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This study aims to examine the influence of trust on the perception of transaction security in the use of online shopping applications among millennials. The development of digital technology and the increasing use of online shopping applications have influenced how users assess security risks, personal data protection, and the reliability of transaction systems. This study employs a literature review method by examining relevant previous studies on trust, transaction security, digital risk, and user experience in online shopping. The findings indicate that user trust plays an important role in shaping perceptions of transaction security. Such trust is influenced by the platform’s ability to protect personal data, provide secure payment systems, and ensure smooth transaction processes. The higher the level of user trust in system security, the greater their tendency to use online shopping applications for transactions. Therefore, trust and transaction security are important factors in increasing millennials’ interest and comfort in online shopping.

The rapid development of Artificial Intelligence (AI) has brought significant changes to the criminal justice system, particularly in criminal investigations and evidentiary processes, while simultaneously raising complex legal and ethical challenges. Objective: This study aims to analyze the legal implications of the use of AI in criminal investigations, focusing on its benefits, risks, and challenges related to the admissibility of AI-based evidence, as well as the need for regulatory frameworks that ensure fairness, transparency, and accountability. Methods: This research employs a normative qualitative approach through the analysis of legal regulations, a review of legal and technological literature, and a comparative approach across jurisdictions, complemented by case studies of AI applications in law enforcement practices. Results: The findings indicate that AI enhances investigative efficiency through data analysis, crime prediction, and digital forensics; however, it also poses risks such as algorithmic bias, human rights violations, and issues concerning the reliability and transparency of evidence. Furthermore, differences across legal systems result in the absence of uniform standards for the admissibility of AI-based evidence. Therefore, adaptive regulatory frameworks grounded in the principles of fairness, transparency, and accountability are required, along with strengthened human oversight to ensure that the use of AI aligns with the principles of justice and human rights protection.

Background: Automatic segmentation of the thyroid gland in ultrasonography (USG) images using deep learning requires a user-friendly interface to support diagnostic and educational processes. Purpose: This study aims to develop and implement a Graphical User Interface (GUI) that integrates a deep learning U-Net model for interactive and efficient segmentation and visualization of thyroid USG images. Method: The development method employed the Rapid Application Development (RAD) approach using MATLAB programming language. The GUI is designed to load transverse and sagittal USG images, display automatic segmentation results, and calculate thyroid gland volume based on dimensions measured automatically from the segmentation output. Testing was conducted using USG image data from 15 volunteers, and GUI functionality was evaluated using black box testing. Result: The GUI successfully displayed USG images and segmentation results with a responsive 4-panel interface; zoom, pan, and image navigation features functioned well. Automatic segmentation occurred in real-time after image input, and volume measurement results appeared automatically. Black box testing evaluation showed all GUI features operated as expected. The average Dice Similarity Coefficient (DSC) of 0.91 indicates high performance of the U-Net model in thyroid segmentation, consistent with previous findings. Statistical testing confirmed no significant difference between volume measurements using the application and manual methods (p = 0.953). Conclusion: This GUI implementation facilitates users in performing deep learning-based segmentation and visualization of thyroid USG images, improving efficiency and accuracy in thyroid volume measurement. The GUI has potential applications in clinical practice and radiology education.

The development of digital technology has transformed interpersonal interaction patterns through online dating applications such as Tinder. Even though the use of Tinder in Indonesia has increased significantly, there are striking differences in communication styles between male and female users in managing these digital relationships. This study aims to analyze in depth the communication style of men as Tinder users in the digital dating search process. Using the framework of four main communication styles: emotive, director, reflective, and supportive style traits, this research explores how men navigate interactions that lack verbal cues and are asynchronous. The research method used is phenomenology by collecting data through participatory observation in the application and in-depth interviews with five male informants who are active Tinder users. The results of the study show that each informant applies different communication styles to build comfort and a positive self-image (selective self-presentation) to achieve their romantic goals. These findings identify that variations in men's communication styles are influenced by risk perception, privacy management, and self-presentation strategies in digital spaces. This research contributes to the literature on computer-mediated communication (CMC) in the context of online dating dynamics in Indonesia.

