉴定结果可能会阻碍法律程序。信息技术在医学领域的应用产生了许多工具，可以帮助专家履行职责。同样，通过图像处理技术和机器学习，临床法医学这种通常比较保守的法医病理学实践也可以得到加强。法医案件以前就有数字技术支持，如法医摄影；但其应用仍有待改进，需要进一步发展。本研究采用基于 Yolo V4 的机器学习和图像处理算法对伤口类型进行分类和检测。选择该算法是因为它在分类和检测任务中速度快、准确性高。研究结果表明，以准确率、精确度、召回率和平均 F1 分数衡量，学习模型的性能达到了 92%。可用性测试表明，该系统性能良好，稍加改进即可发挥作用。
{"title":"Image Processing-Based Application for Determining Wound Types in Forensic Medical Cases","authors":"Elvira Sukma Wahyuni, Alvita Widya, Kustiawan Putri, Nisa Agustin Pratiwi Pelu, Firdaus, I. A. Wiraagni","doi":"10.25077/jnte.v13n1.1148.2024","DOIUrl":"https://doi.org/10.25077/jnte.v13n1.1148.2024","url":null,"abstract":"Wounds result from physical violence that damages the continuity of body tissues and are frequently observed in forensic medicine and medicolegal science. In forensic medicine and medicolegal science, wounds play a significant role in creating a medicolegal examination and report (VeR) for deceased individuals and living victims. However, research findings indicate that the quality of clinical forensic descriptive results in VeR needs to improve in several hospitals in Indonesia. Meanwhile, high-quality VeR results are crucial in determining penalties for perpetrators in court, and poor VeR results can hinder the legal process. The application of information technology in medicine has yielded numerous tools that can assist experts in carrying out their duties. Likewise, clinical forensics, a generally conservative forensic pathology practice, can be enhanced through image-processing techniques and machine learning. Digital technology support for forensic cases has been available previously, such as in forensic photography; however, its application still needs improvement, and further development is required. This study applied a Yolo V4-based machine learning and image processing algorithm to classify and detect types of wounds. This algorithm was chosen for its high speed and accuracy in classification and detection tasks. The research results showed that the learning model's performance, measured in accuracy, precision, recall, and average F1 score, reached 92%. Usability testing showed that the system performed well and could be helpful with minor improvements.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"62 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367830","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 : 2024-03-29DOI: 10.25077/jnte.v13n1.1177.2024
Wayan Sutaya, Ida Ayu, Dwi Giriantari, W. G. Ariastina, Nyoman Satya Kumara
Implementing photovoltaic (PV) systems as direct power sources for motors without batteries is a complex process that requires a sophisticated control mechanism. The crucial aspect of PV systems is the Maximum Power Point Tracking (MPPT) process, which ensures that the installed PV system generates optimal energy output. A recent study has analyzed research related to PV systems supplying power to pump motors, and the results have successfully classified these systems into two main models: the two-stage and the single-stage. The two-stage model involves separate power tracking and load consumption control processes, while the single-stage model integrates power tracking and load consumption control into a single process. A comparative analysis of these two models has revealed that the two-stage model exhibits higher stability due to the separate power tracking and load consumption control processes. Aspects such as the MPPT process, motor power consumption, and the utilization of DC-link capacitors were examined in this study. The findings of this comparative study contribute valuable insights into the effectiveness and stability of two-stage and single-stage models in PV systems supplying power to motors without batteries. The results will significantly interest researchers and practitioners working in Photovoltaic systems and motor control, providing helpful information for designing and implementing more efficient and reliable PV systems.
