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The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.

The development of financial technology has led to the emergence of cryptocurrency as a decentralized digital instrument that enables fast and cross-border financial transactions. While this technology offers efficiency and flexibility in digital financial activities, it also creates opportunities for misuse in various forms of crime, including terrorist financing. This study aims to analyze the use of cryptocurrency as a means of financing terrorist activities in Indonesia, examine the existing legal framework governing terrorist financing, and identify the challenges faced in law enforcement. This research employs a normative legal method using statutory, conceptual, and case study approaches. The findings indicate that the use of cryptocurrency as a medium for terrorist financing still fulfills the elements of a criminal offense as regulated under Law Number 9 of 2013 concerning the Prevention and Eradication of Terrorism Financing. However, the characteristics of cryptocurrency, such as anonymity, decentralization, and cross-border transactions, create significant challenges in the processes of evidence gathering, transaction tracing, and identification of perpetrators. In addition, there is a regulatory gap between the recognition of crypto assets as economic commodities and the supervision of their potential misuse for terrorist financing. Therefore, stronger regulations are needed to explicitly integrate crypto assets into the terrorist financing prevention regime, along with improving the capacity of law enforcement agencies in blockchain transaction analysis and strengthening international cooperation to enhance the effectiveness of law enforcement in the digital economy era.

Although cryptographic security is often the main concern in e-voting system development, practical implementation failures are frequently associated with poor user interfaces that increase cognitive load and voting errors. This study evaluates the effectiveness of User-Centered Design (UCD) interventions in improving usability and voter confidence in an e-voting system. A one-group pretest-posttest design was applied to 36 respondents representing novice voters. Usability was measured using the System Usability Scale (SUS) before and after the interface redesign. The intervention focused on visual hierarchy, navigation consistency, status visibility, and error prevention. The results show a statistically significant improvement, with the mean SUS score increasing from 69.10 in the marginal category to 95.76 in the excellent category. The paired sample t-test produced t(35) = -7.3441 with p < 0.0001, while Cohen's d reached 1.2130, indicating a large practical effect. The standard deviation also decreased from 21.98 to 3.09, demonstrating a more consistent user experience. These findings indicate that interface optimization is essential for strengthening accessibility, voting accuracy, and user trust in digital democracy systems.

Administrative letter services at the village (nagari) level are still largely conducted using conventional methods, resulting in various issues such as limited service hours, slow processing times, and risks of document loss. This study aims to develop a web-based letter service system with the implementation of digital signatures in Nagari Bukit Bais to improve efficiency, security, and transparency of public services. The research adopts the Agile Development method with an iterative approach, including requirement analysis, system design, implementation, and testing. The developed system enables citizens to submit requests online and is equipped with features such as officer verification, digital signing by the village head, automatic notifications, digital archiving, and document verification using QR Codes. Security mechanisms are implemented using SHA-256 cryptographic hashing and RSA-2048 digital signature algorithms, supported by X.509 digital certificates. Functional testing using end-to-end methods shows that all system features operate successfully without failures, while non-functional testing confirms the reliability of document security and integrity. The resulting system is able to automate the entire service process, reduce processing time, and ensure document authenticity and security. Therefore, this system can serve as a solution to support the digital transformation of public services at the village level.

This study analyzes the performance of the Dual Modulus RSA algorithm in securing text data using Python. The rapid growth of digital technology has increased the risk of data security threats, making efficient and secure encryption essential. Dual Modulus RSA is a modification of the classic RSA algorithm that uses two different moduli in the encryption and decryption process, thus increasing security levels because attackers must factorize two moduli simultaneously. This research uses an experimental quantitative approach by measuring the execution time of encryption and decryption processes with variations in plaintext length (5, 10, and 15 characters). Implementation was carried out using Python 3 with the time.perf_counter() function for microsecond-precision measurement. The results show that the Dual Modulus RSA algorithm successfully encrypts and decrypts all test plaintexts correctly. Encryption time ranged from 0.0212 ms to 0.0823 ms, while decryption time ranged from 0.0422 ms to 0.0955 ms. There is a positive linear relationship between plaintext length and processing time. Decryption is consistently slower than encryption due to the larger private key exponent (d1=2753, d2=3533) compared to the public exponent (e=17). The main factors affecting performance are exponent size, dual modulus overhead, CPU caching effects, and Python interpretation overhead. This study recommends using Dual Modulus RSA with hybrid encryption for practical implementation to balance security and performance.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

