TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Predicting the adoption of “Buku Kedai”: a digital book-keeping application among Malaysian micro-entrepreneurs
Full text linkThe emergence of digital economies has led to the diffusion of digital innovation in business activities. A book-keeping digital application known as “Buku Kedai” is among the innovations invented by researchers to optimise entrepreneurial business performance. Despite offering tremendous benefits, its adoption is low. The intriguing phenomenon triggers the researchers to study the factors accelerating its adoption. Emulating the factors proposed by the theory of acceptance model (TAM), the researchers predict that adopting “Buku Kedai” is related to perceived usefulness, perceived ease of use and subjective norms. The researchers distributed 300 questionnaires to Malaysian micro-entrepreneurs. However, only 248 data proceeded for further analysis. Employing SPSS 27 and partial least square-structural equation model (PLS-SEM) 3, the results indicated that all three variables, namely perceived usefulness, perceived ease of use and subjective norms, are significant predictors of “Buku Kedai” adoption. The discoveries shed insights for policymakers, governments, and academia to formulate development programs for empowering entrepreneurs through digital and capability upscaling. Besides, the programs should encourage the involvement of social contacts to support microentrepreneurs in adopting “Buku Kedai”. Adopting the “Buku Kedai” application is crucial in enhancing microentrepreneurs' quality, viability, resilience, and competitiveness in pursuing success and sustainability
ANN-based performance estimation of a slotted inverted F-shaped tri-band antenna for satellite/mm-wave 5G application
Full text linkIn this research, we explain comprehensive industrial and innovation results on using an artificial neural network (ANN) method to improve the performance of microstrip patch antennas for 5G, indoor-outdoor, and Ku band uses. To determine if an antenna is appropriate, this article discusses multiple methods, one of which is to do a simulation using validating software like high frequency structure simulator (HFSS) and Altair Feko. Based on the Rogers RT 5880 substrate, the antenna is constructed. There is a loss tangent of 0.0009 and its dimensions are 17.1053 mm in length and 16 mm in width. Its dielectric constant is 2.2. Despite its small size, it boasts an impressive maximum efficiency of almost 90% and a gain of approximately 8 dB. As an indicator of ANN model performance, we may look at the R-squared value (99%), the mean square error (MSE), which is approximately 0.0015, and the confidence interval (99%). The ANN models are the most accurate and have the lowest error rate when it comes to predicting efficiency and gain. The suggested antenna is a promising contender for the targeted Ku band, indoor/outdoor, and 5G uses, as verified by the clustering of computer simulation technology (CST), HFSS, and Altair Feko simulated results with the measured and predicted outcomes of ANN approac
BER-performance evaluation for 5G-PD-NOMA system in multipath communication channels
Full text linkIn this paper, a bit error rate (BER) performance is evaluated for power domain non-orthogonal multiple access (PD-NOMA) system. The performance test is examined considering; additive white gaussian noise (AWGN), flat and long-term evolution (LTE)-multipath selective channels such as; pedestrian channel model (EPA), vehicular channel model (EVA), and typical urban model (ETU). The proposed system considering two user equipment’s (UE1 and UE2) with a single base station (BS) for downlink channel. Two different powers were allocated to each user according to their positions from the BS. The superposition coding process is performed with successive-interference-cancelation (SIC) at both transmitter/receiver sides respectively to distinguish the desired received signal. The performance evaluations proves that the degree of power allocated to each user plays a significant rule in frequency selection environments. UE1 has a better BER performance than UE 2 by about 9 dB in EPA, 6 dB in EVA, and 7 dB in ETU environments respectively at a BER of 10-3. However, in flat fading environment, the distance for each user represents a significant factor affecting the BER performance, where, UE 2 has a better BER performance than UE 1 by about 10 dB at a BER of 10-3
Enhanced sentiment analysis and emotion detection in movie reviews using support vector machine algorithm
