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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 25, No 1: January 2022" : 65 Documents clear
Coverage enhancements of vehicles users using mobile stations at 5G cellular networks Jaafar A. Aldhaibani; Mohanad S. Alkhazraji; Hasanain Lafta Mohammed; Abid Yaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp388-395

Abstract

High mobility requirements are one of the challenges face fifth-generation wireless (5G) cellular networks by providing acceptable wireless services to users traveling at speed up to 350 km/h. This paper presents a new scenario to increase the bit rate and coverage for passengers that use the vehicles for traveling through the installation a mobile station (MS) on these vehicles to provide a high-quality service to users. Based on signal to noise ratio (SNR’s) mathematical derivation and the outage probability of the user link, the proposed system is evaluated. Numerical results indicate an enhancement for users who received signal strength (RSS) from (-72 to -55) dBm and (15 to 38) Mbps in bit rate. Moreover, their number of users increased by proposed system adoption.
Segregation of oil palm fruit ripeness using color sensor Aiman Mustaffa; Faiz Arith; Nurin Izzati Fauzi Peong; Nurul Rafiqah Jaffar; Evelyn Larwy Linggie; Ahmad Nizamuddin Mustafa; Fara Ashikin Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp130-137

Abstract

Oil palm is an important industry that has contributed to income and support to the economic sector especially for Malaysia and Indonesia. However, most of the equipment in the oil palm industry is still operated manually. This work developed a system to separate bunches of oil palm fruit using color sensors according to maturity level. Fruit color plays a decisive point in determining fruit maturity. Here, a specific threshold point of red green blue (RGB) was obtained for the determination of the maturity level of oil palm fruit. Point values of < 120, 120 < x < 150 and > 150 represent the maturity levels of unripe, under ripe and ripe, respectively. This paper is the first to report the RGB points for use in the development of automated oil palm segregation system in the oil palm plantation industry. Thus, this paper will pave the way in producing an accurate and reliable oil palm separation system, which in turn has a positive effect in reducing human error. In the future, a set of sensors is proposed to detect a bunch of the oil palm fruits. This further can speed up the segregation process and more suitable for adaptation to the industry.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field Oussama Hamed; Mohamed Hamlich; Mohamed Ennaji
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp159-171

Abstract

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
Optimizing the effect of charging electric vehicles on distribution transformer using demand side management Swapna Ganapaneni; Srinivasa Varma Pinni
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp25-34

Abstract

This paper mainly aims to present the demand side management (DSM) of electric vehicles (EVs) by considering distribution transformer capacity at a residential area. The objective functions are formulated to obtain charging schedule for individual owner by i) minimizing the load variance and ii) price indicated charging mechanism. Both the objective functions profit the owner in the following ways: i) fulfilling their needs, ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding the overloading of distribution transformer. The proposed objective functions were worked on a residential area with 8 houses each house with an EV connected to a 20 kVA distribution transformer. The formulations were tested in LINGO platform optimization modeling software for linear, nonlinear, and integer programming. The results obtained were compared which gives good insight of EV load scheduling without actual price prediction.
Max stable set problem to found the initial centroids in clustering problem Awatif Karim; Chakir Loqman; Youssef Hami; Jaouad Boumhidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp569-579

Abstract

In this paper, we propose a new approach to solve the document-clustering using the K-Means algorithm. The latter is sensitive to the random selection of the k cluster centroids in the initialization phase. To evaluate the quality of K-Means clustering we propose to model the text document clustering problem as the max stable set problem (MSSP) and use continuous Hopfield network to solve the MSSP problem to have initial centroids. The idea is inspired by the fact that MSSP and clustering share the same principle, MSSP consists to find the largest set of nodes completely disconnected in a graph, and in clustering, all objects are divided into disjoint clusters. Simulation results demonstrate that the proposed K-Means improved by MSSP (KM_MSSP) is efficient of large data sets, is much optimized in terms of time, and provides better quality of clustering than other methods.
Customer churn analysis using XGBoosted decision trees Muthupriya Vasudevan; Revathi Sathya Narayanan; Sabiyath Fatima Nakeeb; Abhishek Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp488-495

Abstract

Customer relationship management (CRM) is an important element in all forms of industry. This process involves ensuring that the customers of a business are satisfied with the product or services that they are paying for. Since most businesses collect and store large volumes of data about their customers; it is easy for the data analysts to use that data and perform predictive analysis. One aspect of this includes customer retention and customer churn. Customer churn is defined as the concept of understanding whether or not a customer of the company will stop using the product or service in future. In this paper a supervised machine learning algorithm has been implemented using Python to perform customer churn analysis on a given data-set of Telco, a mobile telecommunication company. This is achieved by building a decision tree model based on historical data provided by the company on the platform of Kaggle. This report also investigates the utility of extreme gradient boosting (XGBoost) library in the gradient boosting framework (XGB) of Python for its portable and flexible functionality which can be used to solve many data science related problems highly efficiently. The implementation result shows the accuracy is comparatively improved in XGBoost than other learning models.
A simple faulted phase-based fault distance estimation algorithm for a loop distribution system Shwe Myint; Warit Wichakool
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp14-24

Abstract

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.
A new smart approach of an efficient energy consumption management by using a machine-learning technique Maha Yousif Hasan; Dheyaa Jasim Kadhim
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp68-78

Abstract

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
Characterization of silicon tunnel field effect transistor based on charge plasma Firas Natheer Abdul-kadir; Faris Hassan Taha
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp138-143

Abstract

The aim of the proposed paper is an analytical model and realization of the characteristics for tunnel field-effect transistor (TFET) based on charge plasma (CP). One of the most applications of the TFET device which operates based on CP technique is the biosensor. CP-TFET is to be used as an effective device to detect the uncharged molecules of the bio-sample solution. Charge plasma is one of some techniques that recently invited to induce charge carriers inside the devices. In this proposed paper we use a high work function in the source (ϕ=5.93 eV) to induce hole charges and we use a lower work function in drain (ϕ=3.90 eV) to induce electron charges. Many electrical characterizations in this paper are considered to study the performance of this device like a current drain (ID) versus voltage gate (Vgs), ION/IOFF ratio, threshold voltage (VT) transconductance (gm), and sub-threshold swing (SS). The signification of this paper comes into view enhancement the performance of the device. Results show that high dielectric (K=12), oxide thickness (Tox=1 nm), channel length (Lch=42 nm), and higher work function for the gate (ϕ=4.5 eV) tend to best charge plasma silicon tunnel field-effect transistor characterization.
Effective task scheduling algorithm in cloud computing with quality of service alert bees and grey wolf optimization Nidhi Bansal; Ajay Kumar Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp550-560

Abstract

Quality-based services are an indicative factor in providing a meaningful measure. These measures allow labeling for upcoming targets with a numerical comparison with a valid mathematical proof of reading and publications. By obtaining valid designs, organizations put this measure into the flow of technology development operations to generate higher profits. Since the conditions were met from the inception of cloud computing technology, the market was captured heavily by providing support through cloud computing. With the increase in the use of cloud computing, the complexity of data has also increased greatly. Applying natural theory to cloud technology makes it a fruit cream. Natural methods often come true, because survival depends on the live events and happenings, so using it in real life as well as any communication within technology will always be reliable. The numerical results are also showing a better value by comparing the optimization method. Finally, the paper introduces an adaptation theory with effective cloudsim coding of honey bees and grey wolf in conjunction with their natural life cycle for solving task scheduling problems. Using adapted bees improved the results by 50% compared with the original bees and secondly by honeybees and grey wolf improved 60%.

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