cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Suci Dwijayanti
Contact Email
sucidwijayanti@ft.unsri.ac.id
Phone
-
Journal Mail Official
selcojournal@gmail.com
Editorial Address
Jl. Palembang Prabumulih KM. 32 Laboratorium Teknik Kendali dan Robotika, Jurusan Teknik Elektro, Fakultas Teknik Universitas Sriwijaya
Location
Kab. ogan ilir,
Sumatera selatan
INDONESIA
Sriwijaya Electrical and Computer Engineering (Selco) Journal
Published by Universitas Sriwijaya
ISSN : -     EISSN : 30326370     DOI : https://doi.org/10.62420
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Selco Journal is a peer-reviewed, open-access publication dedicated to advancing research and innovation in the fields of electrical engineering and computer engineering. SELCO journal serves as a platform for scholars, researchers, academicians, and industry professionals to disseminate their original contributions, share valuable insights, and engage in the exchange of knowledge in these dynamic and rapidly evolving disciplines. Selco Journal is published 2 times a year (2 issues/year) every February and September. Key Focus Areas: Electrical Engineering: Power Systems and Renewable Energy Electrical Machines and Drives Control Systems and Automation High Voltage Engineering Electronics and Microelectronics Signal Processing and Communication Robotics and Automation Electric Transportation Systems Computer Engineering: Computer Architecture and Organization Software Engineering and Development Artificial Intelligence and Machine Learning Embedded Systems and IoT Computer Networks and Security Data Science and Big Data Analytics Cyber-Physical Systems Quantum Computing and Emerging Technologies
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 10 Documents
 1 
Real-time recognition of Indonesian sign language using recurrent neural network Yoel Andreas; Suci Dwijayanti; Hera Hikmarika; Bhakti Yudho Suprapto
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i1.1

Abstract

Hand gestures serve as a vital means of communication for deaf individuals. They often face communication challenges in their daily interactions due to the language barrier. This underscores the necessity of sign language interpreters. However, prevailing methods primarily rely on the Indonesian Sign Language System (SIBI), despite the widespread use of Indonesian Sign Language (BISINDO) for communication. Additionally, the effectiveness of these methods hinges greatly on the accuracy of feature extraction. To address this limitation, this study introduces a Recurrent Neural Network (RNN) approach for BISINDO interpretation. Data acquisition involved the use of a webcam to capture video data, subsequently transformed into frames andarrays. Collected from three respondents, the dataset comprises 3,240 videos and 97,200 array data points, encompassing letters and numbers. Among the tested parameters, training results indicate that utilizing the Adam optimizer with a learning rate of 0.0001 and 500 epochs yields the highest accuracy and minimal loss compared to other configurations. Subsequently, this modelunderwent real-time testing, conducted five times for 36 classes, achieving an accuracy of 81.67%. It is important to note that errors may arise due to similarities within hand signal language, particularly involving characters such as I, J, D, P, M, and N.
Simulation of robot arm system control using fuzzy logic Syarifa Fitria; Sariman Sariman; Zainuddin Nawawi; Rizda Fitri Kurnia; Dwirina Yuniarti; Tresna Dewi
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i1.2

Abstract

This study explains a robot arm system control simulation using a fuzzy logic controller. The robot arm is a nonlinear system and is not easily controlled using a conventional control system since the mathematical model of the system is not easy to obtain. Considering these problems, this final project presents a simulation of a fuzzy logic controller on a robot arm system. The aim of this control is to move the robot arm at an angular speed thus the robot gets the desired torque or the right position. Determining fuzzy rules greatly influences system stability. This control network produces output in the form of torque while the robot arm system calculates the next torque from the robot. The best output results were found on the triangular curve at response 2 because of the difference in values 0.01 on the final angle obtained.
Effect of fresnel lens distance on the output power of the prowe plant solar-based electricity transistor 2N3055 Akbar Nugraha; Caroline Caroline; Ike Bayusari; Rahmawati Rahmawati; Hermawati Hermawati
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i1.3

Abstract

Developments in the human life sector have progressed rapidly today, paralleled by the continuous advancement of technology. This trend is expected to significantly elevate the demand for electricity as one of the primary requirements. Currently, power plants heavily rely on petroleum as their main fuel source for electricity generation, contributing to environmental unfriendliness. In response to this, renewable energy has emerged as a viable solution to reduce dependence on petroleum fuel. One notable example of renewable energy is solar power, harnessed through Solar Power Plants. Research has been conducted to develop a transistor-based solar power plant using the 2N3055 model, with three variations in the distance of the Fresnel lens: 5 cm, 10 cm, and 20 cm over 14 days. The research yielded the highest output power, with an average voltage value of 13.868 V and a current value of 0.000038 A recorded on the 13th day. The power value reached 0.0005199 W on a prototype with a lens distance of 5 cm on the 14th day of research. This phenomenon occurs during sunny environmental conditions, leading to the transistor producing higher voltage and current due to focused light. However, different lens distances can result in improper focusing of light or even prevent light from reaching the transistors altogether, causing suboptimal electricity production or no production. Therefore, the influence of different Fresnel lens distances affects the light focusing on the transistor, consequently impacting the overall power output value.
Performance comparison of MobileNet, EfficientNet, and Inception for predicting crop disease Salman Al Farizi Harahap; Irmawan Irmawan
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i1.4