Full text linkFilms evoke diverse responses and reactions from audiences, captured through their reviews. These reviews serve as platforms for audiences to express opinions, evaluations, and emotions about films, reflecting the personal experiences and unique perceptions of the viewers. Given the vast volume of reviews and the distinctiveness of each perspective, automated analysis is essential for efficiently extracting valuable insights. This study employs the support vector machine (SVM) algorithm for classifying movie reviews into positive and negative categories. The dataset includes 50,000 IMDb movie reviews, split evenly between positive and negative sentiments. Each review is analyzed using the National Research Council Canada (NRC) emotion lexicon (NRCLex) to assign scores for emotions such as anger, disgust, fear, joy, sadness, and surprise. Subsequently, these reviews are further analyzed using term frequency-inverse document frequency (TF-IDF) for classification. The proposed algorithm achieves 90% accuracy, indicating its effectiveness in classifying sentiments in movie reviews. The study's findings confirm the potential of the SVM algorithm for broader applications in sentiment analysis and natural language processing. Additionally, integrating emotion detection enhances understanding of nuanced emotional content, providing a comprehensive approach to sentiment classification in large datasets
A combination of hill cipher and RC4 methods for text security
Full text linkTo hide confidential messages from people who are not responsible or who can access the messages, a way is needed to hide the messages. One way to hide messages in transmission is to change the data into something unintelligible by encoding and embedding it using cryptography and steganography techniques. This application was built using the hill cipher algorithm and the Rivest Cipher 4 (RC4) method. This algorithm is a symmetric key algorithm which has several advantages in data encryption. The hill chiper algorithm uses a mxm matrix as the encryption and decryption key. Meanwhile, the RC4 symmetric key is in the form of a stream cipher which can process input data as well as messages or information. Input data is generally in the form of bytes or even bits. The results of this research show that hill cipher and RC4 have their respective advantages and disadvantages. However, currently, RC4 is generally considered less safe for use in security-critical scenarios due to its vulnerability to attack. It is highly recommended to use an encryption algorithm such as advanced encryption standard (AES) which is modern and strong and has been tested and proven to be more resilient
3D word embedding vector feature extraction and hybrid CNN-LSTM for natural disaster reports identification
Full text linkSocial media contain various information, such as natural disaster reports. Artificial intelligence is used to identify reports from eyewitnesses early for disaster warning systems. The artificial intelligence system includes a text classification model with feature extraction and classification algorithms. Word embedding-based feature extraction is widely used for 1-dimensional (1D) and 2-dimensional (2D) data, suitable for traditional or deep learning algorithms. However, applying feature extraction to 3-dimensional (3D) data for text classification is limited. Previous studies focused on word embedding for 1D, 2D, and 3D outputs with convolutional neural network (CNN). Yet, using 3D data and CNN did not perform well. Despite using CNN and 3D variants, identifying natural disaster reports remains below 80% accuracy. This research aims to improve identifying earthquakes, floods, and forest fires with 3D data and hybrid CNN long short-term memory (LSTM). The study found models with accuracies of 83.38%, 83.72%, and 89.03% for each disaster type. Hybrid CNN LSTM significantly enhanced identification compared to CNN alone, supported by statistical tests with P value less than 0.0001
A circular compact ultra‐wideband antenna for 5G microwave applications
Full text linkThis article introduces an innovative circular and compact ultra‐wideband (UWB) radiator designed specifically for 5G microwave applications. This antenna incorporates a “TU”-shaped ground plane on its reverse side, with strip lines feeding the circular element on the front side. Notably, the antenna exhibits impressive characteristics, including an outstanding impedance bandwidth of 107%, and an impressive return loss of -32 dB. Its operational frequency range spans from 2.4 GHz to 11 GHz, centered at 6.7 GHz. Extensive simulations were conducted using CST microwave studio software to validate its performance. The antenna’s physical dimensions are defined by a size of 0.12 λ × 0.08 λ × 0.012 λ relative to its wavelength. Furthermore, this antenna demonstrates exceptional stability in its polar patterns and maintains a high-efficiency level, achieving a substantial gain of 3.75 dBi with an efficiency rating of 84.5%. These remarkable attributes make this antenna suitable for a wide range of applications, including Wi-Fi, 5G, WLAN, and various other microwave communication scenarios
