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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 9,174 Documents
 765   766   767   768   769 
Adaptive tuning of PID using chef‑based optimization algorithm for improving automatic voltage regulator Widi Aribowo; Reza Rahmadian; Mahendra Widyartono
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1215-1223

Abstract

This  article  presents  the  proportional-integral-derivative  (PID)  parameter tuning  on  the  automatic  voltage  regulator  (AVR)  using  the chef-based optimization   algorithm   (CBOA).   CBOA   is   modeling   cooking   training activities consisting of students and young chefs in an effort to mature cooking skills.  This  article  uses  other  methods  as  a  comparison  in  measuring  the performance  of  the  proposed  method.  The  methods  used  are grasshopper optimization algorithm (GOA) and cooperation search algorithm (CSA). The simulation results show that the proposed method, namely CBOA, has a better ability in the peak value of overshoot, which is 0.232% compared to the CSA method and 12.99% compared to the GOA method.
Augmented reality for anatomy course for children with autism Misael Lazo-Amado; Laberiano Andrade-Arenas
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1246-1257

Abstract

Autism spectrum disorder (ASD) that mainly affects social interaction and effective communication, showing problems in their learning, reflected in the lack of attention in schools, such as in anatomy classes. The main objective of the project is to develop a mobile application for children with autism to improve their learning in Anatomy and social interaction through augmented reality (AR). The methodology to be used is ADDIE which is in charge of analyzing the experience of the work team to find the ease of software development, proposing the efficient development with continuous improvement according to the evaluation to the specialists. The results show the evaluation of the specialists on the mobile application with AR where they will indicate their satisfaction with the application. In conclusion, it will be shown a mobile application with AR that offers a technological and eye-catching novelty for users in order to improve the educational development of children with autism spectrum disorder.
Authentication schemes in wireless internet of things sensor networks: a survey and comparison Pendukeni Phalaagae; Adamu Murtala Zungeru; Boyce Sigweni; Selvaraj Rajalakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1876-1888

Abstract

The proliferation of wireless sensor networks (WSNs) fuels internet of things (IoT's) rapid global development, connecting diverse devices. IoT transforms devices into intelligent entities delivering exceptional services. This work addresses IoT authentication gaps through a comprehensive survey, analyzing recent works and exploring techniques in various applications. It includes a comparative analysis of authentication schemes, evaluating Bi-Phase authentication scheme (BAS) in WSNs. BAS outperforms sensor protocol for information via negotiation (SPIN), broadcast session key protocol (BROSK), and localized encryption and authentication protocol (LEAP), resulting in lower energy consumption and higher efficiency. With energy efficiency at 60 Kb/J for 25 nodes, BAS focuses on power optimization and lightweight security measures, reducing energy consumption, maximizing efficiency, and extending WSN lifespan. The evaluation, conducted using MATLAB/Simulink, demonstrates BAS's superiority, achieving 10 J, 12 J, 14 J, and 15 J energy consumption for 25 nodes during simulation, showcasing its effectiveness and future potential in advancing IoT authentication.
Hybrid RIS-assisted interference mitigation for heterogeneous networks Soumana Hamadou, Abdel Nasser; wa Maina, Ciira; Soidridine, Moussa Moindze
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp175-190

Abstract

Reconfigurable intelligent surfaces (RIS) have evolved as a low-cost and energy- efficient option to increase wireless communication capacity. In this research, we suggest using hybrid RIS (H-RIS) to reduce interference in heterogeneous networks (HetNet). In contrast to traditional passive RIS, a hybrid RIS is suggested, which is fitted with a few active elements to not only reflect but also amplify incident signals for a significant performance increase. By jointly optimising the passive and active coefficients of the H-RIS, we aim to maximise the rate of the small cell user (SUE). We presented an effective alternating optimisation (AO)-based phase shift matrix coefficients (AO-PMC) technique to tackle this problem by iteratively optimising these variables because the optimisation problem is not convex. The simulation results demonstrate that, in comparison to the passive RIS-assisted HetNet scheme and the scheme without RIS, the suggested scheme, with just 8% of active elements, can enable HetNet to gain superior spectral efficiency (SE) and energy efficiency (EE). The outcomes also demonstrate that, in the majority of the cases taken into account, H-RIS can outperform the active RIS-assisted HetNet scheme.
Model development of bond graph based wind turbine Sugiarto Kadiman; Oni Yuliani
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp715-726

