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This research aims to analyse the role of archives as agents of change in the digital transformation of archives and as catalysts for bureaucratic reform in the era of digital government at the Sorong City Regional Secretariat. The research used a mixed-methods approach, combining a quantitative survey of 30 respondents with in-depth interviews with five key informants, and analysed the data using correlation statistics and thematic analysis. The study's results show that the digital transformation of archives is closely linked to bureaucratic reform and digital governance, and qualitative findings confirm that digitisation improves administrative efficiency, data integration, and organisational accountability. The research highlights the importance of strengthening technological infrastructure, integrating information systems, and improving the competence of the apparatus as prerequisites for the success of the digital transformation of archives. This study has limitations in its scope, involving only one local government agency and a relatively small number of respondents. Therefore, further research is recommended to broaden the scope, incorporate public service quality variables, and use a longitudinal design to examine the long-term impact of archive digitisation on the performance of bureaucratic reform and digital governance more comprehensively. These findings also provide practical contributions for local governments in designing integrated, sustainable electronic archive management policies that support effective, transparent, and responsive public services to meet the needs of modern society in the future, as well as strengthening data-driven governance.  

There are various characters that can be drawn from the One Piece animation that need to be studied and explored further so that the large number of fans can emulate the relevance of Quranic characters in their favorite animation. This research stems from the phenomenon of discovering the representation of character relevance to the teachings of the Qur'an in an animation called One Piece, focusing on the research to determine how the Quranic character traits are depicted in the One Piece Arabasta Arc, and how the visualizations of these Quranic characters are presented in the One Piece Arabasta Arc. This research uses a qualitative approach. The research results illustrate that there are 18 findings in episodes 97, 99, 103, and 108 that indicate values in line with the character traits or moral teachings of the Qur'an. In episode 97, there are 4 character values: stop complaining, avoid greed and wastefulness, don't lie, and help others. In episode 99, there are 6 character values: love for the homeland, helping others, fighting for a cause, feeding the hungry, and consulting others. Meanwhile, in episode 103, there are 4 character values: obedience, fighting for a cause, friendliness, and avoiding bad assumptions. Additionally, in episode 108, there are 4 character values: helping others, fighting for a cause, and avoiding bad assumptions. Furthermore, the visualization of Quranic characters in the One Piece animation can be seen through the elements of film production, using narrative elements by observing the characters and the message of the story.

Indonesia is the largest archipelagic country in the world, Belawan Ocean Fishing Port is one of the largest fishing ports in WPP-NRI 571, strategic position because it is located between the waters of the East Coast and the South China Sea. The status of fish resource utilization in Belawan Ocean Fishing Port which has been fully exploited makes a study to analyze the level of fishing gear use in Belawan Ocean Fishing Port. This study aims to determine the level of environmental friendliness of purse seine fishing gear. This study was conducted at Belawan Ocean Fishing Port in November-December 2025. The research method used was a survey method, direct observation and interviews using a questionnaire. The sample used was 30 respondents. The analysis of the level of environmental friendliness of fishing gear was carried out in accordance with the criteria of 9 Code of Conduct for Responsible Fisheries (CCRF). The results of the calculation of the environmental friendliness score were 30.2 out of a total of 908 points for the level of friendliness of purse seine fishing gear at Belawan Ocean Fishing Port, and this fishing method is categorized as a very environmentally friendly fishing gear.

This community service program aims to strengthen high school graduates’ understanding of the architectural profession, educational pathways, and legal qualifications required to become a professional architect under the Indonesian Institute of Architects (IAI). The online outreach was held on November 27, 2025, featuring two licensed architects from IAI and attended by 97 participants. The activities included interactive presentations, discussions, and an evaluation through a five-item pretest and post-test. The results show an overall improvement in participants’ comprehension of architects’ roles, responsibilities, and professional legality, with an average accuracy above 90%. The greatest improvement occurred in the legal aspect, where participants recognized the importance of the Surat Tanda Registrasi Arsitek (STRA) as a legal credential for professional practice. This program effectively increased prospective students’ awareness of professionalism, ethics, and regulatory understanding in architectural practice. It also served as an academic promotion platform that bridges architectural education with society’s demand for competent and ethical architects.