Prodeo legal aid is one of the ways in which the state fulfils its responsibility to guarantee access to justice for economically disadvantaged members of the public. However, its implementation still faces challenges, particularly budgetary constraints, meaning that not all applications for prodeo legal aid can be processed. This issue arises in Industrial Relations Court (PHI) cases at the Banjarmasin District Court. This study aims to examine the implementation of prodeo services in PHI cases and to analyse these budgetary constraints from a constitutional law perspective. The methodology employed is a normative legal approach with an empirical focus, utilising a review of legislation, interviews, observations, and a literature review. The research findings indicate that prodeo is a service for litigation at no cost, funded by the state through the State Budget Allocation (DIPA). In 2025, a budget of Rp. 33,728,000 was only sufficient to handle 13 cases, meaning the service depends on the availability of funds. This situation reflects a gap between the constitutional guarantee of access to justice and practice on the ground. Therefore, improvements are needed in budget planning, allocation, and management, as well as the strengthening of the role of Legal Aid Posts and Legal Aid Institutions to enhance access to justice for the underprivileged.

This study aims to analyze the implementation of e-government in improving the quality of public services at the Population and Civil Registration Office of Gresik Regency. The study uses a qualitative descriptive approach, with data collection techniques including observation, interviews, and documentation. The analysis focuses on the stages of e-government implementation, covering the dimensions of presence, interaction, and transaction in population administration services. The results indicate that the implementation of e-government at the Population and Civil Registration Office of Gresik Regency has been carried out through the provision of digital information media, such as an official website and online-based population administration service applications. In the presence dimension, the local government provides various information related to population administration services, including requirements, procedures, service times, and the types of services available to the public. In the interaction dimension, the digital service system allows the public to communicate with the service office by submitting questions, complaints, or requests for information online. Meanwhile, in the transaction dimension, the public can submit requests for population documents, such as Family Cards, birth certificates, and other documents, through the digital service system. The implementation of e-government has positively impacted the efficiency, transparency, and ease of access to population administration services for the public. Therefore, the utilization of information technology in public services can serve as an important strategy for improving the quality of population administration services in local government.

Digital transformation in the public sector has significantly driven service innovations, particularly in pension administration for Civil Servants (ASN). This study aims to analyze the development of digital-based public service innovations in pension administration while identifying the benefits and challenges associated with their implementation. The research employs a Systematic Literature Review (SLR) method by examining relevant scholarly articles from national journals focusing on the digitalization of public services and pension systems. The literature selection process was conducted systematically to identify, evaluate, and synthesize key findings related to digital pension service innovations. The results indicate that digitalization through applications and electronic platforms enhances administrative efficiency, accelerates data verification, and improves the speed of pension fund disbursement. It also strengthens transparency and accountability while simplifying bureaucratic procedures and expanding service accessibility for retirees. However, several challenges remain, including low digital literacy among retirees, limited access to technological devices, and insufficient public awareness regarding digital service usage. The findings suggest that the success of digital-based public service innovations depends not only on technological availability but also on human resource readiness, institutional capacity, and the level of public acceptance. Therefore, a comprehensive strategy is required, including improving digital literacy, strengthening information technology infrastructure, and optimizing communication efforts to ensure effective adoption.

Digital transformation in the public sector has driven various service innovations, including pension administration services for State Civil Apparatus (ASN). This study aims to analyze the development of digital-based public service innovations in pension administration and identify the benefits and challenges of their implementation. This study used the Systematic Literature Review (SLR) method by reviewing various relevant scientific articles from national journals that discuss the digitalization of public services and pension administration. The literature selection process was carried out systematically to identify, evaluate, and synthesize research findings related to digital-based pension service innovations. The review results indicate that digitalization of pension administration services through the use of electronic service applications and platforms can improve administrative efficiency, accelerate data verification and pension fund disbursement, and increase transparency and accountability in public services. The implementation of digital services also contributes to simplifying bureaucratic procedures and increasing service accessibility for retirees. However, the literature review also revealed challenges in implementing digital pension services, including low digital literacy among retirees, limited access to technological devices, and suboptimal dissemination of service information. The findings of this study indicate that the success of digital-based service innovations depends not only on technology, but also on human resource readiness, the organizational capacity of government institutions, and the level of public acceptance of the use of digital technology. Therefore, developing digital-based pension services requires a comprehensive strategy.