The rapid expansion of internet usage has led to a significant increase in cybersecurity threats, particularly phishing attacks delivered through malicious links. Phishing links are designed to imitate legitimate websites in order to deceive users and steal sensitive information. This study presents the implementation of a phishing link detection website based on SSL validation and URL scoring mechanisms. The proposed system integrates heuristic-based URL analysis with real-time SSL certificate validation obtained through the SSL handshake process. Digital certificates are verified using RSA-based digital signature verification issued by trusted Certificate Authorities (CAs). In addition, the SHA-256 hash algorithm is employed to generate certificate fingerprints and URL hashes to ensure data integrity and uniqueness. The system also evaluates HTTPS usage, domain and certificate consistency, certificate validity period, and RSA public key strength. All validation results are processed using a URL scoring system to generate a security score ranging from 0 to 100, which classifies links into safe, suspicious, or dangerous categories. Experimental results demonstrate that the proposed website is capable of effectively identifying phishing indicators and providing transparent cryptographic evidence in real time. This approach can assist users in making informed decisions and improving protection against phishing threats in web environments.

This paper reviews the dynamics of cybersecurity in Indonesia, focusing on the role of the Indonesian National Cyber and Crypto Agency (BSSN) in Indonesia’s cybersecurity architecture. The conceptual framework used is cyber security to analyze cybersecurity phenomena in Indonesia and multi-level cybersecurity governance as a tool for analyzing the role of BSSN in the field of cybersecurity. A descriptive qualitative approach is the main methodological approach in this study. With this approach, this study attempts to describe the role of BSSN in Indonesia’s cybersecurity architecture. Data collection was carried out through a literature study obtained from BSSN’s main documents, official reports, and related journal articles. The findings show that BSSN has a strategic role in Indonesian cybersecurity. This strategic role is demonstrated by BSSN’s various achievement in cybersecurity, such as the drafting of a national cybersecurity bill, the development of human resources capacity in the cyber field and the establishment of cooperation with various parties, both domestically and internationally.

This research presents the development of a transaction data protection mechanism for a bouquet sales application by utilizing the Advanced Encryption Standard (AES) algorithm. The rapid growth of digital commerce has led to an increase in online transactions, which in turn raises serious concerns regarding the security of sensitive transaction data. Information such as customer identities, order details, delivery addresses, and payment data are vulnerable to unauthorized access, data leakage, and manipulation if not properly secured. To address these issues, this study applies the AES-128 encryption algorithm using a 128-bit secret key to secure transaction data before it is stored in the system database. The encryption process follows the standard AES workflow, including key expansion, initial transformation, multiple encryption rounds, and a final transformation stage. Decryption is restricted exclusively to authorized users who possess the correct encryption key. The research methodology includes system analysis, AES integration into the application, and functional testing of the encryption and decryption processes. Data integrity is validated by comparing the original plaintext with the decrypted output, while system performance is evaluated based on processing time and decryption accuracy. Experimental results indicate that the average encryption and decryption time remains under 10 milliseconds per transaction, without affecting system performance. The findings confirm that AES-128 effectively enhances transaction data confidentiality and integrity in the bouquet sales application

