Publication Search

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

Digital copyright piracy remains a growing legal issue in Indonesia alongside the rapid development of information technology. This practice not only causes economic losses to creators and copyright holders but also reflects a gap between legal norms and social realities. This study aims to analyze legal protection for digital copyrights and to assess the effectiveness of Law Number 28 of 2014 on Copyright. This research employs a normative legal method using a library research approach by examining statutory regulations and relevant academic literature. The findings indicate that although Indonesia has established an adequate legal framework for copyright protection, its implementation remains ineffective due to low public legal awareness, weak law enforcement, and challenges in monitoring digital platforms. These findings emphasize that effective copyright protection requires not only comprehensive regulations but also synergy between the government, law enforcement authorities, and the development of a strong legal culture within society.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.

This study identified the use of abbreviations in Medical Treatment Consent Forms (SPTK) at X Hospital Indonesia. A quantitative cross-sectional descriptive approach was applied to 76 SPTKs in September 2024, and questionnaires were administered to 30 patient-responsible physicians (DPJP). The results showed that 75% of SPTKs contained abbreviations, even though 97% of respondents understood the risk of miscommunication to patient safety. The state of the art includes accreditation standards that prohibit the use of abbreviations in informed consent, with global orthopedic studies reporting a decrease from 54% to 22% after educational interventions, as well as Indonesian regulations, namely Peraturan Mentri Kesehatan (Permenkes) Republik Indonesia No. 24/2022, which emphasizes that medical records must be complete. The novelty lies in the first empirical analysis in Indonesian hospitals to reveal the disparity between high physician knowledge and low documentation compliance, contributing to the development of evidence-based monitoring for patient safety. These findings support recommendations for daily review of SPTK, ongoing socialization, and integration of digital checklists to reduce medical errors.

This study was conducted to examine the level of implementation of Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM) at Kedai Nduy, a micro-scale culinary business located in Depok. The basis for conducting this research is the low quality of financial records among UMKM players due to a lack of understanding of accounting, limited assistance from competent parties, and the suboptimal implementation of financial reporting standards, which are the main obstacles. This research was conducted using a descriptive qualitative method, where data was collected through interviews, direct observation, and document searches related to transaction recording activities, sales, raw material purchases, and daily cash flow. The results of the study show that Kedai Nduy has consistently recorded transactions using a simple financial recording format using Excel tables, but the implementation does not fully comply with SAK EMKM provisions. This business entity has also not prepared the required financial statements, namely the Statement of Financial Position, Income Statement, and Notes to the Financial Statements. The main obstacles faced are limited understanding of accounting, lack of time available to business owners, and the unavailability of technical assistance and accounting training. Nevertheless, simple record keeping still provides benefits, particularly in monitoring sales, purchasing, and daily cash flow activities. Therefore, this study recommends accounting training, the use of SAK EMKM-based applications or templates, and ongoing assistance to improve the quality of financial recording and reporting in accordance with applicable standards.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

Chronic kidney disease (CKD) is a global health problem with continuously increasing prevalence and mortality. This condition is characterized by a progressive and irreversible decline in kidney function, which is clinically assessed using the estimated glomerular filtration rate (eGFR) as one of the main parameters for staging and monitoring disease progression. Red blood cell distribution width (RDW) is a hematological parameter that reflects the degree of variation in erythrocyte size. In patients with CKD, elevated RDW levels are often associated with oxidative stress and chronic inflammation, which may accelerate kidney damage and increase mortality risk. To analyze the correlation between RDW and eGFR values in patients with chronic kidney disease at ODSK Hospital, North Sulawesi Province. This study used an analytical observational design with a cross‑sectional approach. It was conducted retrospectively using secondary data from the medical records of CKD patients at ODSK Hospital, North Sulawesi Province, from February 2024 to August 2025. This study used a total sampling method with 189 CKD patients who met the inclusion and exclusion criteria.  Based on Spearman-rank correlation analysis, the p‑value obtained was 0.129 (>0.05). There was no significant relationship between RDW and eGFR values in CKD patients at ODSK Hospital, North Sulawesi.Province.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

