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INDONESIA
JURNAL NASIONAL TEKNIK ELEKTRO
Published by Universitas Andalas
ISSN : 23022949     EISSN : 24077267     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Jurnal Nasional Teknik Elektro (JNTE) adalah jurnal ilmiah peer-reviewed yang diterbitkan oleh Jurusan Teknik Elektro Universitas Andalas dengan versi cetak (p-ISSN:2302-2949) dan versi elektronik (e-ISSN:2407-7267). JNTE terbit dua kali dalam setahun untuk naskah hasil/bagian penelitian yang berkaitan dengan elektrik, elektronik, telekomunikasi dan informatika.
Arjuna Subject : -
Articles 610 Documents
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Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control Anandika, Arrya; Laksono, Pringgo Dwi; Suhaimi, Muhammad Syaiful Amri bin; Muguro, Joseph; Rusydi, Muhammad Ilhamdi
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1160.2023

Abstract

Rapid technological developments, one of which is technology to build communication relationships between humans and machines using Biosignals. One of them is Electrooculography (EOG). EOG is a type of biosignals obtained from eye movement. Research related to EOG has also developed a lot, especially for virtual keyboard control. Research on virtual keyboard control based on eye gaze motion using electrooculography technology has been widely developed. Previous research mostly drew conclusions based on time consumption in typing paragraphs. However, it has not been seen based on the number of eye gaze motions made by the user. In this research, an adaptive virtual keyboard system is built, controlled using EOG signals. The adaptive virtual keyboard is designed with 7x7 dimensions and has 49 buttons, including main buttons, letters, numbers, symbols, and unused buttons. The layout of the adaptive virtual keyboard has six zones. Each zone has a different number of steps. Characters located in the same zone have the same number of steps. The adaptive feature is to rearrange the position of the character's button based on the previously used characters. In the experiments, 30 respondents controlled static and adaptive virtual keyboards with 7 paragraphs typed. Adaptive mode rearranges the position of buttons based on k-selection activities from respondents. the k numbers are 10, 30, 50, 70 and 100. Two virtual keyboard modes are evaluated based on the number of steps required to type the paragraphs. Test results show that the performance of the adaptive virtual keyboard can shorten the number of user steps compared to static mode. There are tests of the optimal system that can be reduced up to 283 number of steps and from respondents, that can reduced up to 258 number of steps or about 40% of steps. This research underscores the promise of EOG-driven adaptive virtual keyboards, signaling a notable stride in augmenting user interaction efficiency in typing experiences, heralding a promising direction for future human-machine interface advancements.
Comparative Analysis of Two-Stage and Single-Stage Models in Batteryless PV Systems for Motor Power Supply Sutaya, I Wayan; Dwi Giriantari , Ida Ayu; Ariastina, Wayan Gede; Satya Kumara, I Nyoman
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 1: March 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n1.1177.2024

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.
A Techno-Economic Analysis for Raja Ampat Off-Grid System Subekti, Lukman; Nugraha, Candra Febri; Arrofiq, Muhammad; Muthahhari, Ahmad Adhiim; Prasetyo , Budi Eko; A’yun, Qurrota
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 1: March 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n1.1180.2024

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.
Sebuah Identifikasi yang Ditingkatkan dari Penyakit Katup Jantung Dengan Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) Muhammad Rafli Ramadhan; Mandala, Satria; Rafi Ullah; Wael M.S. Yafooz; Muhammad Qomaruddin
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 1: March 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n1.1184.2024

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.
Robot Tanggap Bencana Berbasis IoT untuk Identifikasi Korban pada Runtuhnya Bangunan Pramono, Herlambang Sigit; Hakim, Vando Gusti Al; Alfianto, Faris
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 1: March 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n1.1191.2024

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.
Pengembangan IDS pada IoT Menggunakan Ensemble Learning Nadia Ariana; Satria Mandala; Mohd Fadzil Hasssan; Muhammad Qomaruddin; Bilal Ibrahim Bakri
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1113.2024

Abstract

The utilization of intrusion detection systems (IDS) can significantly enhance the security of IT infrastructure. Machine learning (ML) methods have emerged as a promising approach to improving the capabilities of IDS. The primary objective of an IDS is to detect various types of malicious intrusions with a high detection rate while minimizing false alarms, surpassing the capabilities of a firewall. However, developing an IDS for IOT poses substantial challenges due to the massive volume of data that needs to be processed. To address this, an optimal approach is required to improve the accuracy of data containing numerous attacks. In this study, we propose a novel IDS model that employs the Random Forest, Decision Tree, and Logistic Regression algorithms using a specialized ML technique known as Ensemble Learning. For this research, we used the BoT-IoT datasets as inputs for the IDS model to distinguish between malicious and benign network traffic. To determine the best model, we compared the performance metrics of each algorithm across different parameter combinations. The research findings demonstrate exceptional performance, with metric scores exceeding 99.995% for all parameter combinations. Based on these conclusive results, we deduce that the proposed model achieves remarkable success and outperforms other traditional ML-based IDS models in terms of performance metrics. These outcomes highlight the potential of our novel IDS model to enhance the security posture of IoT-based systems significantly.
Water Quality Control in Carp Fish Ponds Using Fuzzy Logic Darwison; Zaini; Riko Nofendra; Amirul Luthfi; Gylang Bramantya Pratama
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1136.2024