Abstract

Disease plant food can cause significant loss in production agriculture since difficult to detect early symptoms of disease. Apart from that, the selection of Convolutional Neural Network (CNN) architecture for the detection of disease plants often faces the challenge of trade-offs between accuracy and efficiency. In this research, we propose a solution with compares the performance of three current CNN architectures, ie MobileNet, EfficientNet, and Inception, in context predictions of disease plant food. We implement a transfer learning approach to increase efficiency and performance model predictions. The contribution of this study is located on the guide practical for researchers and practitioners in choosing appropriate CNN architecture with need-specific application detection disease plant food. In this experiment, we use 3 datasets to represent plant food in Indonesia, namely rice, corn, and potatoes. Metric evaluation performances like accuracy, precision, recall, and F1-score are used to compare the results of the experiment. Experimental results show a significant difference in performance third tested architecture. MobileNet stands out in speed inference and necessity source low power, temporary EfficientNet shows a good balance between accuracy and efficiency. Inception delivers superior results in detecting feature complex however needs to source more power. In conclusion, the selection of CNN architecture for predictions of disease plant food must consider the trade-off between accuracy, speed inference, and necessity source power. These experimental results can give a guide valuable for practitioners in making appropriate technology with need Specific application detection disease plant food.
Application of the Savitzky-Golay filter in multi-spectral signal processing Syahrial Syahrial; Melinda Melinda; Junidar Junidar; Safrizal Razali; Zulhelmi Zulhelmi
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i1.5

Abstract

Multi-spectral signals are the result of the interaction between electromagnetic energy and the test material, which is then displayed by the signal fluctuation pattern of the test material. Signal fluctuations are inaccuracies in the peak amplitude of a signal caused by noise in the data. This fluctuation pattern reflects the properties of the test material, especially in this case H2O. To overcome this problem, it is necessary to use the right filter to smooth the signal and reduce the noise in the data so that the fluctuation pattern obtained is clearer and more accurate. This research involves the segmentation of HF fluctuation patterns, followed by the application of a Savitzky-Golay filter for signal smoothing. Signal quality is assessed objectively by calculating the Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The research results show that the Savitzky-Golay filter succeeded in reducing noise and producing clearer fluctuation patterns. The SNR value varies, with the largest value reaching 16.6146 dB, and the smallest value being 3.0171 dB. This research contributes to a new method, namely the Savitzky-Golay adaptive filter, to identify multi-spectral signal fluctuation patterns more effectively, thereby enabling more accurate identification of fluctuation patterns. Apart from that, this research also provides insight into the characteristics of H2O which can be identified through fluctuation patterns, especially in certain segments with high amplitude. This method has potential for applications in various fields, especially in precise multi-spectral signal analysis.
Image processing using sparse representation for classification with semi-random projection dimension reduction for the image recognition system Puspa Kurniasari; Izzatul Jannah
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.6

Abstract

Image recognition is a technology to identify objects, places, people and several other variables in digital images. The algorithm that can be used in the image recognition system is Sparse Representation for Classification. However, the high computational load is a problem in this study. In addition, there is a lot of training data needed to meet the sparse condition which is a weakness of this algorithm. Thus, to overcome this problem, dimensionality reduction can be carried out on the image using the Semi Random Projection method. In this study, the author used PyCharm software to process the Guide User Interface (GUI) of the dimensionality reduction system and image recognition using Semi Random Projection-Sparse Representation for Classification. Testing was carried out using 100 training data in the form of 50 red-green-blue images and 50 grayscale images. The images are divided into 10 classes and use 10 test images that have been added with noise and occlusion. Testing was carried out five times each on each test image. From the testing in this study, the results obtained on good performance parameters with an average accuracy of 98.93%, an average Peak Signal Noise to Ratio (PSNR) of 33.392741 dB and an average computing time of 1112.57952 ms.
Enhancing the performance of IoT network intrusion detection models using NF-ToN-IoT-V2 and IoTID20 datasets with chi-square feature selection Thereza, Nadia; Harahap, Pardomuan Raja
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.7

