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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
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Processing queries on encrypted document-based database Belhaj, Abdelilah; Ziti, Soumia; Elbouchti, Karim; Falih, Noureddine; Lagmiri, Souad Najoua
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.pp1299-1309

Abstract

Big  data is a set of technologies and strategies for storing and analyzing large volumes of data in order to learn from it and make predictions. Since non-relational databases such as document-based have been applied in various contexts, the privacy protection must be taken into account by strengthening security to prevent the exposure of user data. In this paper, we focus mainly on secret sharing scheme that supports secure query with data interoperability to design a practical model for document-based databases, especially MongoDB. This approach, being based on secure query processing by defining elementary and suitable operators, allows us to perform operational computations and aggregations on encrypted data in the non-relational document database MongoDB. The obtained results, in the present work, could find places in various fields where data privacy and security are primordial such as healthcare, cloud computing, financial services, artificial intelligence and machine learning, in which user data remains secure and confidential during processing.
An approach-based ensemble methods to predict school performance for Moroccan students Maiti, Abdallah; Abarda, Abdallah; Hanini, Mohamed
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.pp1211-1220

Abstract

Education is a key factor in Morocco's development, with school performance serving as a critical measure of the education system’s quality. However, disparities in student outcomes remain, influenced by socioeconomic, demographic, and infrastructural factors. Our study aims to develop a predictive model to assess and improve school performance in Morocco using ensemble machine learning techniques, focusing on the stacking approach. Data from the Massar platform includes variables such as gender, age, type of school, parental occupation, academic results, and residential area. After rigorous data cleaning and preprocessing, a stacking model was created by combining predictions from five base models: random forest, gradient boosting, k-nearest neighbors (KNN), support vector machine (SVM), and multi-layer perceptron (MLP). A random forest metamodel was used to integrate these results. The experimental results of the paper demonstrate the effectiveness of our approach. The stacking model achieved an accuracy of 78.70%, surpassing the individual base models. The meta-model demonstrated strong reliability, achieving an F1 score of 78.62% while reducing false negatives and ensuring balanced predictions. Among the base models, neural networks showed the best performance, achieving the highest predictive accuracy. This research highlights the potential of stacking methods for predicting school performance. Incorporating additional variables, such as parental education and teacher attributes, could further refine the model and enhance Morocco’s educational outcomes.
Clustering technique for dense D2D communication in RIS-aided multicell cellular network Susanto, Misfa; Sabella, Soraida; Hakim, Lukmanul; Kurnianto, Rudi; Abd Aziz, Azrina
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.pp927-940

Abstract

Device-to-device (D2D) communication and reconfigurable intelligent surface (RIS) are well-known as two promising technologies for nextgeneration cellular communication networks. D2D users operate on the same spectrum as traditional cellular users, potentially leading to increased interference and reduced efficiency in frequency resource usage. RIS provides a remedy for clearing blocked signals from obstructions by reflecting the desired signals to the intended receiver. However, RIS elements reflect not only the desired signals but also the interference signals. This paper proposes a distance-based clustering method aimed at creating a grouping algorithm for neighboring D2D users using different channels, thereby reducing co-channel interference. The simulation indicates that the proposed clustering method for D2D users' equipment (DUEs) leads to a 0.72 dB increase in signal-to-interference-plus-noise ratio (SINR), enhances throughput to 11.25 Mbps, and reduces the bit error rate by up to 24×10⁻² compared to the baseline system. The study findings also indicate that cellular users' equipment (CUEs) experience satisfactory signal quality, even with the presence of DUEs on the cellular network. Our clustering algorithm is feasible to deploying D2D densely in RIS-aided cellular network without significantly affecting CUE performance.
Wolfram Alpha based-inventory model for damaged items of pharmaceutics by utilizing exponential demand rate Indrawati, Indrawati; Puspita, Fitri Maya; Supadi, Siti Suzlin; Yuliza, Evi; Tampubolon, Farah Nabilah
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.pp1145-1154

