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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 5 Documents
 1 
Search results for , issue "Vol 13, No 2: July 2024" : 5 Documents clear
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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