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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 9,138 Documents
 892   893   894   895   896 
Digital and academic libraries through cloud computing Sivanandham, Karthika; John, Dominic; Sivankalai, Sivankalai
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp896-905

Abstract

In an era characterized by the dominance of digital information, libraries have undergone significant transformations, evolving from traditional brickand-mortar institutions to dynamic hubs of digital knowledge. The emergence of digital libraries, which give users access to vast collections of digital resources, has facilitated this evolution. However, effective management of digital resources poses numerous challenges, including issues related to storage, preservation, and accessibility. In response, cloud computing has developed as a powerful solution for addressing these challenges and revolutionizing how libraries operate. Cloud computing reduces the need for expensive infrastructure expenditures and increases flexibility and scalability by allowing libraries to store, manage, and access digital resources remotely over the internet. This paper examines the intersection of digital libraries and cloud computing, examining the role of cloud computing in modern libraries and its implications for the future of information management. By analyzing current trends, case studies, and best practices, this paper provides insights into the benefits and challenges of adopting cloud computing in the context of academic libraries.
Identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering Rajendrakumar, Shiny; Rajashekarappa, Rajashekarappa; Parvati, Vasudev K.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1100-1108

Abstract

Plant disease diagnosis is crucial for preventing productivity and quality losses in agricultural products. Because plants are continually attacked by insects, bacterial infections, and smaller scale organisms it is necessary for early diagnosis disease control is a vital part of profitable chilli crop production, hence early diagnosis of disease identification is an important aspect of crop management. This paper discusses strategies for detecting disease effectively in order to improve chilli plant product quality. An image processing technique based on identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering (KMC). The approach was carried out in five stages: acquiring the image, preprocessing, extracting features, classifying the diseases, and showing the outcome. This work offers a thorough implementation of CLAHE for preprocessing, k-means cluster for feature extraction and support vector machine (SVM) for classification of chilli leaf diseases. The accuracy was tested for standard chilli dataset for major 2 types of diseases including anthracnose and bacterial blight form kaggle dataset with varying samples of 70:30 and 60:40 respectively and it is observed that the average accuracy improved to 98% compared to existing techniques.
Analytical study of a single slope solar still: experimental evaluation Prakash Sharma, M. Bhanu; Perumal, D. Arumuga; Sundari, M. S. Sivagama; Karuppasamy, Ilango
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp850-859

Abstract

Even though water covers the surface of the Earth in three quarters, many nations face shortages of drinkable water due to rapid global population and industrial growth. Solar power emerges as an efficient solution, particularly in hot climates with water and energy scarcity. This research focuses on a practical solar solution known as a solar still, a basic apparatus designed to convert available salty water into potable water. In this study, a single-slope solar still using acrylic material is experimentally analysed, predicting daily distillate production under varying climatic conditions. Using heat and solar radiation, solar distillation offers a simple, affordable, and small-scale approach to clean water production. The solar still, utilizing acrylic sheets as a basin material, minimizes heat losses and enhances water evaporation rates, making it a promising technology for addressing water scarcity issues. The experimental analysis results revealed a distillate output of 420 ml per 0.49 m² per day.
Prediction of Parkinson's disease using feature selection and ensemble learning techniques T. D., Sharan; Joshi, Sujata
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1736-1744

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that significantly impacts quality of life and healthcare systems. Early detection is crucial for timely interventions that can mitigate disease progression and improve patient outcomes. This study leverages advanced machine learning (ML) techniques to detect PD using speech features as non-invasive biomarkers. A dataset containing 754 features derived from sustained vowel phonations of 252 individuals (188 PD patients, 64 healthy controls) was analyzed. The dataset, originally collected by Istanbul University and publicly hosted via the UCI ML repository, was accessed through Kaggle for preprocessing and analysis. To identify the most predictive features, we employed recursive feature elimination (RFE), random forest importance, lasso regression, and the boruta algorithm—ensuring robust feature selection while reducing dimensionality. The XGBoost model, optimised using synthetic minority oversampling technique (SMOTE) for class balancing, achieved an accuracy of 96.69%, a recall of 96%, and an F1-score of 98%. Model robustness was validated through 5-fold cross-validation, yielding an average accuracy of 89.54%. These findings establish a scalable, costeffective, and non-invasive framework for early PD detection, demonstrating the potential of speech analysis and ML in neurodegenerative disease management.
IoT-based real-time monitoring of river water quality: a case study of the Selangor River Ahmad Jafri, Nur Aqilah; Markom, Arni Munira; Yusof, Yusrina; Burham, Norhafizah; Markom, Marni Azira
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1541-1552

Abstract

Monitoring river water quality is crucial for preserving freshwater ecosystems, ensuring public health, and supporting resource management. Traditional methods, while accurate, lack the scalability and real-time capabilities needed for proactive intervention. This study introduces an IoT based water quality monitoring system for the Selangor River, integrating sensors for pH, temperature, turbidity, and total dissolved solids (TDS) with a NodeMCU ESP32 microcontroller. To complement the IoT system, a handheld test pen was used to measure salinity and electrical conductivity (EC), offering additional insights into water quality. Field tests at four stations along the river revealed significant spatial variations. Station 1, near the river mouth, showed high salinity, EC, and TDS, indicating saltwater intrusion, with relatively low turbidity. Stations 2 and 3 recorded the highest turbidity levels, suggesting sedimentation and upstream activities, with moderate salinity and EC. Station 4, upstream, demonstrated stable freshwater characteristics, with low salinity, EC, and turbidity levels. The IoT system reliably monitored real-time parameters, and its measurements were validated against those from the handheld test pen. Minor discrepancies in TDS and temperature readings highlighted the importance of calibration.
Microservices caching for container-based IoT system in the edge and cloud Qasha, Rawaa; Sulyaman, Haleema
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1652-1660