This community service programme was implemented in the coastal community of Padelegan Village, Pademawu Subdistrict, Pamekasan Regency, with a focus on strengthening health literacy and behaviour-based disease prevention at the household level. The background of the activity is based on the vulnerability of coastal communities to health problems related to hygiene, household drinking water management, and environmental cleanliness, as well as the need for an approach that not only increases knowledge but also encourages consistent preventive practices. The programme aims to improve residents' ability to understand and use health information in an applicable manner, while strengthening key disease prevention behaviours through education and mentoring. The activity will be carried out from February to April 2025 with a behaviour-based education design combined with practice demonstrations and community mentoring, involving 48 participants (40 residents/households and 8 posyandu/PKK cadres). The stages included initial assessment, development of KIE media (brief modules and leaflets/posters), two education-demonstration sessions, two home mentoring sessions, and final evaluation. The evaluation was conducted using pre-post knowledge, attitude, and practice (KAP) assessments and practice observation through a checklist. The results showed an increase in health literacy and improved consistency in preventive behaviour; knowledge increased from 5.4 to 7.8, and compliance with hand washing at critical times increased from 41.7% to 72.9%. Programme outputs included an information, education and communication (IEC) package, a household monitoring checklist, and capacity building for cadres as local facilitators. It was concluded that the integration of participatory education, practical demonstrations and cadre mentoring has the potential to be effective in encouraging preventive behavioural change in coastal communities, with recommendations for integrating monitoring into the routine agenda of integrated health service posts (posyandu) and community empowerment groups (PKK) and for follow-up monitoring for 3–6 months to maintain the sustainability of practices.

Malnutrition in children remains a serious public health challenge in Indonesia because it directly impacts growth, cognitive development, and the child's future quality of life. Parents play a crucial role in maintaining children's health, particularly in providing balanced nutrition and preventing nutritional problems such as stunting and malnutrition. This study aims to analyze the role of parents in maintaining children's health due to malnutrition based on a literature review. The method used is a qualitative literature study, through a search of various written sources in the form of scientific journals, articles, and relevant books obtained from Google Scholar. The results of the study indicate that the level of knowledge, education, parenting patterns, socioeconomic conditions, and active involvement of parents in monitoring children's growth significantly influence children's nutritional status and health. Parents play a role as providers of nutritious food, nutrition educators, role models of healthy lifestyles, and regular monitors of children's growth and development. Parents' lack of understanding of balanced nutrition, the habit of consuming instant foods, and minimal participation in health programs are risk factors for malnutrition in children. Therefore, increasing parental education and involvement through a family-based approach is an important strategy in efforts to prevent and address child nutrition problems sustainably.

Diabetes mellitus (DM) is a global health problem characterized by increased blood glucose levels. Type 2 diabetes mellitus (T2DM) accounts for 90-95% of all diabetes cases and is characterized by insulin resistance. Persistent hyperglycemia in patients with T2DM increases the risk of various complications, including renal dysfunction. Therefore, monitoring HbA1c levels is important for assessing long-term glycemic control and preventing complications, one of which is impaired kidney function that can be detected through increased blood creatinine levels. To analyze the relationship between HbA1c levels and blood creatinine levels in type 2 diabetes mellitus patients at RSUD ODSK North Sulawesi Province. This study used an observational analytic design with a cross-sectional approach. This study was retrospective using secondary data in the form of medical record data for the period February 2024-August 2025. This study used a total sampling method with 71 patients who met the inclusion criteria. The findings showed that the majority of patients had HbA1c levels >6,5%, while most blood creatinine levels were within the normal range. Based on the results of the Spearman-Rank correlation test, a significance value of 0.040 (p-value <0.05) was obtained. There is a significant relationship between HbA1c levels and blood creatinine levels, with a negative correlation strength in patients with type 2 diabetes mellitus at RSUD ODSK North Sulawesi Province.

Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare, life-threatening mucocutaneous reactions characterized by extensive epidermal necrolysis and mucosal involvement. The entities represent a clinical continuum distinguished by the extent of epidermal detachment (SJS <10% body surface area [BSA], SJS/TEN overlap 10–30%, TEN >30%). Most cases are drug-induced, making early culprit-drug withdrawal and structured supportive care the cornerstones of management. Immunopathogenesis is primarily mediated by drug-specific cytotoxic T cells and NK cells through granulysin, Fas–Fas ligand, and perforin/granzyme pathways, with emerging roles of necroptosis and candidate biomarkers such as RIP3 and galectin-7. Diagnosis relies on timely recognition of the characteristic painful erythematous/targetoid lesions, Nikolsky/Asboe–Hansen signs, multi-site mucositis, accurate BSA assessment, and early severity scoring (e.g., SCORTEN) to guide referral and monitoring. Systemic immunomodulators (corticosteroids, cyclosporine, IVIg, anti-TNF agents) have variable evidence; thus, individualized selection and early multidisciplinary care particularly ophthalmologic involvement are essential to reduce acute mortality and long-term sequelae.

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of peripheral vertigo, characterized by brief episodes of vertigo due to otoconia displacement. Although most previous studies have focused on intrinsic factors such as age, gender, osteoporosis, and metabolic disorders, evidence regarding the role of environmental factors, particularly occupational noise exposure, is limited. Chronic noise has the potential to affect vestibular function through both sensory and vascular mechanisms. This study aims to narratively review the effect of occupational noise exposure on the risk of BPPV by integrating clinical, epidemiological, and experimental findings. The method used is a literature-based narrative review of the PubMed, Scopus, Web of Science, and Google Scholar databases without year restrictions, using the keywords "BPPV", "occupational noise exposure", "vestibular dysfunction", "VEMP", and "otoconia displacement". The search results obtained 25 relevant articles linking BPPV to otolith, hormonal, vascular, lifestyle factors, and occupational noise exposure. The results indicate that chronic noise can cause sensory damage (otoconia and vestibular hair cells), vascular disorders (hypertension, cardiovascular disorders, and inner ear microvascular circulation disorders), and exacerbate lifestyle comorbidities (sedentary lifestyle, osteoporosis, hypertension, diabetes). The discussion confirms that these multifactorial mechanisms explain the susceptibility of industrial workers to BPPV despite normal hearing function. The conclusion of this study is that workplace noise exposure has been shown to play a significant role as a risk factor for BPPV, therefore, prevention strategies, vestibular health monitoring, and healthy lifestyle interventions need to be optimized in occupational health programs.

This study investigates the levels of asprosin and irisin in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), and evaluates their relationships with insulin resistance, glycemic control, and renal function. Additionally, it explores their diagnostic performance as potential non-invasive biomarkers for the early detection of DN using ROC curve analysis. A controlled prospective study was conducted involving 130 participants, categorized into healthy controls, T2DM patients, and T2DM with DN. Serum asprosin, irisin, and insulin levels were measured using ELISA, while biochemical and renal parameters such as fasting blood glucose, HbA1c, creatinine, urea, and eGFR were assessed using standard spectrophotometric techniques. ROC analysis was employed to assess the diagnostic accuracy of asprosin and irisin. Asprosin levels were significantly elevated, while irisin levels were markedly reduced in DN patients compared to T2DM and control groups (p < 0.001). Moreover, DN patients exhibited higher levels of fasting glucose, HbA1c, HOMA-IR, creatinine, and urea, with lower eGFR, indicating substantial renal dysfunction. ROC analysis revealed that asprosin had an AUC of 0.910 (95% CI: 0.839–0.981), with 90.0% sensitivity and 88.9% specificity at a cutoff value of 11.27 ng/mL. Irisin showed an AUC of 0.886 (95% CI: 0.812–0.960), with 95.0% sensitivity and 71.1% specificity at a 158.25 ng/mL cutoff. Asprosin and irisin are strongly associated with insulin resistance and renal impairment in patients with T2DM and DN. Their high diagnostic performance supports their utility as promising non-invasive biomarkers for the early detection and monitoring of diabetic nephropathy progression.

This community service activity aimed to strengthen teachers’ competencies in utilizing digital platforms to support project-based learning at a vocational high school. The background of this activity was the limited ability of teachers to manage project-based learning in a structured and systematic manner using digital platforms. The method employed was participatory mentoring, involving needs analysis, activity planning, implementation of digital platform mentoring, monitoring and evaluation, and reflection with follow-up actions. The participants were teachers of SMK Negeri 1 Tukak Sadai, Bangka Barat. The results indicated an improvement in teachers’ understanding and skills in designing, managing, and evaluating project-based learning using digital platforms. Teachers became more confident in integrating technology into learning processes and demonstrated better organization of project activities. This activity contributed to enhancing teachers’ digital competencies and improving the quality of project-based learning practices in vocational education. The findings suggest that participatory mentoring is an effective approach to supporting sustainable digital learning innovation in vocational schools.