{"title":"Comparative Analysis of Two-Stage and Single-Stage Models in Batteryless PV Systems for Motor Power Supply","authors":"Wayan Sutaya, Ida Ayu, Dwi Giriantari, W. G. Ariastina, Nyoman Satya Kumara","doi":"10.25077/jnte.v13n1.1177.2024","DOIUrl":"https://doi.org/10.25077/jnte.v13n1.1177.2024","url":null,"abstract":"Implementing photovoltaic (PV) systems as direct power sources for motors without batteries is a complex process that requires a sophisticated control mechanism. The crucial aspect of PV systems is the Maximum Power Point Tracking (MPPT) process, which ensures that the installed PV system generates optimal energy output. A recent study has analyzed research related to PV systems supplying power to pump motors, and the results have successfully classified these systems into two main models: the two-stage and the single-stage. The two-stage model involves separate power tracking and load consumption control processes, while the single-stage model integrates power tracking and load consumption control into a single process. A comparative analysis of these two models has revealed that the two-stage model exhibits higher stability due to the separate power tracking and load consumption control processes. Aspects such as the MPPT process, motor power consumption, and the utilization of DC-link capacitors were examined in this study. The findings of this comparative study contribute valuable insights into the effectiveness and stability of two-stage and single-stage models in PV systems supplying power to motors without batteries. The results will significantly interest researchers and practitioners working in Photovoltaic systems and motor control, providing helpful information for designing and implementing more efficient and reliable PV systems.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"54 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365602","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 : 2024-03-29DOI: 10.25077/jnte.v13n1.1184.2024
Muhammad Rafli Ramadhan, Satria Mandala, Rafi Ullah, Wael M.S. Yafooz, Muhammad Qomaruddin
Valvular Heart Disease (VHD) is a significant cause of mortality worldwide. Although extensive research has been conducted to address this issue, practical implementation of existing VHD detection results in medicine still falls short of optimal performance. Recent investigations into machine learning for VHD detection have achieved commendable accuracy, sensitivity, and robustness. To address this limitation, our research proposes utilizing Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) to enhance VHD detection. Notably, SFD-CNN operates on phonocardiogram (PCG) signals, distinguishing itself from existing methods based on electrocardiogram (ECG) signals. We present two experimental scenarios to assess the performance of SFD-CNN: one under default parameter conditions and another with hyperparameter tuning. The experimental results demonstrate that SFD-CNN surpasses other existing models, achieving outstanding accuracy (96.80%), precision (93.25%), sensitivity (91.99%), specificity (98.00%), and F1-score (92.09%). The outstanding performance of SFD-CNN in VHD detection suggests that it holds great promise for practical use in various medical applications. Its potential lies in its ability to accurately identify and classify VHD, enabling early detection and timely intervention. SFD-CNN could significantly improve patient outcomes and reduce the burden on healthcare systems. With further development and refinement, SFD-CNN has the potential to revolutionize the field of VHD detection and become an indispensable tool for healthcare professionals.
{"title":"Enhanced Identification of Valvular Heart Diseases through Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN)","authors":"Muhammad Rafli Ramadhan, Satria Mandala, Rafi Ullah, Wael M.S. Yafooz, Muhammad Qomaruddin","doi":"10.25077/jnte.v13n1.1184.2024","DOIUrl":"https://doi.org/10.25077/jnte.v13n1.1184.2024","url":null,"abstract":"Valvular Heart Disease (VHD) is a significant cause of mortality worldwide. Although extensive research has been conducted to address this issue, practical implementation of existing VHD detection results in medicine still falls short of optimal performance. Recent investigations into machine learning for VHD detection have achieved commendable accuracy, sensitivity, and robustness. To address this limitation, our research proposes utilizing Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) to enhance VHD detection. Notably, SFD-CNN operates on phonocardiogram (PCG) signals, distinguishing itself from existing methods based on electrocardiogram (ECG) signals. We present two experimental scenarios to assess the performance of SFD-CNN: one under default parameter conditions and another with hyperparameter tuning. The experimental results demonstrate that SFD-CNN surpasses other existing models, achieving outstanding accuracy (96.80%), precision (93.25%), sensitivity (91.99%), specificity (98.00%), and F1-score (92.09%). The outstanding performance of SFD-CNN in VHD detection suggests that it holds great promise for practical use in various medical applications. Its potential lies in its ability to accurately identify and classify VHD, enabling early detection and timely intervention. SFD-CNN could significantly improve patient outcomes and reduce the burden on healthcare systems. With further development and refinement, SFD-CNN has the potential to revolutionize the field of VHD detection and become an indispensable tool for healthcare professionals.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"37 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366716","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 : 2023-11-30DOI: 10.25077/jnte.v12n3.1120.2023
Levin Halim, Sin Euy Gun, Faisal Wahab
Indonesia's heavy reliance on non-renewable energy sources, such as fossil fuels and other resources obtained from mining, poses sustainability challenges. Solar panels, which are environmentally friendly and renewable energy alternatives, are designed to convert solar energy into electricity, and they have shown room for improvement in their efficiency. One method to enhance its efficiency is the utilization of dual-axis solar tracking, employing linear actuators for control over both horizontal and vertical panel movements. In addition, solar panels frequently experience efficiency losses as a result of high working temperatures when exposed to sunlight. The use of water treatment techniques may help address this problem. In this research, the two-axis solar tracking approach with water treatment methods were combined to achieve greater efficiency and boost energy production. A notable increase in solar panel efficiency was seen subsequent to the design, implementation, and testing of the proposed system, resulting in a notable rise in power output. Combining the two-axis solar tracking approach with water treatment methods produced solar panels with a 7.46% efficiency and a 17.77% power increment. Dual-axis solar tracking and combined with water treatment could significantly increase solar panel efficiency, which will ultimately lead to environtmentally clean renewable energy production increment.