This study aims to analyze the Transformation of E-Government Towards E-Governance in the Public Service Process at the Population and Civil Registration Office of Gresik Regency. The utilization of information technology in public services is one of the government’s efforts to improve administrative efficiency, transparency, and the quality of services provided to the public.This research employs a qualitative approach with a descriptive method to provide an in-depth depiction of the implementation of E-Governance in population administration services. Data collection techniques include interviews, observations, and documentation, while data analysis follows the stages of Grouping the data according to key constructs, Identifying bases for interpretation, Developing generalizations from the data, Testing alternative interpretations, and Forming and/or refining generalizable theory from the case study.The results indicate that the implementation of the Transformation of E-Government Towards E-Governance in the Public Service Process at the Population and Civil Registration Office of Gresik Regency is carried out through several key dimensions, namely E-Administration, E-Service, and E-Society. The E-Administration dimension is reflected in the use of digital-based administrative systems for managing population data and processing document applications electronically. The E-Service dimension shows that online services provide easier access for the public to manage population documents more quickly and efficiently. Meanwhile, the E-Society dimension demonstrates an increased utilization of information technology by the public in accessing population administration services. Nevertheless, the implementation of digital services still faces several challenges, such as limited digital literacy among the public and uneven internet access. This study concludes that the application of E-Governance in population administration services in Gresik Regency has made a positive contribution to improving the quality of public services through the utilization of information technology.

Implementing REST in modern applications, security will be a key foundation for its development because the REST architecture requires communication between servers. In this study, we will enhance REST request security by using SHA-1 tokens and the Keccak algorithm. Tokens are the access keys for making requests. The token generation process is carried out on the server; the client will generate a token, and the server will return a valid token. This valid token can be used to request data from the server. Adding a token will impact the security and speed of REST. The token will be verified by the server and declared valid. If valid, the server will return the data; otherwise, the server will send an error message. Compared to using a token, data security is more assured. Furthermore, adding a token parameter will increase the token verification process, thus increasing the number of processes, which will impact speed. The results of this test show that server data security is better maintained and more secure compared to using a token, because anonymous users cannot access the data. The API access speed without a token is 48.8 milliseconds, while using a SHA-1 token is 62.3 milliseconds, and the Keccak algorithm is 58.9 milliseconds. The time efficiency reduction for implementing the SHA-1 token algorithm is 27.67% or 13.5 milliseconds, and the Keccak algorithm is 26.6% or 10 milliseconds.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

This article explores mathematical logic as a fundamental discipline within mathematics that systematically investigates the principles of valid, coherent, and consistent reasoning. Mathematical logic serves as an essential foundation for developing structured thinking and constructing well-grounded, persuasive arguments supported by clear justification. It primarily focuses on the processes of reasoning, argumentation, and formal proof, enabling individuals to distinguish between valid and invalid conclusions. By establishing precise rules and logical structures, mathematical logic provides a rigorous framework for assessing the strength and consistency of arguments. The discipline emphasizes clarity, objectivity, and analytical accuracy in examining propositions and their relationships. In practice, mathematical logic is presented through the use of symbolic representations, formal logical statements, connectives, quantifiers, and truth tables to analyze patterns of reasoning and verify the truth values of statements. These systematic tools allow complex ideas to be evaluated methodically, prevent logical fallacies, and ensure that conclusions are derived from sound and demonstrable reasoning principles.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

Educator certificates are official documents that play a crucial role for teachers, as they serve as legal proof of professional competence and are required for various administrative purposes, such as professional allowance applications, promotion, transfer, and institutional accreditation. Along with the increasing number of educators in Indonesia, the volume of educator certificate data managed by educational institutions has also grown significantly. However, certificate management is still largely conducted in a conventional manner, functioning merely as digital or physical archives without an effective search mechanism, resulting in inefficiencies and difficulties in retrieving relevant documents. Therefore, an information retrieval approach is needed to support fast and accurate document searching. This study aims to analyze and implement an information retrieval system for educator certificates using the Cosine Similarity method. The research data consist of educator certificate documents, including professional educator certificates, training certificates, and competency certificates. The retrieval process involves text preprocessing, term weighting using TF-IDF, and similarity measurement using Cosine Similarity. The results show that document d1 (Professional Mathematics Educator Certificate) has the highest similarity value to the query “educator certificate,” as it contains all query terms with relatively high TF-IDF weights. Document d3 ranks second due to partial term similarity, while document d2 has the lowest similarity value because it shares only one common term with the query. These findings indicate that the Cosine Similarity method is effective in ranking educator certificate documents based on their content relevance in an objective and measurable manner. The proposed system can improve the efficiency and accuracy of educator certificate document management and retrieval in educational institutions.