The rapid development of financial technology (fintech) and crypto assets has created a paradox in the global financial system: while promoting inclusion and efficiency, it also opens new vulnerabilities for money laundering offenses. Notaries, as public officials and legal professionals involved in various financial and business transactions, are at the forefront of preventing this misuse. This article analyzes the specific risks of money laundering through fintech and crypto assets and formulates the due diligence framework required by notaries in carrying out their preventive functions. The research method uses a normative juridical approach with doctrinal analysis of regulations and international standards, enriched with case studies and best practice references. The findings indicate that notaries face three main challenges: (1) difficulty in identifying parties in virtual transactions, (2) the volatility and relative anonymity of crypto assets, and (3) the gap between the speed of technological innovation and regulatory adaptation. This article proposes an Enhanced Digital Due Diligence (ED3) model that integrates digital verification technology, blockchain forensic analysis, and a dynamic risk-based approach. This framework is expected to strengthen the role of notaries as gatekeepers in the rapidly evolving digital financial ecosystem while maintaining the profession's relevance in the era of digital transformation.

Data security is a critical aspect in the digital era due to the increasing exchange of sensitive information through electronic media. One widely used approach to protect data confidentiality is cryptography, particularly asymmetric encryption algorithms. This study aims to analyze the implementation of the Rivest–Shamir–Adleman (RSA) algorithm as a data security mechanism through an encryption and decryption process. The research method used is an experimental approach by implementing the RSA algorithm in a text-based data security simulation. The stages include key generation, encryption, and decryption processes, followed by analysis of the correctness and effectiveness of the algorithm in maintaining data confidentiality. The results show that the RSA algorithm is capable of converting plaintext into unreadable ciphertext and successfully restoring it to its original form through the decryption process using the correct private key. This confirms that RSA provides a high level of security based on the difficulty of factoring large prime numbers. The implication of this study is that the RSA algorithm can be effectively applied to secure sensitive data transmission in information systems, especially in environments requiring strong authentication and confidentiality.

Information security is an essential aspect of digital communication, particularly in the exchange of text-based messages through open networks. Messages transmitted without protection are vulnerable to interception and unauthorized modification. One classical cryptographic technique that remains relevant as a foundational learning tool is the Caesar Cipher algorithm. This study aims to implement the Caesar Cipher algorithm for message encryption and decryption and to analyze its effectiveness and security level. The research method employed is a descriptive approach through literature review and a case study by applying character-shift techniques to text messages. The results indicate that the Caesar Cipher algorithm successfully transforms plaintext into ciphertext and restores it back to its original form through the decryption process. Although the algorithm is simple and easy to implement, it has significant limitations in terms of security due to its small key space and vulnerability to brute-force attacks. Therefore, Caesar Cipher is not suitable for protecting sensitive data but remains valuable as an introductory model for understanding basic cryptographic concepts.

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.

This community service activity aimed to enhance the security and efficiency of halal certification mentoring services at the Aurum First Sunrise community in Surakarta. The main challenge faced by the partner was the risk of sensitive SME data leakage such as ID cards, recipes, and supply chain information, due to the lack of an adequate document security mechanism. The core solution implemented was Technology Implementation in the form of a Cryptographically-based Document Management Information sistem (utilizing the Light Weight PDAC algorithm) integrated with digital access rights management and user Training. Evaluation demonstrated successful implementation, evidenced by an increase in the average satisfaction of SMEs regarding data security to 97.8%, confirming enhanced trust. Furthermore, digitalization successfully improved the efficiency of the mentoring team, reflected by a satisfaction score of 85.0%. In conclusion, this service successfully transformed the partner into a secure, efficient, and credible mentoring institution, significantly supporting SMEs in accessing halal certification.