The implementation of the Global Minimum Tax (GMT) through Pillar Two of the OECD/G20 marks a fundamental change in the international tax architecture, especially for developing countries such as Indonesia. One of the key instruments in Pillar Two is the Qualified Domestic Minimum Top-Up Tax (QDMTT), which provides an opportunity for source countries to retain the right to tax the profits of multinational companies with an effective tax rate below 15 percent. This study aims to analyze Indonesia's readiness to face the implementation of GMT through the QDMTT policy, focusing on regulatory aspects and tax administration capacity. The research method uses literature studies with a qualitative-descriptive approach through the analysis of policy documents, tax regulations, as well as academic literature and international reports. The results of the study show that Indonesia's readiness is still in the transition stage. In terms of regulation, Indonesia has shown an initial commitment through the issuance of PMK Number 136 of 2024, but the regulation still needs to be strengthened at a higher level of regulation for long-term legal certainty. From the administrative aspect, the main challenges include the complexity of calculating jurisdiction-based Effective Tax Rates, cross-border data management, as well as increasing the capacity of human resources and information technology infrastructure. This study concludes that the success of QDMTT implementation in Indonesia depends on strengthening regulations, increasing tax administration capacity, and reformulating sustainable investment policies.

The café industry has become increasingly competitive, including in the Arcamanik District of Bandung City. This study seeks to investigate the extent to which marketing intelligence capabilities influence the competitiveness of café MSMEs in the region. A descriptive and verificative research approach was employed, utilizing linear regression analysis with the assistance of SPSS version 20 software. The study population consisted of 43 café MSME entrepreneurs, selected through a saturated sampling technique, thereby including all members of the population. Data were collected through field studies—comprising questionnaires, interviews, and direct observations—as well as literature reviews to strengthenthe theoretical framework. The results demonstrate that marketing intelligence capabilities have a significant effect on competitiveness. This finding highlights the critical role of marketing intelligence in enabling café MSMEs to sustain their operations and enhance their competitive advantage. Strengthening marketing intelligence can serve as an effective strategic approach for café MSMEs to ensure business sustainability and improve competitiveness. The study suggests that future research may incorporate moderating variables such as digitalization or service quality to further explore the dynamics between marketing intelligence and competitiveness.

The café industry has become increasingly competitive, including in the Arcamanik District of Bandung City. This study seeks to investigate the extent to which marketing intelligence capabilities influence the competitiveness of café MSMEs in the region. A descriptive and verificative research approach was employed, utilizing linear regression analysis with the assistance of SPSS version 20 software. The study population consisted of 43 café MSME entrepreneurs, selected through a saturated sampling technique, thereby including all members of the population. Data were collected through field studies—comprising questionnaires, interviews, and direct observations—as well as literature reviews to strengthenthe theoretical framework. The results demonstrate that marketing intelligence capabilities have a significant effect on competitiveness. This finding highlights the critical role of marketing intelligence in enabling café MSMEs to sustain their operations and enhance their competitive advantage. Strengthening marketing intelligence can serve as an effective strategic approach for café MSMEs to ensure business sustainability and improve competitiveness. The study suggests that future research may incorporate moderating variables such as digitalization or service quality to further explore the dynamics between marketing intelligence and competitiveness.

This community service activity was motivated by the limited capacity of village officials in providing government administration services, particularly in managing correspondence, archiving documents, and implementing standard operating procedures for services. This condition has resulted in the suboptimal quality of public services at the village level. The objective of this activity was to improve the capacity and competence of village officials in providing village government administration services to be more effective, efficient, and accountable. The method used was a participatory approach through the stages of problem identification, joint planning, regulation socialization, technical training, service simulations, and implementation assistance. The subjects of the activity were officials from Kadur Village, North Rupat District, Bengkalis Regency. The results of the activity showed an increase in the officials' understanding and skills in administrative management, the development of a more standardized administrative document format, and a growing collective awareness of the importance of orderly administration as part of good governance. In addition, internal leadership initiatives emerged that encouraged sustainable change within the village government environment. Overall, this activity had a positive impact on improving the quality of village administration services and was the first step towards a more professional transformation of village governance.

