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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms Sulaiman Olaniyi Abdulsalam; Micheal Olaolu Arowolo; Yakub Kayode Saheed; Jesutofunmi Onaope Afolayan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.2985

Abstract

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifier
Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks Riandini Riandini; Satria Arief Aditya; Rika Novita Wardhani; Sulis Setiowati
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.3500

Abstract

Eye strain is a big concern, especially when it comes to continuous and prolonged online learning. If this is allowed to continue, it will result in Computer Vision Syndrome, also known as Digital Eye Strain (DES), which includes headaches, blurred vision, dry eyes, and even neck and shoulder pain. This condition can be observed either directly based on excessive eye blinking or indirectly based on observations of the electrical activity of eye movements or electrooculography (EOG). The observed blink signal from the EOG, as a representation of eye strain, is the focus of this study. Data acquisition was obtained using the EOG sensor and was carried out on the condition that the participants were conducting online learning activities. There are four different modes of observation taken in succession: when the eye is in a viewing state but without blinking, when the eye blinks intentionally, when the eye is closed, and finally when the eye sees naturally. Observation time is 10s, 20s and 30s, where each interval is performed three times for every mode. The obtained signal is processed by the proposed method. The resulting signal is then labeled as a Blinking signal. Determination of the number of blinks or CNT_PEAK is the result of training this signal by tuning its threshold and width. If the number of blinks is less than or more than 17 then the system will provide a prediction of eye status which is stated in two categories, the first is normal eye while the last is eye strain or fatigue.
SISO System Model Reduction and Digital Controller Design using Nature Inspired Heuristic Optimisation Algorithms Nivas Bachu; Rittwik Sood
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.3676

Abstract

This research article explores an algorithm to reduce the order of a SISO system and thereby to design a digital controller. The reduced order modelling of a large complex system eases out the analysis of the system. AGTM (Approximate Generalised Time Moments) method was implemented wherein the responses were matched at different time instants to achieve the reduced system. This research work devises a new method, Ensemble Framework for Optimized System (EFOS), resulting into a reduced system with better performance as compared to conventional techniques. The research also efforts towards effective utilization of various heuristic algorithms like Genetic Algorithm, Particle Swarm Optimization and Luus Jaakola Algorithm, their implementation and a comparison with other techniques based on relative mean square error and time complexity. It was observed that the proposed transfer learning based approach, EFOS, combining the advantages of Luus Jaakola and Genetic algorithms depicted better results than their individual counterparts on diverse performance parameters like speed of convergence and optimal convergence to global minima. The percentage improvement achieved in the time taken for design of the digital controller was 85.3%, with no change in delta value.
An Extended Kalman Filter for Nonsmooth Attitude Control Design of Quadrotors using Quaternion Representation Adha Cahyadi; Andreas P. Sandiwan; Samiadji Herdjunanto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.3532

Abstract

This paper proposed Extended Kalman Filter specifically designed for nonlinear and nonsmooth control system applied in Autonomous Quadrotor Control such as sliding mode control. Many controllers focused on global stability usually consider exact parameters through measurements. Such assumptions are not always possible due to the unavailability of sensors or unmeasurable state in real-life condition. In this paper, we consider only the angular velocity is possible for measurement, i.e., only gyroscope measurement is available. This condition is known as omega-state-measurement (OSM). Without loss of generality, for theoretical simplification beside gyroscope measurement, we assume the orientation measurement represented in quaternion is also available. Additive random gaussian noise is included to the measurement model to be used in Kalman Filter. Finally the proposed Extended Kalman Filter implemented in a PD Sliding Mode controller is simulated using many scenarios to verify its effectiveness. The Kalman Filter works well in spite of model error and disturbance.
Bifacial Vertical Photovoltaic System Design for Farming Irrigation System Ghalia Nasserddine; Mohammad Nassereddine
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.3661

Abstract

The increase in population leads to an upsurge in food demands which also expend the agriculture activities. A wide range of electrically powered machines is essential for the success of modern agriculture setup.  Farming required electrical power for numerous activities such as irrigations and electric tractors. A large number of farms are located in remote areas where access to electricity could be costly. Also, farms that are located within the electrical grid suffer from the cost of electricity bills. In line with the United Nations' recommendations to deploy renewable energy sources for electrical power generations, photovoltaic systems are installed for farming activities across countries. A photovoltaic system converts solar radiation into electrical power and with the use of advanced power electronics devices, PV technologies become very attractive to farmers. The work in this paper capture the PV system operation for farming purposes. The contents cover standard PV panels and their current deployment layout for farms. The paper introduces the bifacial panel's concept and its novel layout. Furthermore, the paper proposed a novel installation layout for bifacial panels to support the farm electrical demands. The case study, which is based on three-phase irrigation pumps, explains and verifies the advanced role that the proposed layout of bifacial panels over the standard one. The advantages of the proposed installation layout are also included
Fault Classification in a DG Connected Power System using Artificial Neural Network Anshuman Bhuyan; Basanta K. Panigrahi; Subhendu Pati
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.3777