This Study analyzes the fulfillment of dowry requirements as mutamawwal property according to KHI Articles 30-38 and assesses the suitability of crypto assets as dowry under the positive law regime of the Marriage Law, including its implications for the protection of wives’ rights and legal certainty in the digital era. The research employs a qualititative method with a normative juridical approach through literature study of primary sources (the Qur’an, hadith, KHI, Law No. 1 of 1974, Bappebti and OJK regulations, an MUI fatwas) and secondary sources comparising journals, theses, and dissertations on crypto dowry and maqasid syariah, analyzed descriptively-analitycally and comparatively bertween Islamic law and positive law. The findings that demonstrate that normatively, crypto assets can be classified as valuable property within the KHI framework provided they meet the following criteria: possessing economic value, having clear specifications regarding type and amount, being transferable through legal mechanisms (such as gifts or wallet transfers), and being mutually agreed upon by boh parties. Consequently, they are valid as dowry with the value determined at the time of the marriage contract to minimize the impact or price volatility. However, it still poses potential value disputes, necessitating the strengthening of administrative guidelines at the KUA (office of Religious Affairs) and the enhancement of digital forensic capacty in Religious Courts. The study recommends further harmonization among the KHI, crypto asset regulations, and religious fatwas through the development of technical guidelines for crypto dowries oriented toward mashlahah and the protection of wives’ rights in the 4.0 era.

Steganography is a method to hide confidential messages in digital media so that they are not detected by unauthorized parties. Unlike cryptography which protects the content of messages through encryption, steganography hides the message itself. One popular technique is the Least Significant Bit (LSB), which replaces the least important bit on the pixel with a secret message bit. However, conventional LSB methods such as 1-bit or 3-bit have limitations due to the compromise between insertion capacity and visual quality of the media. This study proposes an LSB-based video steganography method with an adaptive multi-bit embedding approach. This technique determines the number and position of bits that are dynamically inserted based on the local brightness and texture levels of each video frame, with Laplacian operators used to analyze both high and low textured areas. The process includes frame and audio extraction, frame-by-frame embedding, inserted video reconstruction, and decoding using video cover references. The evaluation was carried out quantitatively using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, as well as qualitatively through visual comparison. The results showed that the adaptive multi-bit method was able to maintain visual quality with a PSNR of 45.23 dB and SSIM of 0.9424, and increased the insertion capacity by up to 2–3 times compared to the 1-bit adaptive method. Thus, this approach effectively balances imperceptibility and insertion capacity on dynamic video steganography systems.  

This research aims to enhance the imperceptibility of secret messages in video steganography to prevent detection by third parties. Unlike cryptography, which focuses on securing the message content, this method seeks to conceal the very existence of the message by maintaining a visual quality nearly identical to the original video. The proposed approach utilizes an adaptive multi-bit scheme based on the Least Significant Bit (LSB) technique, which intelligently analyzes the local characteristics of each frame, including brightness, texture, and motion. This strategy allows for higher data insertion in areas with high visual tolerance while limiting bits in sensitive areas to prevent distortion. Evaluation results using PSNR and SSIM metrics indicate that while increasing message capacity (from 1-bit to 3-bit) reduces the PSNR from 51.80 dB to 39.44 dB, the method remains highly effective in preserving visual integrity. Overall, this technique proves to be more secure and superior to traditional LSB in balancing storage capacity with high-quality video output.

Cryptocurrency has become a rapidly developing digital asset class that attracts widespread investor interest due to its decentralized, anonymous, and highly volatile nature. Such volatility creates uncertainty in market movements, making it important to understand the factors that drive fluctuations in returns. This research aims to examine how fundamental indicators namely price, trading volume, and market capitalization affect return volatility, as well as to determine which cryptocurrency demonstrates the highest market efficiency based on risk assessment. The study uses panel data covering the five largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, USD Coin, and Binance Coin) over the period 2019–2023. The analytical methods applied include panel data regression to identify the determinants of volatility and Value at Risk (VaR) to measure asset risk and efficiency. The findings show that price and trading volume positively and significantly increase return volatility, whereas market capitalization exerts a negative and significant effect, indicating its stabilizing role. Based on VaR analysis, Binance Coin (BNB) emerges as the asset with the highest market efficiency. The study concludes that fundamental indicators play a crucial role in shaping volatility and that BNB offers relatively better risk performance compared to its peers.