Abstract

In this study, an inventory model is developed for pharmaceutical products that deteriorate over time with an exponential demand rate. The discussion of exponential demand is rarely explored but has the advantage that the demand value toward total cost remains positive. This study assumes allowable shortages and complete backlogging, making it necessary to design an optimal policy for deteriorating goods with an exponential demand rate. The model shows that the initial stock decreases over time, potentially leading to shortages before the next order arrives. The optimal solution indicates that the inventory reaches the zero point at ????1 = 0.0000011 and the cycle length ????1 = 0.012 resulting in an average minimum total cost of ????????̅̅̅̅ = $17,133.9 per cycle by Wolfram Alpha. Sensitivity analysis measures the changes of the results in the increasing value of ????????̅̅̅̅ for all parameters. Exponential function variables (???? and ????) produces ????1 and ????1 stable values. On increasing the cost of each damage (????????) and constant damage rate (????) produces a ????1 stable value, but the value of ????1 increases. An increase in storage costs (h) results in a decrease in the value of ????1 and ????1. Increasing in the cost of shortages (s) resulted in an increase in the value of ????1 and a decrease in the value of ????1.
Dynamic attendance system using face recognition via machine learning models Upadhyay, Nishant; Bansal, Nidhi; Velinov, Emil; Harshit, Harshit; Sharma, Abhay; Kumar, Sanjeev
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.pp1421-1430

Abstract

Traditional methods to handle attendance have been implemented in the schools in the past and most of them are discouraging as they require that the institutions implement the use of paper and pen to get the results. To enhancing effectiveness and safeguarding, this paper presents a face recognition attendance system that mechanizes the usual attendance taking process. Using best practices in facial recognition, the system captures images of students’ faces, stores them, feeds them into a recognition model, and uses real-time facial recognition to mark attendance. This means that the system enjoys data encryption and password protected access that ensures data is safe. In the proposed system, the OpenCV face recognition libraries combined with machine learning algorithms for better face recognition ability with better efficiency. The results confirm that the system provides a reliable approach to handling attendance and it may debut in various contexts.
A multi-tier framework of decentralized computing environment for precision agriculture (DCEPA) Panduranga, Kiran Muniswamy; Ranganathasharma, Roopashree Hejjaji
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.pp1072-1080

Abstract

Although collecting enormous volumes of heterogeneous data from many sensors and guaranteeing real-time decision-making are problems, precision agriculture (PA) has emerged as a promising approach to increase agricultural efficiency. The efficacy of current centralized solutions is limited in large-scale agricultural settings due to resource limitations and data saturation. In order to solve these problems, this paper suggests a decentralized computing environment for precision agriculture (DECPA), which divides resource management and data processing among several layers (end, edge, and cloud). DECPA optimizes task execution and resource allocation in the field by utilizing ensemble machine learning models (deep neural network (DNN), long short-term memory (LSTM), autoencoder (AE), and support vector machine (SVM)) and a multi-tier architecture. The findings demonstrate that DECPA combined with DNN performs better than alternative models, achieving a 20% decrease in energy usage, an 18% speedup in response time, a 5% improvement in accuracy, and a 51% reduction in latency. This illustrates the system’s capacity to manage massive amounts of data effectively while preserving peak performance. To sum up, DECPA uses decentralized resources and cutting-edge machine learning models to provide a scalable and affordable precision agriculture solution. To improve the system’s flexibility and real-time responsiveness, future research will investigate additional optimization and use in various agricultural contexts.
Recognizing AlMuezzin and his Maqam using deep learning approach Shatnawi, Nahlah Mohammad; Nahar, Khalid M. O.; Al-Issa, Suhad; Alikhashashneh, Enas Ahmad
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.pp1360-1372

Abstract

Speech recognition is an important topic in deep learning, especially to Arabic language in an attempt to recognize Arabic speech, due to the difficulty of applying it because of the nature of the Arabic language, its frequent overlap, and the lack of available sources, and some other limitations related to the programming matters. This paper attempts to reduce the gap that exists between speech recognition and the Arabic language and attempts to address it through deep learning. In this paper, the focus is on Call for Prayer (Aladhan: ناذآلا ) as one of the most famous Arabic words, where its form is stable, but it differs in the notes and shape of its sound, which is known as the phonetic Maqam (Maqam: ماقملا  يتوصلا ). In this paper, a solution to identify the voice of AlMuezzin ( نذؤملا ), recognize AlMuezzin, and determine the form of the Maqam through VGG-16 model presented. The VGG-16 model examined with 4 extracted features: Chroma feature, LogFbank feature, MFCC feature, and spectral centroids. The best result obtained was with chroma features, where the accuracy of Aladhan recognition reached 96%. On the other hand, the classification of Maqam with the highest accuracy reached of 95% using spectral centroids feature.
Optimization of IoT-based monitoring system for automatic power factor correction using PZEM-004T sensor Somantri, Maman; Fauzan, Mochamad Rizal; Surya, Irgi
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.pp860-873

