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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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Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture Mohd Mizan Munif; Husna Jamal Abdul Nasir; Muhammad Imran Ahmad
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.pp261-268

Abstract

Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.
The De Bruijn graph of non-sequential pattern repetitions in DNA strings Fong, Wan Heng; Ildrussi, Ahmed; Yosman, Ahmad Firdaus
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.pp787-794

Abstract

In molecular biology, constructing a genome based on substantially many reads from multitudes of deoxyribonucleic acid (DNA) strings has become an insurmountable task; one which has been continuously addressed by the introduction of various assembly algorithms based on three steps called the overlap-layout-consensus strategy. In the overlap step, the De Bruijn graph is one of many graphs that illustrate the data of all the assembly algorithms. In this article, by using definitions and methods of mathematical induction, some properties of the De Bruijn graph of one time and two times non-sequential repetition of patterns in a DNA string are presented. Examples of these De Bruijn graphs are also given. From there, a generalisation of said properties for m times non-sequential pattern repetition in a DNA string is acquired by means of mathematical induction, as well. The theoretical work in this research is invaluable to develop algorithms that increase the computational efficiency of assembly algorithms.
Virtual analysis of machine learning models for diseases prediction in muskmelon Deeba Kannan; Balakrishnan Amutha; Sattianadan Dasarathan; Daniel Rosy Salomi Victoria; Vikas Maheshkar; Ravindran Ramkumar; Dhandapani Karthikeyan
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.pp1748-1759

Abstract

Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.
A 9.73 GHz wide-band off-body patch antenna for biomedical applications Niloy Goswami; Md. Abdur Rahman
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.pp151-158

Abstract

The primary goal of this study is to design a simple antenna that has a wide bandwidth and low return loss for biomedical applications. The paper shows the recommended antenna’s three-stage modeling, with the goal of assessing every important parameter while a Teflon or polytetrafluoroethylene (PTFE) polymer substrate is used. In order to better comprehend, a comparison with prior studies employing teflon and similar substrate materials is conducted for the proposed patch antenna. The analysis includes the phantom model, evaluating performance criteria such as specific absorption rate (SAR), return loss, bandwidth, and gain values relevant to biomedical applications. The antenna works at two different frequencies: 9.73 and 9.39 GHz, one in free space and another in a skin-cotton layer. The bandwidth of the antenna is 4.067 GHz in free space at the resonance frequency of 9.73 GHz, where the return loss is -62.18 dB. The performance of the proposed antenna in the field of biomedical applications, its underlying reasons, and its impacts are discussed in detail in this study.
Design of a chatbot in a mobile application for managing payments and controlling activities in a fast school organization Medina, Gustavo Teves; Cano Lengua, Miguel Angel; Medrano, Hugo Villaverde
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.pp1271-1286

Abstract

The fast school (FS) educational organization, like many contemporary educational institutions, faces challenges in efficient payment management and rigorous control of activities. Technology, particularly through mobile applications, has shown to be a potential solution to these problems, allowing institutions to stay at the forefront and provide optimized services to their educational community. Therefore, this research work focuses on how a chatbot, integrated into a mobile application, can improve payment management and control of activities in the FS educational organization. Through a detailed study on current trends in educational technology, the design and development of a chatbot adapted to the specific needs of the organization is presented. This chatbot not only facilitates payment processes, offering immediate responses and managing transactions, but also allows for more efficient control of academic and extracurricular activities, improving the experience of its users. In conclusion, the integration of chatbots in mobile applications is presented as a viable and promising solution to face and overcome management challenges in modern educational environments, providing adaptive and user-centered tools that enhance the operational efficiency of institutions. This work is developed with the Scrum methodology and presents a security gateway validated by a digital token.
Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison Shaikh, Zakir Mujeeb; Ramadass, Suguna
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.pp263-273

Abstract

Deep learning algorithms have revolutionized various fields by achieving remarkable results in time series analysis. Among the different architectures, recurrent neural networks (RNNs) have played a significant role in sequential data processing. This study presents a comprehensive comparison of prominent RNN variants: long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional GRU (BiGRU), and RNN, to analyze their respective strengths and weaknesses of national stock exchange India (NSEI). The Python application developed for this research aims to evaluate and determine the most effective algorithm among the variants. To conduct the evaluation, data from the public domain covering the period from 1/1/2004 to 30/06/2023 is collected. The dataset considers significant events such as demonetization, market crashes, the COVID-19 pandemic, downturns in the automobile sector, and rises in unemployment. Stocks from various sectors including banking, automobile, oil and gas, metal, and Pharma are selected for analysis. Finally, the results reveal that algorithm performance varies across different stocks. Specifically, in certain cases, BiLSTM outperforms, while in others, both BiGRU and LSTM are surpassed. Notably, the overall performance of simple RNN is consistently the lowest across all stocks.
An effective secondary personalization file system driven by FileForge module Gaojian Liu; Yufei Hu; Ngai Cheong
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.pp1315-1323

Abstract

Digital service platforms provided by academic support departments in Macao assist academic staff and students in various areas such as registry, student affairs, academic activities, and research. As the number of undergraduate students increases and new departments are established, academic staff often face the challenge of dealing with paperwork that contains similar content but different formats. This situation results in redundancies and a waste of time. This paper presents our endeavors to simplify administrative procedures in higher education by automating restructured documentation and developing secondary file systems. The paper presents two case studies: Scenario One focuses on streamlining the publication system for academic staff who submit papers in different formats. At the same time, Scenario Two aims to simplify the daily paperwork process for academic staff. Both cases involve transforming the distribution of administrative documents, transitioning from a standardized form with guidelines to a customized form with concise tips. This approach allows academic staff to handle only the necessary information, which may not be available in the database or requires verification. The case studies serve to demonstrate the effectiveness of this administrative simplification.
Improving graphics processing unit performance based on neural network direct memory access controller Santosh Kumar; Neelappa Neelappa; Saroja Bhusare; Veeramma Yatnalli
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.pp1476-1484

Abstract

In this paper proposes the design and implementation of the back propagation algorithm based neural network DMA (Direct Memory Access) Controller for use of multimedia applications. The proposed DMA controller work with the back propagation-training algorithm. The advantages of the back propagation algorithm it will be work on the gradient loss w.r.t the network weights. So this back propagation algorithm is used as training algorithm for the DMA controller. The proposed method is test with the different workload characteristics like heavy workload, medium workload and normal workload. The performance parameters are considered here is like accuracy, precision, recall and F1 score etc. The proposed method is compared with existing methods like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Sort term Memory) and GRU (Gated Recurrent Unit) etc. Finally, the proposed design will give the better performance than existing methods.
Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms Lahcen Amhaimar; Younes Nadir; Bakhouyi Abdellah; Khalifa Mansouri; Mohamed Bayjja; Abderrahim Khalidi
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.pp926-935

Abstract

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.
Mitigating ransomware attacks through cyber threat intelligence and machine learning Mamady Kante; Vivek Sharma; Keshav Gupta
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.pp1958-1965

Abstract

In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.

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