The public complaint system is an important facility for the police institution to improve the quality of public services, particularly in receiving, processing, and following up on various reports and complaints from the public. However, in practice, the complaint process is still largely carried out manually, such as through face-to-face interactions, paper-based forms, or direct communication that is not well documented. This gives rise to problems such as delays in handling, difficulties in tracking the history and status of reports, low work efficiency of officers, and limited transparency of information for reporters. These problems ultimately affect the level of public trust in the police institution. This study aims to design a web-based public complaint information system that supports a more structured, measurable, and transparent complaint management process. This system is designed by implementing the Kanban method as a workflow management tool that allows for prioritization, monitoring the handling process, and tracking the status of complaints in real time. The research methods include a literature review related to information systems and the Kanban method, direct observation of the existing complaint process, interviews with stakeholders, system requirements analysis, modeling using UML (Use Case Diagram, Activity Diagram, Sequence Diagram), and database design using Entity Relationship Diagram (ERD). The results of this research resulted in the design of a web-based public complaints information system that can facilitate complaint data recording, report management and status updates, task assignments to officers, performance monitoring, and complaint status notification to the public.

The increasing use of electronic devices, specifically gadgets, among elementary school children has caused various problems, such as addiction, decreased social interaction, health problems, and decreased concentration in learning. These problems involve not only children but also objects and things around them, as well as parents in managing them. Gadget use is not just a matter of quantity that exacerbates this condition. As a solution, the community service team held a socialization focused on improving healthy digital literacy through counseling, interactive discussions, and practical simulations. Results achieved included increased knowledge about the risks of excessive gadget use, increased collective awareness in limiting usage time, and the formation of practical guidelines for healthy gadget use in school and family environments. The results of the activity were documented in the form of a scientific article published in the community service journal as an academic contribution. Overall, the activity has gone according to plan in terms of planning, implementation, and partner involvement. Active participation of participants was evident in the enthusiasm in the discussions and the school's commitment to continuously monitoring gadget use. Thus, this program is expected to have a long-term positive impact in shaping wise behavior in using technology among elementary school children.

This study aims to develop a student learning progress monitoring system designed to assist teachers and parents in tracking students’ academic performance at SMP Negeri 2 Loura. The main issue identified in the school is that academic information is still distributed using manual procedures, which results in delays and limited transparency regarding students’ learning progress. To address this problem, the system was developed using the Model View Controller (MVC) architecture and the Waterfall approach to system development, which consists of several sequential phases such as analyzing requirements, designing the system, implementing the solution, conducting tests, and performing ongoing maintenance. The findings of this research show that the system that has been created is capable of presenting academic information in a complete and structured manner, including assignment scores, daily tests, and semester examinations. The system provides faster and easier access for teachers to input grades and for parents to monitor their children’s academic development in real time. Functional testing shows that all features operate correctly according to user needs, with no errors found during system operation.

This study aims to develop a Student Progress Assessment Information System for PAUD Laskar Pelangi as a solution to various problems that arise in the academic data management process, which was previously carried out manually. The management of student grades, student data, class data, and subject data often encountered obstacles such as delays, recording errors, difficulty in searching for data, and inaccuracies in report preparation. To overcome these issues, a web-based information system was developed to manage all data in an integrated and more efficient manner. The system development method used the Website Development Life Cycle approach, and the application was implemented using the PHP programming language. The system is designed to enable the admin to manage student data, class data, subject data, grades, and automatically generate student grade reports. Database implementation was carried out to ensure that data is stored properly and can be accessed easily. Functional testing was conducted to ensure that all features operate according to user needs. The results of the study show that the developed information system is capable of improving administrative efficiency, accelerating the grade processing workflow, minimizing recording errors, and generating student grade reports quickly and accurately. Therefore, this system provides significant benefits for PAUD Laskar Pelangi in improving the effectiveness of academic administrative management and can be further developed according to the school's needs.