This study aims to map the intellectual structure and research trends in entrepreneurial innovation using bibliometric analysis based on Scopus data. A total of 891 documents published between 1972-2025 were analyzed through Bibliometrix and Biblioshiny, employing techniques such as bibliographic coupling, co-authorship, and thematic mapping. The results reveal four major clusters: (1) innovation theory and entrepreneurial development, (2) business model innovation and digital transformation, (3) regional innovation systems and policy frameworks, and (4) sustainability and green entrepreneurship. Emerging themes include artificial intelligence (AI), generative AI applications, and digital entrepreneurship education, indicating a shift toward multi-level and interdisciplinary integration. Influential documents and authors were identified, highlighting their role in shaping the knowledge base. The findings suggest that entrepreneurial innovation research is evolving toward digitalization, sustainability, and policy-driven ecosystems, offering opportunities for longitudinal and mixed-method studies. This study contributes by providing a comprehensive overview of the field, identifying gaps, and proposing future research directions to strengthen theoretical and practical advancements.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

Goals – Goals from studies This is For explore approach strategic in development System Information Management (SIM) as integral part in support digital transformation of modern organizations. Study This emphasize importance integration technology information , effective data management as well as improvement digital competence resources Power man in operation system. Design/ methodology / approach – Conceptual article This use method review library with analyze various work relevant academic and technical manuals , in particular related implementation of SIM in the sector public and private . Study This referring to the works Jatnika et al. (2022–2024), including utilization Microsoft Office applications as skills supporting basis​ organizational digital literacy . Findings – Findings studies This show that SIM development is not just effort technical , but rather need strategic in support digital transformation . Key strategies covers design modular systems , data mining integration , training programs based users , and evaluation system in a way periodic . Components This allows organization build responsive and adaptive SIM ecosystem . Implications practical – Organizations that want to do digital transformation is necessary invest in development digital capabilities of sources Power the human as well as ensure effectiveness use developed SIM system in a way strategic can become driving force main in increase efficiency , accuracy , and capability taking decision across work units . Originality / value – Study This offers a conceptual model structured about development of SIM in context digital transformation , based on literature applications and needs organizations in the real world . This article give outlook practical for taker policy , IT managers , and HR developers .  

This study aims to analyze the acoustic characteristics of coconut-fiber-based Cement-Bonded Particleboard (CBPB) through measurements of the Sound Absorption Coefficient (SAC, α) and Transmission Loss (TL). CBPB samples were fabricated with thickness variations of 12, 16, and 20 mm and tested using an impedance tube in accordance with ISO 10534-2:1998 for SAC and ASTM E90-09 (2016) for TL measurements. The study examined the effect of panel thickness on sound absorption and transmission loss across a frequency range of 125–4000 Hz. The results showed that the α value increased with both frequency and panel thickness, reaching a maximum of 0.78 at frequencies of 2500–3150 Hz for the 20 mm panel. The TL measurements indicated that the highest transmission loss reached 42 dB at a frequency of 4000 Hz. These findings suggest that thicker CBPB panels provide better acoustic performance, both in absorbing sound and reducing transmission. Overall, coconut-fiber-based CBPB demonstrates strong potential as an eco-friendly structural acoustic material suitable for applications in sustainable building design, interior partitions, and noise control solutions.

This study aims to evaluate the financial recording system implemented by Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector, and analyze its impact on business performance. MSMEs are a very dynamic sector and contribute significantly to the national economy, but many of them do not yet have an adequate financial recording system. Good financial recording is key in business decision making, budget planning, and profitability assessment. The population in this study were 370 culinary MSMEs in Harjosari I Village. The research sample was taken at 20%, namely 74 respondents. The study used a quantitative approach with a survey method through the distribution of questionnaires to 74 culinary MSMEs in Harjosari I Village, Medan Amplas. Data analysis was carried out using simple linear regression to see the relationship between the quality of the financial recording system as an independent variable and business performance as a dependent variable. The results showed that the better the financial recording system, the better the business performance, this can be shown by the regression equation, Y = 1.395 + 0.308 + e. The research results indicate that improving accounting literacy and the use of digital technologies, such as MSME bookkeeping applications, are essential. The involvement of local governments and financial institutions is crucial in educating and facilitating digital-based bookkeeping systems for culinary MSMEs to enhance their competitiveness and business sustainability.