{"title":"Solar Panel Efficiency Improvement through Dual-Axis Solar Tracking with Fuzzy Logic and Water Treatment Techniques","authors":"Levin Halim, Sin Euy Gun, Faisal Wahab","doi":"10.25077/jnte.v12n3.1120.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n3.1120.2023","url":null,"abstract":"Indonesia's heavy reliance on non-renewable energy sources, such as fossil fuels and other resources obtained from mining, poses sustainability challenges. Solar panels, which are environmentally friendly and renewable energy alternatives, are designed to convert solar energy into electricity, and they have shown room for improvement in their efficiency. One method to enhance its efficiency is the utilization of dual-axis solar tracking, employing linear actuators for control over both horizontal and vertical panel movements. In addition, solar panels frequently experience efficiency losses as a result of high working temperatures when exposed to sunlight. The use of water treatment techniques may help address this problem. In this research, the two-axis solar tracking approach with water treatment methods were combined to achieve greater efficiency and boost energy production. A notable increase in solar panel efficiency was seen subsequent to the design, implementation, and testing of the proposed system, resulting in a notable rise in power output. Combining the two-axis solar tracking approach with water treatment methods produced solar panels with a 7.46% efficiency and a 17.77% power increment. Dual-axis solar tracking and combined with water treatment could significantly increase solar panel efficiency, which will ultimately lead to environtmentally clean renewable energy production increment.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208990","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 : 2023-11-30DOI: 10.25077/jnte.v12n3.1092.2023
Muhammad Imran Hamid, Sulfandri, Afifah
This research examines the impact of interruptions in electricity supply on the production of small and medium enterprises in West Sumatra from 2014 to 2021. The data used in the research was obtained from the Ministry of Trade and Industry of West Sumatra, including the production variables, employment, investment, and other variables that influence the production activities. A regression equation connecting production factors and production levels is formulated. Furthermore, another regression equation is also formulated by considering the electricity interruption factor, namely the SAIDI index on production levels. The effect of electrical power interruptions is then evaluated by comparing the two equations. The research results show that the most significant production loss occurred in 2019, 16.07 hours/year, while the most negligible loss occurred in 2015, 6.53 hours/year. Trend data collected during the research period regarding loss conditions and interruption parameters shows that electricity disturbances do not have a linear impact on production losses. The research also shows that electric power does not significantly impact the production activities of small and medium enterprises in West Sumatra.
{"title":"The Effect of Electricity Supply Interruptions on Small Business Productivity in West Sumatra","authors":"Muhammad Imran Hamid, Sulfandri, Afifah","doi":"10.25077/jnte.v12n3.1092.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n3.1092.2023","url":null,"abstract":"This research examines the impact of interruptions in electricity supply on the production of small and medium enterprises in West Sumatra from 2014 to 2021. The data used in the research was obtained from the Ministry of Trade and Industry of West Sumatra, including the production variables, employment, investment, and other variables that influence the production activities. A regression equation connecting production factors and production levels is formulated. Furthermore, another regression equation is also formulated by considering the electricity interruption factor, namely the SAIDI index on production levels. The effect of electrical power interruptions is then evaluated by comparing the two equations. The research results show that the most significant production loss occurred in 2019, 16.07 hours/year, while the most negligible loss occurred in 2015, 6.53 hours/year. Trend data collected during the research period regarding loss conditions and interruption parameters shows that electricity disturbances do not have a linear impact on production losses. The research also shows that electric power does not significantly impact the production activities of small and medium enterprises in West Sumatra.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"1016 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139204903","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}