Abstract

Just recently, wind energy continues to be one of most important renewable energy resources, by cause of its production is ecologically friendly; for that reason, the technology built for the renewable energy production by way of wind turbines takes excessive challenges in the study. Due to several physical domains prevailing in wind turbine, namely aerodynamical, mechanical and electrical, the modeling of wind turbine is problematic; thus, modeling based on physical techniques has a superior reliability in these circumstances. One of these approaches is bond graph modeling that model system evolved from conservation law of both of mass and energy comprising in the structures. This study presents modeling the parts of bond graph-based wind turbine. Then, sub models are connected together to attain the entire model of wind turbine for simulation based on 20-Sim software. The proposed wind turbine is 2.5 kW of variable velocity wind turbine with three blades, gearbox, tower, and doubly-fed induction generator type. The effectiveness of bond graph modeling system on wind turbine has been proven in simulation results.
Poultry disease early detection methods using deep learning technology Liu Yajie; Md Gapar Md Johar; Asif Iqbal Hajamydeen
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1712-1723

Abstract

Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.
Automatic translation from English to Amazigh using transformer learning Otman Maarouf; Abdelfatah Maarouf; Rachid El Ayachi; Mohamed Biniz
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1924-1934

Abstract

Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.
Optimization and analysis of distributed generation units in distributed system for minimizing losses Bharath Suriyakumar; Vasuki Arumugam
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp31-39

Abstract

The proposed study presents a novel methodology aimed at mitigating losses in distributed generation (DG) systems within distributed networks. This methodology involves the integration and implementation of DG units that utilize non-conventional, sustainable resources, potentially enhancing traditional DG systems. When DG units are located near the point of consumption, they create favorable conditions for voltage support, reduction in energy losses, and lower emissions. The strategic placement of DG units, in terms of both size and location within an existing generation network, is critical for the construction, execution, and operational planning of real-time distribution networks. This optimal positioning is key to maximizing voltage stability and minimizing power loss. The study proposes an innovative strategy to decrease real power losses and improve voltage profiles, which includes optimizing substation capacity by introducing DG units.
Automatic detection and prediction of signal strength degradation in urban areas using data-driven machine learning El Moudden, Ibrahim; Benmessaoud, Youssef; Chentouf, Abdellah; Cherrat, Loubna; Mohammed Rida, Ech-Charrat; Ezziyyani, Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp958-970

Abstract

Signal strength degradation represents a variation in the coverage area for radio networks. Building maps that represent this degradation requires collecting information about signal coverage in scattered locations, which can be done conventionally by measurement methods such as the manual drive test. Nevertheless, as this process is large-scale, time-consuming, and costly, several methods for the minimization of drive tests have been introduced. In this study, our methodology first consisted of dividing the study area into several zones, and each zone into several sectors without considering the position of the existing broadcast base station. Then, we developed a custom mobile application to collect the signal strength data and the location coordinates of the concerned area. For data collection, we deployed the mobile application on more than 10 users' phones, who navigated in different areas using their cars. We applied the gradient-boosted trees algorithm to predict signal strength degradation in different areas. Our model has shown some interesting results after studying and analyzing the collected data, based on data mining algorithms. We also evaluated our model's ability to predict the zone's structure according to the strength of the degradation signal.
Predicting likelihood of fraud among financial distressed firms in Malaysia using textual analysis Marziana Madah Marzuki; Syerina Azlin Md Nasir; Siti Fadilah Mat Zain; Nik Siti Madihah Nik Mangsor
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1620-1631

Abstract

This research paper aims to analyze and predict fraud patterns among failed companies in Malaysia. The approach involves utilizing textual analysis on the management discussion and analysis (MD&A) section within the annual reports. The dataset is subjected to text clustering to group companies based on similar financial characteristics. This clustering process entails several steps, including data conversion, collation, and summarization into a structured format, followed by text pre-processing to cleanse the dataset. Notably, RapidMiner Studio software was utilized to extract data for the study. Subsequently, the documents are clustered using both the K-means and latent dirichlet allocation (LDA) methods. Upon examining a sample of 22 failed companies in the year 2020, the study reveals that financially distressed companies exhibit prominent financial negativity and utilize litigious financial terms within their MD&A sections. These linguistic traits are found to be closely associated with seven distinct characteristics of fraudulent firms. This preliminary findings provide compelling evidence that financial pressure may serve as a triggering factor for fraudulent activities within companies.

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