Abstract

Regularly monitoring pond water quality in fish farming is a crucial practice often neglected, negatively impacting goldfish yields. Addressing this issue, a sophisticated device leveraging fuzzy logic has been engineered to accurately regulate acidity, temperature, and water levels, with real-time data accessible through the Blynk smartphone application. This innovative system employs a trio of sensors—namely an acidity sensor, a DS18B20 temperature sensor, and an HCSR04 ultrasonic sensor—coupled with three output mechanisms: an inlet pump, an outlet pump, and a heater, to ensure precise control. Rigorous testing under various conditions at different times of the day, lasting approximately one hour each, demonstrated the device's capability to adjust water's acidity by about 0.1 units per minute, reflecting the influences of fish activity and water temperature, with a deficient accuracy error of 0.19%. Additionally, the system's effectiveness in maintaining a consistent water level was confirmed, exhibiting a refill rate of 1.2 cm per minute as detected by the sensor. This integrated system is instrumental in safeguarding goldfish health and optimizing their productivity by ensuring water quality remains within the desired acidity, temperature, and volume parameters.
APD-BayTM: Prediksi Indeks Kualitas Udara Jakarta Menggunakan Bayesian Optimized LSTM Raey Faldo; Satria Mandala; Mohd Shahrizal Sunar; Salim M. Zaki
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1183.2024

Abstract

The Air Quality Index (AQI) is a metric for evaluating air quality in a region. Jakarta holds the fifth position globally in terms of air pollution. Several studies have been performed to forecast pollution levels in Jakarta. However, existing studies exhibit limitations such as outdated datasets, lack of data normalization, absence of machine learning parameter setting, neglect of k-fold cross-validation, and a failure to incorporate deep learning algorithms for pollution detection. This study introduces an air quality detection system called APD-BayTM to address these issues. This proposed system leverages Long Short-Term Memory (LSTM) and uses Bayesian Optimization (BO) to enhance the performance of air pollution detection. The methodology used in this research involves four key steps: data preprocessing, LSTM model development, hyperparameter tuning through BO, and performance assessment using 5-fold cross-validation. APD-BayTM exhibits robust performance that is comparable to previous research outcomes. The LSTM model in APD-BayTM on the training dataset achieved average precision, recall, F1 score, and accuracy values of 93.29%, 91.41%, 91.89%, and 95.90%, respectively. These metrics improved on the test dataset, reaching 97.44%, 99.71%, 98.52%, and 99.34%, respectively. These findings show the robustness of APD-BayTM across datasets of varying sizes, encompassing both large and small datasets.
Strategi pengoptimalan QCI untuk meminimalkan latensi Adhistian, Patria; Wibowo, Priyo
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1193.2024

Abstract

Limited QCIs (QoS Class Identifiers) restrict the handling different service types with varying quality requirements. This necessitates research on QoS management to minimize latency and improve user experience, particularly for real-time applications like video conferencing and online gaming. This paper proposes a combined optimization scheme targeting QCI 3 to reduce latency. The approach involves disabling DRX, optimizing pre-allocation, and reducing the PDCP discard timer. The optimization performance is studied by taking the case of an e-sport game that demands low network latency, affecting the quality of the players' experience. The optimization scheme was validated through functionality, resource allocation, and air interface latency tests conducted under actual e-sport gaming conditions. Network latency was measured every minute to evaluate the impact of optimization on esports games running under QCI 7, QCI 3, and optimized QCI 3. In addition, air interface latency for optimized QCI 3 under networks with poor coverage and very high-capacity networks was compared to latency under QCI 8 (basic), QCI 7, and regular QCI 3. The optimization strategy demonstrated a significant reduction in air interface latency, up to 19% improvement compared to non-optimized QCI 3. It has reduced air interface latency's maximum, minimum, and standard deviation values during gameplay. The strategy also ensured concurrent operation with multiple QCI values without compromising other application’s throughput. The proposed optimization strategy effectively enhances the user experience by significantly reducing average latency and jitter.
Portable Stress Detection System for Autistic Children Using Fuzzy Logic Melinda, Melinda; Setiawan, Verdy; Yunidar, Yunidar; Gopal Sakarkar; Nurlida Basir
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1203.2024

Abstract

Stress is prone to occur in children with autism. According to the study, around 85% of children who have autism suffer from anxiety disorders that can exacerbate their condition, leading to self-harm and harm to those in their vicinity. Heart rate, skin conductance, and finger temperature changes occur during stress. In this paper, we design a system to monitor heart rate, body temperature, and skin conductance to detect signs of stress. Subsequently, the measurement data is processed using the fuzzy logic (FL) method as a decision-maker algorithm. In particular, we use 64 fuzzy rules with membership functions for each parameter. Parameter measurement results will be displayed using a widget called Gauge, while stress conditions will be displayed using a label widget. The results will be displayed on the Blynk application with an IoT system and viewed remotely via Android devices. The test was conducted on five children aged 5-9 years with varying body conditions. From the test results, the mean accuracy of the heart rate sensor was 95.01%, the mean temperature sensor accuracy was 97.7%, and the mean conductance sensor accuracy was 93.75%. The stress levels range from a minimum of 25% to a maximum of 75%. These findings indicate that the developed tool has performed effectively, and it is feasible to monitor its operation remotely.

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