Hyperemesis Gravidarum (HG) is a pregnancy complication that is often overlooked as it is typically considered normal. If HG is not properly treated, nutrition will not be fulfilled which can negatively affect maternal and fetal health and even maternal and fetal death. The exact cause of HG is not identified, so there are no effective preventive methods. However early detection can help for prompt and appropriate treatment. Therefore, a monitoring system for pregnancy conditions was designed for HG early detection. This system employs the MPX5050 DP pressure sensor for measuring blood pressure, the MAX30100 for assessing maternal heart rate and oxygen saturation, the MAX4466 sensor for monitoring fetal heart rate, and an expert system using the certainty factor method to diagnose the probability of hyperemesis gravidarum. The expert system achieves an accuracy of 93.33%. In comparison to the aneroid sphygmomanometer, the designed sphygmomanometer reveals a mean difference of 3.5 mmHg for diastolic pressure, with a standard deviation below 8 mmHg for both systolic and diastolic pressures. The measurement of heart rate and oxygen saturation has a deviation of 1.8 % and 1.02 % respectively. These deviations align with the standards specified by the Ministry of Health for medical devices. For the fetal heart rate, the mean deviation is 3.4 bpm, and the measurement error is 2.38%. Thus, this system can be utilized to monitor pregnancy conditions, enabling the early detection of hyperemesis gravidarum
{"title":"Electromedical Device And Expert System for Early Detection of Hyperemesis Gravidarum","authors":"Fitrilina Fitrilina, Ganesha, Yanolanda Suzantry Handayani, Alex Surapati, Rahayu Trisetyowati Untari, Heru Dibyo Laksono, Melda Latif","doi":"10.25077/jnte.v12n3.1130.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n3.1130.2023","url":null,"abstract":"Hyperemesis Gravidarum (HG) is a pregnancy complication that is often overlooked as it is typically considered normal. If HG is not properly treated, nutrition will not be fulfilled which can negatively affect maternal and fetal health and even maternal and fetal death. The exact cause of HG is not identified, so there are no effective preventive methods. However early detection can help for prompt and appropriate treatment. Therefore, a monitoring system for pregnancy conditions was designed for HG early detection. This system employs the MPX5050 DP pressure sensor for measuring blood pressure, the MAX30100 for assessing maternal heart rate and oxygen saturation, the MAX4466 sensor for monitoring fetal heart rate, and an expert system using the certainty factor method to diagnose the probability of hyperemesis gravidarum. The expert system achieves an accuracy of 93.33%. In comparison to the aneroid sphygmomanometer, the designed sphygmomanometer reveals a mean difference of 3.5 mmHg for diastolic pressure, with a standard deviation below 8 mmHg for both systolic and diastolic pressures. The measurement of heart rate and oxygen saturation has a deviation of 1.8 % and 1.02 % respectively. These deviations align with the standards specified by the Ministry of Health for medical devices. For the fetal heart rate, the mean deviation is 3.4 bpm, and the measurement error is 2.38%. Thus, this system can be utilized to monitor pregnancy conditions, enabling the early detection of hyperemesis gravidarum","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139200142","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 : 2023-11-30DOI: 10.25077/jnte.v12n3.1121.2023
Nia Madu Marliana, Satria Mandala, Yuan Wen, Hau, Wael M.S. Yafooz
Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.
心肌梗死（MI）是一种严重的心血管疾病，在全世界的死亡率都很高。早期检测和持续治疗可大大降低心血管疾病的死亡率。然而，目前需要一种高效的模型，能够在不依赖训练有素的临床专家的情况下实现心脏病的早期检测。使用声心动图（PCG）信号并实施集合学习模型的心肌梗死研究仍然相对较少，往往导致准确率和检出率较低。本研究旨在采用集合学习模型，利用 PCG 信号将心肌梗死分为不同类别。在现阶段的研究中，包括随机森林和逻辑回归在内的几种分类算法利用从音频信号中提取的特征作为集合学习的基本模型。对模型性能的评估显示，堆叠模型的准确率达到 96%。这些结果表明，我们的系统可以适当、准确地对 PCG 数据中的 MI 进行分类。我们相信，这项研究的结果将提高心脏病发作的诊断和治疗水平，使其更加有效和准确。
{"title":"Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning","authors":"Nia Madu Marliana, Satria Mandala, Yuan Wen, Hau, Wael M.S. Yafooz","doi":"10.25077/jnte.v12n3.1121.2023","DOIUrl":"https://doi.org/10.25077/jnte.v12n3.1121.2023","url":null,"abstract":"Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.","PeriodicalId":30660,"journal":{"name":"Jurnal Nasional Teknik Elektro","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139198394","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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