Indonesian Journal of Electrical Engineering and Computer Science
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An innovative image encryption scheme integrating chaotic maps, DNA encoding and cellular automata
Get PDFIn the current digital era, securing image transmission is crucial to ensure data integrity, prevent tampering, and preserve confidentiality as images traverse unsecured channels. This paper presents an innovative encryption scheme that synergistically combines a two-dimensional (2-D) logistic map, deoxyribonucleic acid (DNA) encoding, and 1-D cellular automata (CA) rules to significantly bolster encryption robustness. The proposed model initiates with the generation of a key image via the 2-D logistic map, yielding intricate chaotic sequences that fortify the encryption mechanism. DNA cryptography is employed to amplify randomness through diffusion properties, providing robust defense against various cryptographic attacks. The integration of 1-D CA rules further intensifies encryption complexity by iteratively processing DNA-encoded sequences. Experimental results substantiate that the proposed encryption scheme demonstrates exceptional endurance against a vast spectrum of attacks, affirming its superior security
The impact of COVID-19 on e-commerce: a cross-national analysis of policy implications
Get PDFThe field of e-commerce research has evolved over recent decades, but the coronavirus disease 2019 (COVID-19) pandemic significantly accelerated its prominence, as evidenced by extensive literature. The pandemic underscored the pivotal role of e-commerce in driving the digital transformation of the global economy. However, there remains a lack of comprehensive reviews in this area, particularly comparative analyses of how different countries leveraged e-commerce to navigate the pandemic’s challenges. This paper addresses this gap by examining the literature on e-commerce adoption and its implications during COVID-19, focusing on select countries, including China, Malaysia, and several European nations. The case of China, as a major economic power in Asia, offers particularly valuable insights
Hash-based message authentication code with secure hash algorithm-256 for efficient data sharing in blockchain
Get PDFRecently, cloud servers have increasingly been utilized for storing a large amount of data, which is stored in the form of ciphertext. In a decentralised system, the communication overhead on the network is recognized as the main problem due to the numerous transaction data recorded across the data Sharding and nodes with authorized users. Hash-Based Message Authentication Code with Secure Hash Algorithm-256-bit (HMAC-SHA-256 bit) is proposed for secure and effective data sharing in blockchain to overcome this issue. The secure algorithm HMAC serves for authenticating both the data origin and integrity. That uses a cryptographic hash procedure in combination with a confidential key to validate both the verification and tamper-proof content of a message. HMAC consists of a particular content and an authentication key with a hashing code value. In the Blockchain framework, the HMAC algorithm is utilized with the SHA-256bits to generate and validate the signatures of many transactions. SHA-256 is a hash algorithm that creates a 256-bit cryptographic checksum. The blockchain uses HMAC along with SHA-256bits, which is a safe and clearly expressed algorithm to allocate or convey the data securely. The Authentication of HMAC-SHA-256bits achieves the optimal retrieval times of 0.4s, 1.0s, 1.5s, 1.9s, 2.2s, and 2.8s for file sizes of 50KB, 100KB, 150KB, 200KB, 250KB, and 300KB, correspondingly, when compared to interplanetary file system (IPFS)
On-grid vs. off-grid photovoltaic systems for smart greenhouses: a techno-economic case study
Get PDFIntegrating photovoltaic (PV) systems into agricultural applications has gained significant attention as a sustainable energy solution. However, the feasibility of on-grid and off-grid PV systems for smart greenhouse applications in Indonesia remains unclear. This study compares both systems' technical performance, economic viability, and regulatory challenges through simulations and case studies in Lembang, Bandung. The analysis considers solar radiation levels, shading effects, installation costs, energy independence, and long-term operational efficiency. Results indicate that while on-grid systems offer lower initial investment and seamless integration with the utility grid, regulatory constraints and limited capacity approvals pose significant barriers. Despite higher initial costs, off-grid systems provide energy independence and long-term cost benefits by eliminating dependency on grid electricity and avoiding bureaucratic hurdles. The study concludes that off-grid PV systems are a more practical and sustainable solution for smart greenhouse applications in Indonesia, mainly where grid connection processes are complex or unreliable
Multi-visual modality for collaborative filtering-based personalized POI recommendations
Get PDFPoint-of-interest (POI) recommendation systems help users discover locations that match their interests. However, these systems often suffer from data sparsity due to limited user check-in history. To address this challenge, this study proposed a novel user profiling framework that incorporates multiple visual modalities derived from user-generated photos. Three types of visual-based user profiles were constructed: image label-based, image feature-based, and a fused profile, combining both modalities through score-level fusion. We conducted extensive experiments on two real-world datasets. The results demonstrate that visual-based profiles, particularly the image feature-based profile, consistently improve recommendation performance under sparse data conditions. Although the fused profile offered stable results, it did not consistently outperform the single modality. Furthermore, performance was sensitive to the number of nearest neighbors and the amount of training data. These findings highlight the importance of modality selection and fusion strategy in visual-based POI recommendation systems