Feature selection to improve distributed denial of service detection accuracy using hybrid N-Gram heuristic techniques
Full text linkDistributed denial of service (DDoS) attacks servers and computers in various ways, such as flooding traffic. There are three DDoS detection methods, namely anomaly-based, pattern-based and heuristic-based. However, pattern-based methods cannot detect recent attacks, while anomaly-based methods have low accuracy and relatively high false positives. This research proposes increasing accuracy using a heuristic-based DDoS detection method and a new feature. The combination of CSDPayload+N-Gram and CSPayload+N-Gram features is called hybrid N-Gram, which is analysed on four datasets: CIC2017, CIC2019, MIB-2016, and H2NPayload. Next, calculate Chi-square distance (CSD) and cosine similarity (CS) using the N-Gram frequency value results. Subsequently, compute Pearson Chi-square using the N-Gram frequency value results. Compare the CSDPayload+N-Gram and CSPayload+N-Gram, along with the Pearson Chi-square value, to classify it as either DDoS or not. Finally, feature selection based on weight correlation and payload classification employs machine learning algorithms: support vector machine (SVM), K-nearest neighbors (KNN), and neural network (NN). The average accuracy rate for detecting DDoS attacks across four datasets, utilising the CSDPayload+4-Gram and CSPayload+4-Gram features with the SVM algorithm, is 99.71%, which surpasses the accuracy achieved by using KNN (96.22%) and NNs (99.50%) imitation. Thus, the best algorithm for detecting DDoS is SVM with hybrid 4-Gram
Multi-step constant current-constant voltage charging method to improve CC-CV method on lead acid batteries
Full text linkConstant current-constant voltage (CC-CV) is one of the battery charging methods that is commonly used. However, this method has several drawbacks, including the charging current in constant current (CC) mode, which can only be set to a maximum of 0.3 C on lead acid batteries, resulting in a relatively long charging duration. Therefore, in this research, the multi-step constant current-constant voltage (MCC-CV) method of battery charging system is developed where this method can use a greater charging current, resulting in a significant reduction in charging duration by using multiple current setpoints in MCC mode, with the initial setpoint current can be set beyond 0.3 C, which is 0.34 C in this system. This system uses a DC-DC single-ended primary inductance converter (SEPIC) converter as a battery charging control system, equipped with a power cut-off relay when the charging current reaches 0.05 C in constant voltage (CV) mode. From the test results obtained, the MCC-CV method can charge the battery to its full capacity faster than the CC-CV method with a difference of 15.34 minutes and the relay on the system can work properly
Transforming the voting process integrating blockchain into e-voting for enhanced transparency and securiy
Full text linkThis study introduces a novel e-voting system utilizing blockchain technology to address the challenges inherent to traditional voting methods. Traditional systems often suffer from inaccuracies, susceptibility to manipulation, and elevated costs. Conversely, while e-voting shows potential, issues related to transparency and security have curbed its full adoption. Our research overcomes these hurdles by integrating a system developed through the Kanban methodology, with the blockchain serving as the central repository for all election data. This approach boosts transparency and security, using public-private key pairs for each transaction, and simplifying blockchain access. Organizers initiate elections and define eligible voters; this data is then securely moved to the Ethereum blockchain. Voters can effortlessly use the system, casting votes and accessing real-time, unalterable results. Various communication protocols ensure system stability, with simulated cyberattacks showcasing its security. After exhaustive testing and refinement, areas for further enhancement have been identified. This innovative system offers unmatched transparency and trust in the voting process, marking a considerable leap for trustworthy elections, especially in small to medium-sized settings