Abstract

Distributed generation is playing an important role in power system to meet the increased load demand. Integration of Distributed Generator (DG) to grid leads to various issues of   protection and control of power system structure.  The effect of the distributed generators to the grid is changes the fault current level, which makes the fault analysis more complex. From the different fault issues occurs in a distributed generator integrated power system, classification of fault remains as one of the most vital issue even after years of in-depth research. This paper emphasis on the classification of faults in DG penetrated power system using Artificial Neural Network (ANN). Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells, and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purpose.  Artificial neural network technique is utilized for the classification of fault in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration.
Performance of Anti-Lock Braking Systems Based on Adaptive and Intelligent Control Methodologies Ahmed J. Abougarair; Nasar Aldian A. Shashoa; Mohamed K. Aburakhis
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3794

Abstract

Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.
Traffic Occupancy Prediction Using a Nonlinear Autoregressive Exogenous Neural Network Nazhon Ismael Khaleel; Anuraj Uthayasooriyan; Joanna Hartley
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3833

Abstract

The main aim of the intelligent transportation systems is the ability to accurately predict  traffic characteristics like traffic occupancy, speed, flow and accident based on historic and real time data collected by these systems in transportation networks. The main challenge of  a huge quantity of traffic data collected automatically, stored and processed by these systems is the way of handling and extracting the required traffic data to formulate the prediction traffic characteristic model. In this research, the required traffic data of a specified road link in UK are extracted from the big raw data of the SCOOT system by designing C++ extractor program. In addition, short term traffic prediction models are created by using deep learning technique NARX neural network to find accurate and exact traffic occupancy. Three scenarios of time interval which are 10 minutes, 20 minutes and 30 minutes are considered for analyzing the prediction accuracy. The results showed that the prediction models for the 30 minutes interval scenario have very good accuracy in estimating the future traffic occupancy compared to another scenarios of time intervals. In addition, the testing and validation study showed that the prediction models for 30 minutes intervals for particular road link yield better accuracy than 10 minutes and 20 minutes intervals.
Performance Evaluation of Different GNSS Positioning Modes Brahim Seddiki; Abd El Mounaim Moulay Lakhdar; Bilal Beldjilali
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3719

Abstract

This paper gives a comparison of different GPS positioning modes using RTKLIB which is free and open-source software. The modes tested in this work are Single point positioning (SPP), precise point positioning (PPP), Satellite-based augmentation system (SBAS), Differential GPS (DGPS), and Real-Time Kinematic (RTK). The data for tests were obtained from NetR9 receivers, these types of receivers are multi-frequencies and multi-constellation receivers that provide carrier and phase measurements. The SPP mode is the very simplest mode, it can be used for applications where accuracy is not less than 5m, and it can be improved to achieve 1m by using SBAS corrections but only in the coverage area of the system. The DGPS can also provide 1m accuracy using a second receiver as a base station which can increase the cost of the operation. For applications that need very high accuracy, RTK and PPP can be used to reach centimeter-level accuracy. RTK needs a base station in addition to the rover receiver used for the positioning; PPP uses precise orbital and clock solutions which are not available in real time for all users.
Bone fracture detection through X-ray using Edge detection Algorithms Tatwamashi Panda; H.S. Pranavi Peddada; Annika Gupta; G Kanimozhi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3776

Abstract

Human beings are highly prone to bone fractures, to a great extent as an outcome of accidents or other factors such as bone cancer. Manual fracture detection takes a lengthy time and comes with a considerable chance of error. As a result, establishing a computer-based method to reduce fracture bone diagnosis time and risk of error is critical. The most common method for segmenting images based on sharp changes in intensity is edge detection. Sobel, Robert, Canny, Prewitt, and LoG (Laplacian of Gaussian) are some of the edge detection approaches that are examined for the study of bone fracture detection. The focal point of this paper is an endeavor to study, analyze and compare the Sobel, Canny, and Prewitt Techniques for detecting edges and identifying the fracture.

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