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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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Big data clustering based on spark chaotic improved particle swarm optimization Saida Ishak Boushaki; Brahim Hadj Mahammed; Omar Bendjeghaba; Messaoud Mosbah
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.pp419-429

Abstract

In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.
A proposed semantic keywords search engine for Indonesian Qur’an translation based on word embedding Trisnawati, Liza; Binti Samsudin, Noor Azah; Bin Ahmad Khalid, Shamsul Kamal; Bin Ahmad Shaubari, Ezak Fadzrin; Sukri, Sukri; Indra, Zul
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.pp987-995

Abstract

Obtaining relevant information from the Holy Qur’an can be really challenging for people who cannot speak Arabic, such as the Indonesian people. One technology implementation which is commonly used to tackle this problem is to develop a search engine application for Al-Qur’an verses. This paper proposes a search engine based on semantic representation keywords for the Indonesian translation of the Al-Qur’an which consists of 3 phases i.e., data preparation, document representation, and search engine development. In the first stage, the Al-Qur’an dataset was built using the official translation of the Al-Qur’an from the Ministry of Religion and then enriched with the Wikipedia corpus. The second phase is document representation which produces feature vectors by utilizing the Word2Vec algorithm. Finally, the development of a search engine that can find the most relevant verses by calculating the cosine similarity between the document and the keywords. It was found that the proposed search engine succeeded in exceeding the performance of ordinary search engines by finding wider information due to the use of semantic keywords. Apart from that, the proposed search engine succeeded in maintaining the relevance of search results by achieving precision and recall levels of 98.7% and 97.3% respectively.
Real-time forest fire detection, monitoring, and alert system using Arduino Afiq Ikhwan Mohd Anuar; Roslina Mohamad; Arni Munira Markom; Ronnie Concepcion II
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.pp942-950

Abstract

Early fire detection is critical to protecting forests from wildfires and enabling rapid responses to minimize fire spread. Existing forest fire detection methods cannot quickly detect forest fires and evaluate the fire risk of these sensitive areas. Hence, this research aims to develop a real-time forest fire detection, monitoring, and alert system. The development of the system started with assembling temperature and humidity sensors, a smoke sensor, an Arduino microcontroller, and a wireless fidelity module. Then, a fire monitoring and alert system was developed using Blynk. From the sensitivity flame sensor analysis with the fire, the flame sensor detected the presence of fire up to 60 cm. The sensor also indicated high temperature (45 °C) and low humidity (53.4%) at noon. Low temperature (29 ℃) and high humidity (88.4%) were identified in the morning. Moreover, the highest carbon dioxide (CO2) concentration of 1,800 ppm was recorded when the smoke from the fire was detected. The global positioning system module shows the accurate real-time location of the system displayed in the Blynk application. In conclusion, this system can detect and monitor early forest fires in real-time and can alert the authorities to protect forests from wildfires.
Multiple object tracking using space-time adaptive correlation tracking Kusuma Sriram; Kiran Purushotham
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.pp1805-1815

Abstract

In application of tracking and detecting the suspicious activities, multiple object tracking (MOT) has been given fine attention due to its application as it provides the parallel task of identification and tracking of human. MOT ensures the identification and trajectory for each object frame as they interact, despite the changes in its appearance, occlusion and various other tasks involved. Recent adoption of deep learning has given a new perspective but still achieving high metrics remains a major issue to overcome such issues, this research work presents the integrated architecture of deep convolutional covariance networks (DCCNs) and space-time adaptive correlation tracking (STACT) algorithm with similarity map function (SMF). Moreover, in proposed work, DCCNs is utilized for feature extractions through each frame capturing the distinctive information, STACT is tracking approaches that utilizes the SMF for locating and tracking objects. SMFs are updated for any changes in human appearances and motion, also it deals with occlusion. Here the proposed model is evaluated on MOT17 and MOT20 dataset. Performance analysis is carried out through comparing the existing model and Integrated-DCCN achieves higher metrics.
Effects of Pr3+ -activated BaZrGe3O9@TiO2 phosphor compound on light emitting diodes validated by computer simulation Le Thi Trang; Le Xuan Thuy; Nguyen Le Thai; Thuc Minh Bui
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.pp1482-1488

Abstract

The Pr3+ -doped BaZrGe39 gallogermanate phosphors are reported to have a well-defined successive deep defect structure that effectively mitigates thermal carrier fading. This phosphor also presents a red emission with a peak at 615 nm, originating from the Pr3+ transtition from 1D2 to 3H4. We investigated the impact of Pr3+ -activated BaZrGe3O9 (referred to as BZG:Pr) on the lighting characteristics of light emitting diodes (LED) packages in this paper. By combining BZG:Pr with TiO2 particles and silicone, we produced a phosphor layer (designated as BZG:Pr@TiO2). The optical performance of the resulting LED was systematically examined by varying the TiO2 doping percentage. Our findings reveal that the incorporation of the BZG:Pr phosphor enhances the red spectral component, thereby contributing to improved homogeneity in color distribution. However, a progressive increase in TiO2 content within the phosphor layer corresponds to diminishing luminous output and decreased chromatic rendering efficiency of the LED. Employing a lower concentration of TiO2 proves advantageous, as it capitalizes on the scattering-enhancing attributes while leveraging the red emission of the BZG:Pr phosphor. This synergistic approach yields a favorable balance between luminosity and color quality, enhancing the LED’s overall performance.
Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS Abdelamine Elouafi; Ilyas Tammouch; Souad Eddarouich; Raja Touahni
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.pp565-574