Abstract

Power factor correction (PFC) is crucial for improving energy efficiency and reducing excessive power consumption, especially in inductive loads commonly found in household and industrial environments. Conventional PFC methods often rely on manual capacitor switching, which is inefficient and impractical for real-time applications. This study proposes an IoT-based automatic power factor monitoring and correction system that dynamically adjusts the power factor using real-time data analysis. The system integrates NodeMCU ESP32 and the PZEM-004T sensor to monitor electrical parameters and automatically switch capacitors based on power factor conditions. The research follows the ADDIE approach (analysis, design, development, implementation, evaluation) to ensure a structured development process. Experimental results demonstrate an average power factor improvement of 48.77% and a reduction in current consumption by 39.90%, significantly enhancing energy efficiency. The system's web-based interface allows real-time monitoring with an average data transmission response time of 207.67 ms, ensuring efficient remote management. Compared to existing systems, the proposed approach eliminates manual intervention and optimizes PFC adaptively. Future research should focus on expanding system reliability, testing on larger-scale applications, and integrating artificial intelligence (AI) for predictive power factor adjustments.
Efficiently tracking and recognition of human faces in real-time video stream with high accuracy and performance Khan, Imran Ulla; Raja, D. R. Kumar
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.pp1261-1268

Abstract

Real time tracking and recognition of human faces in video streams is a critical challenge in computer vision. Existing systems often struggle to balance accuracy and performance, particularly in dynamic environments with varying lighting conditions, occlusions, and rapid movements. High computational overhead and latency further hinder their deployment in realworld applications. These limitations underscore the need for a robust solution capable of maintaining high accuracy and real-time efficiency under diverse conditions. This research addresses these challenges by developing a deep learning-based system that efficiently tracks and recognizes human faces in real-time video streams. Proposed system integrates advanced face detection models you only look once version 5 (YOLOv5) with state-of-theart tracking algorithms, such as deep simple online and real time tracking (SORT), to ensure consistency and robustness. By leveraging graphics processing unit (GPU) acceleration, the system achieves optimal performance while minimizing latency. Multi-frame analysis techniques are incorporated to enhance accuracy in detecting and recognizing faces, even under challenging conditions such as partial occlusions and motion blur. Developed system has broad applications across multiple domains, including surveillance and security, where it can enhance real-time monitoring in crowded environments for seamless face tracking in interactive systems. By focusing on efficiency, robustness, and adaptability this work offering a scalable and high-performance solution for real-time human face tracking and recognition.
Hierarchical enhanced deep encoder-decoder for intrusion detection and classification in cloud IoT networks K. M., Ramya; Biradar, Rajashekhar C.
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.pp1176-1188

Abstract

Securing cloud-based internet of things (IoT) networks against intrusions and attacks is a significant challenge due to their complexity, scale, and the diverse nature of connected devices. IoT networks consist of billions of devices, computer servers, data transmission networks, and application computers, all communicating vast amounts of data that must adhere to various protocols. This study introduces a novel approach, termed hierarchical enhanced deep encoder-decoder with adaptive frequency decomposition (HED-EDFD), and is designed to address these challenges within cloud-based IoT environments. The HED-EDFD methodology integrates adaptive frequency decomposition, specifically adaptive frequency decomposition, with a deep encoder-decoder model. This integration allows for the extraction and utilization of frequency domain features from time-sequence IoT data. By decomposing data into multiresolution wavelet coefficients, the model captures both high-frequency transient changes and low-frequency trends, essential for detecting potential intrusions. The deep encoder-decoder model, enhanced with deep contextual attention mechanisms, processes these features to identify complex patterns indicative of malicious activities. The hierarchical structure of the approach includes a hierarchical wavelet-based attention mechanism, which enhances the accuracy and robustness of feature extraction and classification. To address the issue of imbalanced intrusion data, a cosine-based SoftMax classifier is employed, ensuring effective recognition of minority class samples.

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