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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 64 Documents
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Search results for , issue "Vol 36, No 1: October 2024" : 64 Documents clear
Deep learning based hybrid precoder for optimal power allocation to improve the performance of massive MIMO Bhairanatti, Shilpa; P., Rubini
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp570-582

Abstract

Hybrid precoding is a significant procedure for decreasing the hardware complexity and power usage in massive multiple-input multiple-output (MIMO) systems. However, the effectiveness of hybrid precoding is highly dependent on precise channel state info and designing of the beamforming matrix. In recent years, deep learning-based approaches have emerged as a promising solution to address these challenges. This research focuses on improving the performance of massive MIMO systems. However, several methods have been introduced to develop the hybrid precoding model, but these models suffer from several issues such as complexity, interference and quantization error. Currently, deep learning-based methods have gained huge attention in this domain where these methods learn from the data and try to overcome the challenges. Here, a deep learning-based model is presented where our main aim is to develop a hybrid precoder along with the deep learning-based optimal power allocation model. Therefore, the proposed model overcomes the issue of hybrid precoding and power distribution resulting in improving the overall performance of massive MIMO systems on the parameters such as spectral efficiency (SE) and the sum rate.
The research on the signal source number estimation algorithm Peizhi, Wang; Mohamed, Raihani; Mustapha, Norwati; Manshor, Noridayu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp188-196

Abstract

In array signal processing, Estimating the quantity of signal sources represents a crucial area of investigation. In this paper, a comprehensive introduction and analysis of the estimation methods for determining the number of signal sources are presented, including the background and significance, and the significance of precise estimation of the quantity of signal sources. The influence of factors such as signal-to-noise ratio (SNR), noise background, and number of snapshots on the estimation algorithm is discussed in detail. At the same time, common array models are introduced. Then, different signal source number estimation algorithms are analyzed in detail, and their respective advantages and applicable conditions are pointed out. Finally, the performance of each algorithm in different situations is evaluated by comparing the performance of the algorithms under different SNRs, snapshot numbers, and array elements. The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation.
Improved deep learning architecture for skin cancer classification Owida, Hamza Abu; Alshdaifat, Nawaf; Almaghthawi, Ahmed; Abuowaida, Suhaila; Aburomman, Ahmad; Al-Momani, Adai; Arabiat, Mohammad; Chan, Huah Yong
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp501-508

Abstract

A leading cause of mortality globally, skin cancer is deadly. Early skin cancer diagnosis reduces mortality. Visual inspection is the main skin cancer diagnosis tool; however, it is imprecise. Researchers propose deep-learning techniques to assist physicians identify skin tumors fast and correctly. Deep convolutional neural networks (CNNs) can identify distinct objects in complex tasks. We train a CNN on photos with merely pixels and illness labels to classify skin lesions. We train on HAM-10000 using a CNN. On the HAM10000 dataset, the suggested model scored 95.23% efficiency, 95.30% sensitivity, and 95.91% specificity.
System availability assessment and optimization of a series-parallel system using a genetic algorithm Chaudhary, Priya; Bansal, Shikha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp153-162

Abstract

To optimize the operational availability of the series-parallel system and provide useful insights for maintenance planning, the study attempts to investigate the availability of a ball mill unit. These four different components make up the ball mill production system: “drum,” “ring-gear,” “gearbox,” and “electric motor.” There is a chain mechanism connecting all four components. The “ring gear” and “electric motor” components are composed of two independent units, one of which serves the desired purpose and the other is maintained in cold standby. The “drum” and “gearbox” of the components each contain only one unit. Therefore, a novel mathematical model is designed and implemented in this work by assuming arbitrary repair rates and exponentially distributed failure rates using the Markov process and Chapman-Kolmogorov equations. This study explored the availability with a normalization method and used genetic algorithm techniques to optimize ball mill availability. Putting this article into practice is of great benefit when developing an appropriate maintenance program. Through this, the study achieves maximum production. To investigate the behavior of several performance characteristics of the ball mill production system, numerical results and corresponding graphs are also specifically created for specific values of subsystem parameters, such as failure rate, and repair rate to increase the system’s overall efficiency.
Solar irradiation intensity forecasting for solar panel power output analyze Sucita, Tasma; Hakim, Dadang Lukman; Hidayahtulloh, Rizky Heryanto; Fahrizal, Diki
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp74-85

Abstract

Accurate forecasting of global horizontal irradiance (GHI) is critical for optimizing solar power plant (SPP) output, particularly in tropical locales where solar potential is high yet underutilized due to forecasting challenges. This research aims to enhance GHI prediction in one of the major cities of Indonesia, where existing models struggle with the area’s natural climate unpredictability. Our analysis harnesses a decade of data 2011-2020, including GHI, temperature, and the Sky Insolation Clearness Index, to calibrate and compare these methodologies. We evaluate and contrast the exponential smoothing method versus the more complicated artificial neural network (ANN). Our findings reveal that the ANN method, notably its fourth iteration model with 12 input and hidden layers, substantially outperforms exponential smoothing with a low error rate of 1.12%. The use of these methodologies forecasts an average energy output of 252,405 Watt for a solar panel with specification 15.3% efficiency and 1.31 m2 surface area throughout the 2021 to 2025 timeframe. The work offers the ANN method as a strong prediction tool for SPP development and urges a further exploration into more advanced forecasting methodologies to better harness solar energy.
Comparative analysis of selected optimization algorithms for mobile agents’ migration pattern Oyediran, Mayowa O.; Ajagbe, Sunday Adeola; Ojo, Olufemi S.; Elegbede, Adedayo Wasiat; Adio, Michael Olumuyiwa; Adeniyi, Abidemi Emmanuel; Adebayo, Isaiah O.; Obuzor, Princewill Chima; Adigun, Matthew Olusegun
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp685-693

Abstract

Mobile agents are agents that can migrate from host-to-host to work in a heterogeneous network environment. A mobile agent can migrate from host-to-host in its plan with the statistics generated on each host through a route known as migration pattern. Migration pattern therefore is the route the agents use to travel within the plan from the first host to the last host. However, there is a need for a comparison between the commonly used optimization algorithms in developing migration patterns for mobile agents with respect to some evaluation metrics. In this paper, the three techniques firefly algorithm (FFA), honeybee optimization (HBO) and particle swarm optimization (PSO) were used for developing migration patterns for mobile agents and their comparison was done based on migration time, time complexity and network load as metrics. PSO is discovered to perform better in terms of network load with an average of 242.3905 bits per second (bps), time complexity with an average of 41.2688 number of nodes (n), and migration/transmission time with an average of 4.203462 seconds (s).
A model proposal for enhancing cyber security in industrial IoT environments Buja, Atdhe; Apostolova, Marika; Luma, Artan
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp231-241

Abstract

The revolution of the industrial sector in the automated one has happened with the use of the Industrial Internet of things (IIoT). They are providing unprecedented possibilities for connection, and automation. Also, the ubiquitous of IIoT has brought new cyber security challenges, putting sensitive data at risk. This research paper proposes a comprehensive model for enhancing the cyber security of IIoT systems. Our model integrates various countermeasures, including a proactive assessment of security vulnerabilities, examination of identified vulnerabilities, categorizing data, delivery of comprehensive reports, and assurance of effective countermeasures based on a cost-benefit approach, aligned with industry standards and frameworks. The proposed model aims to address the need for the development of robust and resilient cyber security solutions for IIoT environments. This research work introduces the proposed model's main functions, integration, workflow, and references. With this research, we contribute to the enhancement of cyber security in the IIoT environment by proposing a model that assists with proactive assessment, effective response, and informed decision-making. We envision that the proposed model will support industrial organizations in securing their IIoT systems against cyber threats, ultimately have stability and secure industrial operations.
Towards robust security in WSN: a comprehensive analytical review and future research directions Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Mardenov, Yerik; Adamova, Aigul; Karabayev, Nurdaulet; Abdildayeva, Assel; Baumuratova, Dilaram
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp318-337

Abstract

One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS).
Tourism itinerary recommendation using vehicle routing problem time windows and analytics hierarchy process Nasution, Surya Michrandi; Septiawan, Reza Rendian; Azmi, Fairuz
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp517-534

Abstract

Bandung and Lembang are cities that are chosen by tourists as their destinations. Even though these cities are located side-by-side, each city has different characteristics. Bandung has many hotels and culinary spots, meanwhile, Lembang has many scenery spots. Tourists usually have limited time to visit all the destinations on holiday, which makes them choose several destinations. This paper proposes a tourism itinerary recommendation system based on the calculation of the most optimal route between destinations using the vehicle routing problem with time windows (VRPTW). Later, the optimal route is defined using the shortest path algorithm (Dijkstra). Data for the algorithm came from the collaboration between the several road information and criteria weights that are determined using the analytics hierarchy process (AHP). According to the simulation, the criteria weights are 6.9%, 62.7%, 18.6%, and 11.9% for route length, traffic condition, travel time, and weather condition, respectively. Moreover, the optimal number of tourism itinerary plans is 4 destinations. As the usage of computational resources, it takes 31.8% and 61.9% of CPU and memory usage. The time processing increases exponentially as the increment of the number of requested stops. The output of this research is expected to be a solution to the tourist itinerary plan.
Efficient and secure data transmission: cryptography techniques using ECC Alhaj, Abdullah Ahmad; Alrabea, Adnan; Jawabreh, Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp486-492

Abstract

Data transmission has become a crucial aspect of our daily lives in the current digital age. However, this transmission comes with the risk of security breaches, which can result in data theft and unauthorized access. This issue can be addressed by using cryptographic techniques such as elliptic curve cryptography (ECC). In comparison to other cryptosystems, ECC is a potent cryptographic tool that provides high levels of security with comparatively reduced key sizes. This paper discusses the use of ECC in efficient and secure data transmission. It provides a comprehensive overview of ECC, including its mathematical background and how it can be applied to encryption and decryption processes. The paper also presents a comparison of ECC with other cryptographic techniques and highlights its advantages, including its resistance to attacks and efficiency in resource-constrained environments. Finally, the paper discusses the implementation of ECC in real-world scenarios and its potential to revolutionize secure data transmission.

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