Abstract

Microservices enable agile development by dividing internet of things (IoT) programs into autonomous components, ensuring fault tolerance and parallel operation for enhanced productivity. Their adaptability across diverse service types and applications improves IoT system performance. On the other hand, the container is the preferred solution for microservices-based enterprises. To improve the effectiveness of the deployment system presented in our paper 1, we developed a new caching technique to significantly optimize the performance of the deployment system and automate the sharing and re-using of ready-to-run microservices that have been packaged as Docker images. The new caching techniques are seamlessly integrated with our deployment system to optimize the microservices caching of the IoT application by utilizing Docker-based container virtualization and Redis for consistent data sharing. In addition, DevOps and versioning tools such as GOCD and GitHub are integrated into our system to enhance the automatic deployment of the microservices resulting in self-contained, portable, and repeatable IoT microservices. The effectiveness of the proposed techniques is evaluated via various experiments implemented in various working environments where the results show reduced deployment time and the effort required to re-execute the microservices, in addition to the reduction of burden and error that occur when adopting a manual deployment.
Design and optimization strategy of HAWT using a local pitch angle adaptation Meghlaoui, Issam; Layadi, Toufik Madani
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1467-1479

Abstract

In this paper, a smart design of a horizontal wind turbine (HAWT) has been developed. The developed design allows improving kinetic energy recuperation with high efficiency. The considered design of the wind turbine is characterized by a specific mechanical structure of blades. Each blade contains separated elements with an adaptable local pitch angle. To develop the smart wind turbine, a new algorithm for controlling the blade elements has been implemented. It allows estimating the distribution of the twist angle of each blade element. The achieved twist angle corresponds to the extracted optimal power provides by the wind turbine. The obtained results show a significant improvement relative to the wind turbine power coefficient under operating conditions. In fact, for some rotating velocity, the rate of this coefficient is increased by 21%. Moreover, notable kinetic energy recuperation is observed. Furthermore, smart orientation of elements proved optimal energy recuperation for a large scale of tip speed ratio and wind speed. In addition, the proposed structure of the wind turbine is more beneficial to minimize the axial thrust. Furthermore, the axial thrust of the wind turbine has been decreased by 21% for some operating velocity and specific conditions. As perspectives for the future works many ideas are suggested.
A hybrid approach to behavioral spam review detection on e-commerce platforms using apriori and CNN Wayal, Ganesh; Bhandari, Vijay
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1837-1845

Abstract

Spam reviews significantly undermine the credibility of online review systems on e-commerce websites. This paper presents a hybrid methodology that combines the Apriori algorithm and convolutional neural networks (CNN) to efficiently identify and mitigate spam reviews. By examining user behavior, including activity patterns, reviewer reputation, temporal dynamics, and sentiment consistency, we propose a comprehensive model for understanding user interactions and engagement. To extract important information and build precise spam detection models, we use data mining and machine learning approaches. Furthermore, contextual and domain-specific analyses are conducted to improve detection strategies. The study highlights the significance of hybrid techniques in preserving the integrity of e-commerce platforms through successful industry implementations and presents evaluation metrics, problems, and future research objectives.
CNN-GRU based cyber-attack classification and detection with the CICIDS-2017 dataset using optimization algorithm for honey badger Mahesh, Katikam; Rao, Kunjam Nageswara
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1765-1775

Abstract

The sheer volume of data exchanged has grown through information and communications technology (ICT) swiftly growing importance since the attackers benefit from illegal access to network data and introduce possible dangers for data theft or alteration. It is considered a significant barrier to monitor the network traffic for cyber-attack detection and classification with alarm ring to inform to network administrator. With KDD-CUP99, conventional machine learning methods like deep neural network (DNN), a kind of artificial neural network (ANN), cannot detect and classify novel attacks types and lacks clarity regarding accuracy. The CICIDS 2017 dataset, which is improved in this study, serves as training data for the model and useful framework that combines a hybrid convolutional neural network (CNN) with the gated recurrent unit (GRU) technique. The primary aim of this effort is to classify different security attacks and classify cyberthreats with honey badger optimization algorithm (HBOA). To strengthen the performance criteria for various assault types, such as F1-score, recall, precision, and others, the HBOA is utilized to modify the model parameters high-level features ought to be extracted from the network data using the hybrid model assessed and verified by simulation studies. The detection and classification output from the CNN-GRU model, which detects different security threats with greater accuracy of 94%.
Optimizing energy efficiency and improved security in wireless sensor networks using energy-centric MJSO and MACO for clustering and routing Kalaskar, Srinivas; Bhyri, Channappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1964-1975

Abstract

Wireless sensor networks (WSNs) play a pivotal role in various applications, but their energy-constrained nature poses significant challenges to their sustainable operation. In this paper, we propose a novel approach to enhance energy efficiency in WSNs by leveraging energy-centric multi-objective jaya search optimization (MJSO) and multi-objective ant colony optimization (MACO) for clustering and routing. Our method aims to address the energy consumption issues by optimizing clustering and routing strategies simultaneously. The energy-centric MJSO algorithm is employed to intelligently organize sensor nodes into clusters, considering energy consumption, network coverage, and connectivity. The multi-objective MACO algorithm optimizes routing paths by balancing energy consumption and network lifetime objectives. Through integration and simulations, the approach enhances energy efficiency in WSNs for various applications like environmental monitoring and smart cities, advancing energy-efficient clustering and routing. By integrating energy-centric MJSO and MACO into clustering and routing protocols, WSNs can achieve significant improvements in energy efficiency and security while maintaining reliable communication and data delivery.

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