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

This research aims to develop and assess the feasibility of Microsoft Access-based learning media as a digital archive tool. Using a Research and Development (R&D) approach, the study follows the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The participants included one media expert, one material expert, one language expert, and 33 class X MPLB students at SMK Negeri 49 Jakarta. Data collection was conducted through observations, interviews, and questionnaires rated on a Likert scale, evaluated by experts and students. The findings show that: 1) The Microsoft Access-based media, developed for the MPLB basics subject, can be run on laptops or computers; 2) The media suitability received an 81.3% rating ("Very Appropriate"), material suitability 89.3% ("Very Appropriate"), and language suitability 100% ("Very Appropriate"). In the trials, individual testing with three students yielded 89.6% ("Very Eligible"), small group testing with 10 students received 75.7% ("Decent"), and field testing with 20 students reached 86.8% ("Very Eligible"). 3) The product is deemed highly suitable by experts, with strong support from small group and field trials, confirming its effectiveness as a learning tool.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs), exploiting their inability to separate instructions from data within application contexts reliably. This paper provides a structured review of current attack vectors, including direct and indirect prompt injection, and highlights the limitations of existing defenses, with particular attention to the fragility of Known-Answer Detection (KAD) against adaptive attacks such as DataFlip. To address these gaps, we propose a novel, hybrid, multi-layered detection framework that operates in real-time. The architecture integrates heuristic pre-filtering for rapid elimination of obvious threats, semantic analysis using fine-tuned transformer embeddings for detecting obfuscated prompts, and behavioral pattern recognition to capture subtle manipulations that evade earlier layers. Our hybrid model achieved an accuracy of 0.974, precision of 1.000, recall of 0.950, and an F1 score of 0.974, indicating strong and balanced detection performance. Unlike prior siloed defenses, the framework proposes coverage across input, semantic, and behavioral dimensions. This layered approach offers a resilient and practical defense, advancing the state of security for LLM-integrated applications.

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

This research investigates thermal material properties and performance characteristics for high-speed vessel components subjected to extreme thermal stress during sustained high-speed operations. High-speed vessels including patrol boats, fast ferries, and naval craft experience elevated thermal loads from high-power density propulsion systems, aerodynamic heating, and sustained operational intensities creating demanding conditions for structural and mechanical components. Through qualitative analysis involving naval architects, materials engineers, high-speed vessel operators, and component manufacturers, this study examines how material thermal properties affect component durability, performance, and safety while identifying optimal material selections for critical applications. Results demonstrate that advanced thermal materials including high-temperature aluminum alloys, titanium alloys, ceramic composites, and thermal barrier coatings can extend component service life by 40-70%, improve thermal management effectiveness by 25-45%, and enhance operational reliability compared to conventional materials. Key implementation challenges include material cost premiums of 150-300%, manufacturing complexity, limited operating experience, qualification testing requirements, and supply chain constraints. Findings reveal that strategic thermal material selection for critical components represents essential enabling technology for high-speed vessel performance, reliability, and operational availability supporting defense, commercial, and emergency response applications requiring sustained high-speed capabilities. This research contributes to marine materials engineering literature by providing evidence-based frameworks for thermal material selection applicable to diverse high-speed vessel applications.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.

This research investigates integrated legal-human resource frameworks for autonomous vessel operations in Indonesian archipelagic waters, addressing regulatory compliance gaps and seafarer workforce transition challenges. Through qualitative analysis involving 38 stakeholders including maritime lawyers, regulatory officials, ship operators, seafarer unions, training institutions, and autonomous technology developers, this study examines how existing maritime legal frameworks prove inadequate for unmanned operations while workforce displacement threatens 150,000+ Indonesian maritime workers. Results demonstrate that successful autonomous vessel adoption requires coordinated legal-HR approaches addressing liability allocation (achieving 75-85% clarity through multi-party frameworks), competency certification for remote operators (reducing training gaps by 60-70%), career transition pathways (enabling 55-65% workforce adaptation), and regulatory harmonization (improving compliance efficiency by 45-60%). Key barriers include UNCLOS Article 94 incompatibility, insurance unavailability, seafarer resistance, and jurisdictional fragmentation. Findings reveal that archipelagic contexts demand unique legal-HR solutions integrating traditional maritime rights, hybrid operational modes, and just transition principles. This research contributes frameworks enabling Indonesia to proactively shape autonomous vessel regulations protecting both technological innovation and maritime workforce interests during critical technology transition.