Abstract

The Internet of Things (IoT) expansion increases the number and size of networks and the volume of sensitive and private data. Consequently, IoT networks have become vulnerable to various threats and attacks. Researchers have recently devised intrusion detection systems (IDSs) to detect threats and attacks on IoT networks. However, in developing IDS for IoT networks, previous studies predominantly used more limited datasets to depict the actual IoT network characteristics. Thus, this research used datasets containing network flow records from real IoT networks, namely the NF-ToN-IoT-V2 and IoTID20. Various machine-learning algorithms, such as random forest, decision tree, naïve Bayes, AdaBoost, and XGBoost, were employed to train and evaluate the datasets for developing intrusion detection models. We investigated the model's performance based on accuracy, precision, recall, F1-score, false positive rate, training, and testing time utilization. We used the chi-square algorithm for feature selection to select the most relevant and valuable features. The findings indicate that implementing feature selection using chi-square improves the performance of the detection system models. By applying the Chi-Square algorithm, the RF model that outperforms in terms of accuracy performance increases its accuracy up to 0.42% on the NF-ToN-IoT-V2 testing and 0.16% on the IoTID20 testing. The DT model with the fastest training and testing time reduces its time utilization by 6.98% on the NF-ToN-IoT-V2 testing and 29.63% on the IoTID20 testing through feature selection.
Classification of autism features in electroencephalography recordings using random forest method Melinda, Melinda; Zahran Jemi , Faris; Muliyadi, Muliyadi; Safitri, Rini; Mina Rizky , Muharratul; Duana, Maiza
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.9

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that significantly impacts communication, social interaction, and behavior in children, often leading to withdrawal, repetitive behaviors, and difficulties with eye contact. Traditional diagnostic methods primarily relied on behavioral assessments, which have proven insufficient in accuracy. This study aims to enhance ASD diagnosis by employing Electroencephalography (EEG) as an objective marker to differentiate between individuals with ASD and neurotypical individuals. Utilizing a dataset from King Abdulaziz University comprising 16 children—4 neurotypical and 12 with ASD—this research implements preprocessing techniques such as Independent Component Analysis (ICA) to eliminate noise and artifacts from EEG signals. Following this, Wavelet Packet Decomposition (WPD) is applied at three levels to improve signal resolution. Statistical features including mean, variance, skewness, and kurtosis are extracted for classification purposes. The Random Forest (RF) method is then employed for classification, achieving an accuracy of 76.8%. However, classification errors predominantly arise from the imbalance in the dataset, with more data available for ASD subjects compared to neurotypical subjects. The findings reveal significant differences in statistical features between the two groups, indicating the potential of EEG technology and computational algorithms in developing a more accurate and objective ASD diagnosis system. This research contributes valuable insights for early intervention strategies and future studies aimed at improving diagnostic methodologies for children with autism.
Piezoelectric Output Analysis Oktarina, Yurni; Nur Aina Okta Ferrisa; Pola Risma
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.10

Abstract

This study explores the utilization of mechanical energy generated from human footsteps as an alternative energy source through energy harvesting technology using piezoelectric materials. The designed system takes the form of a ceramic tile floor composed of four tiles arranged longitudinally, with each tile containing 30 piezoelectric elements, each 35 mm in diameter. The configuration consists of six piezoelectric units connected in series and five rows arranged in parallel, resulting in a total of 120 piezoelectric units in the entire system. The voltage, current, and power output depend on variations in body weight (60–94 kg), foot size, and the anatomical shape of the user's foot, which affect how many piezoelectric elements receive sufficient pressure during each step. The generated electrical energy is stored in a 12 Volt, 12 Ah battery for subsequent power use. Experimental results show that the system can produce varying amounts of energy depending on user physical parameters, indicating its potential for small-scale implementation in renewable energy applications within urban environments.
Prototype of humidity and temperature control system in IoT-based coffee drying process T, Alan; Zahri, Dizi Eltrien; Handayani, Marta Tika
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.8

Abstract

Drying coffee beans plays an important role in determining the final quality of the coffee beans. Drying is usually done traditionally by drying in the sun. This drying has many shortcomings, so a prototype of an IoT-based humidity and temperature control system for the coffee drying process was created. The sensors and microcontroller are DHT11 and ESP32, respectively. This prototype will work only if the sensed temperature is higher than 60 oC and the drying room’s humidity reaches 12% then the control system will activate a fan. The temperature and humidity display will be displayed on the LCD layer of the prototype. Data regarding the temperature and humidity in the room will be sent to Blynk (a free phone application commercially available), so that the users can see and control these variables from their cell phone.. In this way, users can control and know the temperature and humidity during the coffee bean drying process.  

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal