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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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Ransomware attack awareness: analyzing college student awareness for effective defense Syamsuar, Dedy; Pakdeetrakulwong, Udsanee; Jacob, Deden Witarsyah; Chandra, Felixius Arelta
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.pp1122-1130

Abstract

There are growing concerns about security as the usage of computers in academic settings continues to increase. This research aims to investigate the level of awareness among university students regarding security threats associated with ransomware. This study examines students' behaviour and preventive motivation for ransomware attacks, along with the measures taken to mitigate these security threats. The study model combines the theory of planned behaviour (TPB) and preventive motivation theory (PMT) with additional threat awareness (TA) variables. The research findings indicate a high level of awareness regarding the dangers. TA has a positive influence on other factors, as indicated by the significant t-values (perceived severity (PS)=4.479, perceived vulnerability (PV)=3.251, response efficacy (RE)=14.344, and self-efficacy (SE)=8.034). This research also demonstrates that subjective norm (SN) and affective responses (AR) have a key impact on behavioural intention (BI). Moreover, two of the preventive motivation factors, PS and PV, significantly contribute to BI, while the other two (RE and SE) did not show a significant contribution to BI.
Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption Hamid El Bourakkadi; Abdelhakim Chemlal; Hassan Tabti; Mourad Kattass; Abdellatif Jarjar; Abdelhamid Benazzi
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.pp1787-1799

Abstract

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.
A decentralized HC-ADMM approach for large antenna arrays Jyothi B. R.; Manjanaik Naganaik
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.pp795-805

Abstract

This work addresses the evolving landscape of internet of things (IoT) applications and large antenna array systems, where optimizing spectral efficiency and simplifying design complexities are crucial. Focusing on two key challenges, the study introduces a novel hybrid analog-digital transceiver strategy tailored for frequency-selective channels. By integrating Shannon and Hartley theorems, the approach enhances data transfer rates, thereby optimizing radio frequency (RF) chain utilization in large-scale antennas. To achieve a balance between transceiver performance and hardware complexity, the study employs a decentralized alternating direction method of multipliers (ADMM) framework. The proposed hybrid consensus ADMM algorithm (HC-ADMM) ensures efficient convergence in decentralized optimization scenarios. Comparative analyses with ADMM and existing system transceiver optimization (ESTO) models highlight HC-ADMM's superior performance across key metrics such as spectral efficiency, efficiency per cell, total efficiency, and optimal scheduling of user equipment (UEs). Particularly notable is HC-ADMM's advanced optimization capabilities as the number of transmit antennas increases, positioning it as a promising approach for enhancing overall communication network performance.
Touch-free tissue dispensing device Mohd Zaki, Nurul Shahira; Nik Dzulkefli, Nik Nur Shaadah; Abdullah, Rina; Ismail, Syila Izawana; Omar, Suziana
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.pp795-803

Abstract

In this paper, an innovative solution for the everyday issue of tissue dispensing is presented. With billions of tissues distributed daily, the current dispensers often face challenges, as revealed in studies of frequently damaged units. The primary objective was to enhance this fundamental item, aiming to simplify users’ lives. The key innovation lies in granting users control over the tissue dispenser’s rolling mechanism. Introducing the Arduino UNO microcontroller-powered smart tissue dispenser. Operated by a stepper motor, the dispenser reacts to the user’s needs. Activation occurs when the infrared sensor detects hands, prompting the motor to release the appropriate amount of tissue. It’s like witnessing magic, yet it’s simply the ingenuity of technology at play. The software for the Arduino UNO, serving as the project’s controller, is compiled, and uploaded using the Arduino IDE. The performance of this automatic tissue dispenser indicates success in addressing common issues and facilitating effortless tissue retrieval.
Artificial intelligence-based Karawo motif formation using genetic algorithm Mukhlisulfatih Latief; Syahrul Syahrul; Abdul Muis Mappalotteng
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.pp1820-1828

Abstract

This research explores the application of artificial intelligence in generating Karawo motifs, a traditional Indonesian pattern. The research involves collecting a dataset of existing Karawo motifs and utilizing genetic algorithms to evolve and create novel pattern variations. The generated motifs are evaluated based on their adherence to traditional design principles and aesthetic appeal. The formation of Karawo motifs begins with randomly selecting image data from a database. Then, the selection of transformation treatments is performed by optimizing the fitness function within the genetic algorithm. The applied types of transformations include geometric transformations, Boolean transformations, and arithmetic transformations. The outlined genetic algorithm steps include determining the fitness function, performing its evaluation, selecting fitness values, applying crossover, implementing mutation, managing survivor selection, and terminating iterations. The results indicate that the developed system is capable of creating diverse and appealing Karawo motif patterns, showcasing the potential of combining traditional artistry with artificial intelligence. This study has the potential to expand the possibilities of Karawo motif design and contribute to the preservation and promotion of Indonesian cultural heritage.
Enhancing hate speech detection in Indonesian using abusive words lexicon Endang Wahyu Pamungkas; Dian Purworini; Divi Galih Prasetyo Putri; Sohail Akhtar
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.pp450-462

Abstract

Hate speech is a major challenge in Indonesia, a diverse country with multiple languages and a dynamic online landscape. This research explores the phenomenon of hate speech and its detection, particularly in language contexts with limited resources. We introduce a new abusive words lexicon, created by collecting words from various sources, adapted for Indonesian, Javanese and Sundanese. Our study investigates the practical implementation of this lexicon. We conducted extensive experiments using different datasets and machine learning models, aiming to improve hate speech detection. The results consistently show a positive impact of the lexicon, which significantly improves detection, especially in languages with fewer resources. But this research paves the way for further exploration. The lexicon can be expanded, broadening its scope. Additionally, we suggest investigating more sophisticated models, such as transformerbased models, to more effectively detect hate speech. In a world where hate speech is a growing problem, our research provides valuable insights and tools to combat it effectively in Indonesia and other countries.
Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN Bhoopal, Sowmyashree; Rao, Mahesh; Krishnappa, Chethan Hasigala
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.pp366-377

Abstract

Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.
Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things Sampoorna Bhimshetty; Agughasi Victor Ikechukwu
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.pp971-980

Abstract

The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.
Supraharmonic mitigation in microgrid and electric vehicle charging station through multilevel converter Ayyar Subramaniya Siva; Sakunthala Ganesan Ramesh Kumar; Karuppiah Dhayalini
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.pp1309-1317

Abstract

The problem of supraharmonics (SH) in a microgrid (MG) system connected to an electric vehicle (EV) charging station is discussed in this work. SH, or high-frequency harmonics which occur beyond the typical harmonic spectrum, can cause problems with power quality (PQ) and equipment failure. A multilevel converter (MLC)-based solution is proposed together with a frequency domain analysis method to lessen this issue. Using frequency domain methods like the fast fourier transform (FFT), the suggested method precisely measures and examines the SH content in the MG and EV charging station. Based on the findings, an MLC control method that actively lowers the SH is created. The efficiency of the suggested strategy in lowering SH levels and ensuring PQ is assessed through experimental validation. The development of SH analysis and mitigation techniques as a result of this research makes it easier to safely and effectively integrate the infrastructure for EVs with renewable energy sources (RES).
A randomized blockchain consensus algorithm for enhancing security in health insurance Najah Al-Sarayrah; Nidal Turab; Abdelrahman Hussien
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.pp1304-1314

Abstract

Health insurance fraud is a significant problem affecting insurance providers and policyholders. To address the rising problem of fraudulent activities in the health insurance sector, this paper proposes a pioneering blockchain-based system aimed at increasing transparency and security. Utilizing a hybrid Blockchain architecture, the system incorporates a consensus algorithm influenced by practical byzantine fault tolerance (PBFT) and proof of activity (PoA) to ensure reliability and efficiency in distributing mining power. Developed using Python, extensive testing confirms the system's performance and security metrics. Results show that a block size containing one transaction is 1.63 KB, with 1.2 KB for data and 0.43 KB for identification and hashing. Operational tests demonstrate that a single participant can upload 850 transactions to the transaction pool, with validation completed in just 7.49 seconds. Block appending time for these transactions is a swift 10 seconds. Notably, the system exhibits resilience against data tampering, detecting unauthorized changes within 881.3 milliseconds across 10,000 blocks and identifying irregularities in the transaction pool within 8.78 seconds. Additionally, to enhance data privacy, patient information is accessible only through a unique QR code, providing an extra layer of security; this research represents a significant advancement in combatting fraud and safeguarding data privacy.

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