Abstract

The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classification algorithms. This study involves a comparative analysis of five algorithms: logistics regression, k-nearest neighbors (K-NN), support vector machine (SVM), decision tree, and random forest. They were applied to a set of data from TIMSS 2019 in Morocco, to determine their effectiveness in predicting student performance using prediction models such as logistics regression, KNN, SVM, decision-tree, and random forest, decision-makers can make data-driven decisions to enhance educational strategies and improve outcomes in mathematics education.
Long range based effective field monitoring system Popuri Rajani Kumari; Chalasani Suneetha; Vadlamudi Sri Lakshmi; Nakka Rama Priya; Bodapati Venkata Rajanna; Ambarapu Sudhakar
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.pp847-853

Abstract

Adoption of the internet of things (IoT) is moving forward quickly because of the developments in communication protocols and technology involving sensors. The IoT is promoting real-time agricultural field monitoring from any distant place. For the IoT to be implemented effectively there are a number of agricultural issues related to less power usage and long-distance transfer of data are to be addressed. By using LoRa, which is a wireless communication system for IoT applications, these difficulties can be avoided when sending information from fields of crops to a web server. Acustomized sensor node and LoRa are used in this work to transmit continuously updated information to a remote server. Monitoring the quality of water, and reducing wasteful use of water are the main goals.
Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing Radhakrishnan Murugesan; Satish Kanapala; Subash Rajendran; Prathaban Banu Priya; Rathinasabapathy Ramadevi; Natarajan Duraichi; Rengaraj Hema
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp380-388

Abstract

In wireless networks, link breaks, and restricted resources create fundamental challenges for maintaining network applications. Several wireless network routing techniques concentrate on power efficiency to expand the network lifetime, but the traffic and reliability parameters are not the primary concern. Though, these techniques are not capable of dealing with the wireless network. Hence, this paper proposes deep neural network (DNN) with a fuzzy algorithm to improve power and traffic-aware reliable reactive routing (PTAR) in wireless networks. The wireless network is formed by clustering by the node power and selects the cluster head (CH) based on a fuzzy algorithm. The wireless node power level, node buffer space, and node reliability to consider the input parameters of the fuzzy system. Then thefuzzy algorithm gives the output for CH round length. This selected CH improves the node reliability, power efficiency with minimized network congestion. Then we use a DNN algorithm to choose an optimal relay by applying an adaptive load balance factor in the network. DNN is a machine learning algorithm, and it provides high accuracy. From the simulation results, the PTAR approach improves the network performance, such as packet received ratio, delay, residual energy, and routing overhead.
Development and implementation of a Python functions for automated chemical reaction balancing Pankaj Dumka; Rishika Chauhan; Dhananjay R. Mishra; Feroz Shaik; Pavithra Govindaraj; Abhinav Kumar; Chandrakant Sonawane; Vladimir Ivanovich Velkin
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.pp1557-1565

Abstract

Chemical reaction balancing is a fundamental aspect of chemistry, ensuring the conservation of mass and atoms in reactions. This article introduces a specialized Python functions designed for automating the balancing of chemical reactions. Leveraging the versatility and simplicity of Python, the module employs advanced algorithms to provide an efficient and user-friendly solution for scientists, educators, and industry professionals. This article delves into the design, implementation, features, applications, and future developments of the Python functions for automated chemical reaction balancing. The functions thus developed were tested on some typical chemical reactions and the results are the same as that in the literature.
Exploring corpus linguistics via computational tool analysis: key finding review Wan Nur Aida Sakinah Wan Jusoh; Norfaizah Abdul Jobar; Md Zahril Nizam Md Yusoff; Hanifah Mahat
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.pp1052-1062

Abstract

Corpus linguistics investigates language using extensive text databases. Tools assist researchers in analyzing, extracting, and interpreting linguistic information efficiently. Furthermore, if researchers only use traditional tools in corpus linguistic analysis, they will lack the comprehensiveness and efficiency required to effectively navigate and derive valuable insights from language data. This paper employed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to find the primary data based on a few keywords in corpus linguistic, corpus analysis, computational linguistic, text corpora and tool support. Based on this method, we used advanced searching techniques on Scopus and Web of Science (WoS) and discovered (N=28) data pertinent to the study. Expert scholars decide on a theme based on the problem, which is (i) types of corpus tools and their uses; (ii) their contributions and their capabilities (iii) limitations of corpus tools. All the tools were used in interdisciplinary studies. In summary, this systematic review uncovers pivotal key findings at the intersection of computational tools and corpus analysis, enriching linguistic knowledge. It highlights the interdisciplinary potential of corpus-based analysis in advancing linguistic tools and, their applications, as well as language analysis.

Page 804 of 914 | Total Record : 9138

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