The acceptance and adoption of technology on government environment: a bibliometric analysis
Get PDFThis study examines technology acceptance and adoption in government, particularly in the context of public service delivery, through a bibliometric analysis conducted using VOSviewer. The analysis aims to identify key research trends, thematic relationships, and emerging patterns in the field of digital governance. Data were retrieved from the Scopus database, covering publications related to the acceptance and adoption of technology in government from 2020 to 2025. The network visualization results indicate that artificial intelligence (AI), digital governance, public transport, e-health, and COVID-19 are among the dominant research themes, reflecting the rapid adoption of technology in transforming public services. The cooccurrence analysis reveals strong linkages among topics such as public health, AI, blockchain, and public trust, underscoring the increasing integration of digital technologies within governance systems. Furthermore, the overlay visualization demonstrates a thematic shift from fundamental studies on acceptance factors—such as trust, security, and digital literacy— toward implementation-oriented strategies, including digital transformation, smart governance, and public service efficiency. The findings suggest that technology adoption in public service continues to expand and diversify; however, significant challenges remain, particularly concerning data security, transparency, and citizen trust. Future research should focus on exploring the application of AI, the use of blockchain for governance, and the integration of internet of things (IoT) in smart city development to support a sustainable, efficient, and citizen-centric digital transformation in the public sector
SCADE: a deep learning ensemble for semantic flow analysis in smart contract vulnerability detection
Get PDFA vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts
The role of artificial intelligence in advancing the performance of information retrieval
Get PDFThe motivation behind applying artificial intelligence (AI) in information retrieval (IR) is that the current methodologies include algorithms designed by researchers, leaving space for the applicability of genetic AI algorithms in IR. While different algorithms designed by developers rely on the originality or performance of the algorithm, precise results are achieved through integrating AI algorithms with traditional algorithms. The proposed methodology introduces document structure weighting with optimized performance. It is enabled by employing genetic algorithm and genetic programming for learning optimal weights in ranking document components. The Croft probabilistic ranking, vector space inner product models, and the BM25 standard were compared with each other after AI integration. Genetic algorithm and genetic programming were applied in the stemming and thesaurus forming processes of these models. Inducing genetic algorithm and genetic programming into the specified models increased the mean average precision of the Croft model and the vector space method by approximately 5% while there were no observable result improvements in BM25. It was found that applying genetic algorithm and genetic programming in learning synonyms and stemming rules, respectively, increased the overall performance of IR models, emphasizing the need for AI in IR
Miniaturized reconfigurable metamaterial based bandstop filter for wireless applications
Get PDFThe design of compact size and high efficiency metamaterial based reconfigurable microstrip bandstop filter for IEEE 802.11 WLAN applications is developed. This paper presents a switchable dual-mode filter, it resonant at 2.4 GHz and 3.6 GHz. The hexagonal metamaterial resonator inserted switch as PIN diode which form reconfigurable filter. By changing the DC bias of the diode, the filter can be reconfigured with a controlled precision, resulting in the frequency reconfigurable. The CST simulator used to simulate filter design, measuring a return loss over -29.12 dB and a low insertion loss less than -0.2 dB, which is a great performance. The filter is compact at the size of 8 mm×12 mm×1.6 mm design using Rogers RT Duroid 5880 substrate
Cardio meta-stack: a meta-classifier ensemble for enhanced cardiovascular disease prognosis
Get PDFCardiovascular diseases (CVDs) remain a significant global health concern, necessitating effective preventive measures and early diagnosis to reduce mortality rates. Leveraging machine learning models to identify risk factors holds great promise, especially in cardiology. This study introduces a robust methodology for prognosing cardiac illnesses based on patient-specific factors. By integrating five publicly available datasets from the UCI Repository and employing Feature Importance techniques for optimal risk factor selection, the proposed approach enhances prediction accuracy. Furthermore, the inclusion of the density-based spatial clustering of applications with noise (DBSCAN) algorithm assists in noise detection and removal, thereby improving model precision. The proposed Cardio MetaStack model, coupled with a stacking classifier ensemble, achieved an accuracy of 94.91%, surpassing that of traditional algorithms such as XGBoost 90.45%, demonstrating its efficacy in heart disease prediction