Data Management Research Articles

Analysis of the Role of Regional Government in Optimizing the School Operational Assistance Program (BOSP) Policy in Surabaya as a Strategy for Improving Education Quality

The development of the implementation of the School Operational Assistance Policy (BOSP) in Surabaya faces various challenges that affect policy optimization. This study aims to analyze the role of local government in optimizing the BOSP policy using Edward III's policy implementation theory, which includes communication, bureaucratic structure, resources, and the disposition or attitudes of implementers. This research employs a qualitative method by collecting data through in-depth interviews with informants from the education office and educational institutions in Surabaya, as well as documentation studies related to the BOSP policy. The study results show that communication contributes 68% to the success of the BOSP policy implementation, although there are still shortcomings in responsiveness and socialization. Bureaucratic structure contributes 72%, with major challenges in fragmented coordination between units and complex decision-making procedures. Resources contribute the highest at 74%, but limitations in staff competence and technological infrastructure remain significant obstacles. The disposition or attitudes of implementers contribute 73% to success, though deficiencies in discipline, integrity, and initiative are still evident. These four aspects indicate that while most elements have performed well, there is still room for improvement requiring serious attention to enhance policy effectiveness.The conclusion of this study is that optimizing the BOSP policy in Surabaya requires strengthening the four analyzed aspects. To address communication shortcomings, more structured and responsive socialization mechanisms are needed. Bureaucratic fragmentation can be resolved by simplifying procedures and integrating data management systems. Human resource and technological limitations must be addressed through intensive training and infrastructure investment. Finally, implementer disposition can be improved through development programs and supervision. These strategic recommendations can support the Surabaya local government in optimizing the implementation of the BOSP policy in a transparent, accountable, and effective manner to achieve better educational goals.

Data Governance for Emerging Technologies: A Conceptual Framework for Managing Blockchain, IoT, and AI

As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.

Implementation of a Laboratory Information Management System (LIMS) for microbiology in Timor-Leste: challenges, mitigation strategies, and end-user experiences

BackgroundEffective diagnostic capacity is crucial for clinical decision-making, with up to 70% of decisions in high-resource settings based on laboratory test results. However, in low- and middle-income countries (LMIC) access to diagnostic services is often limited due to the absence of Laboratory Information Management Systems (LIMS). LIMS streamline laboratory operations by automating sample handling, analysis, and reporting, leading to improved quality and faster results. Despite these benefits, sustainably implementing LIMS in LMIC is challenging due to high costs, inadequate infrastructure, and limited technical expertise.MethodsThis study evaluated the implementation of a customised microbiology LIMS at the National Health Laboratory (NHL) in Timor-Leste. The LIMS was deployed in November 2020, with an accompanying online results portal introduced in early 2021. The implementation was assessed via a checklist based on key challenges and requirements for LIMS in LMIC, alongside a post-implementation survey of scientists and clinicians.ResultsThe assessment revealed significant improvements in laboratory processes, including enhanced sample throughput, data management, and result reporting. The LIMS reduced transcription errors and standardised reporting of antimicrobial susceptibility testing (AST), improving data quality and accessibility. However, challenges such as unreliable internet connectivity and the need for ongoing funding and technical support persist. The user satisfaction survey, with responses from 19 laboratory scientists and 15 clinicians, revealed positive feedback on workflow improvements and result accessibility, although concerns about internet speed, sustainability, and the need for further training were noted.ConclusionThis study highlights the importance of careful planning, customisation, and stakeholder engagement in LIMS implementation in LMIC. The success in Timor-Leste demonstrates the potential for improved laboratory quality and patient outcomes, but also underscores the need for ongoing investment in infrastructure, technical expertise, and sustainability planning.

Case for Establishing a National Stroke Activation Fee in the United States: Learning From Trauma Centers.

There is a large burden of stroke in the United States, and extensive systems of care have been established to address it. The resources devoted to stroke centers are analogous to those of trauma centers, both sharing many strict requirements for certification, clinical preparedness, quality improvement, data management, and reporting. However, trauma programs partly defray these costs through a trauma activation billing code, a billable fee that is charged for activation of the trauma team under strict criteria. There are potential benefits to establishing an analogous national stroke code activation fee. Although a billable stroke code activation fee may increase financial burden on patients, this may be counterbalanced by the significant potential for individual and societal benefits. Providing additional financial support for stroke systems of care may improve acute stroke treatment, reduce stroke burden and poststroke disability, and reduce inequality by broadening the reach of stroke systems of care to disadvantaged communities. Further evaluation of the costs and benefits of implementing a stroke code activation fee based on that currently used by trauma centers is needed.

Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review.

Peripheral artery disease (PAD) is a major public health concern worldwide, associated with high risk of mortality and morbidity related to cardiovascular and adverse limb events. Despite significant advances in both medical and interventional therapies, PAD often remains under-diagnosed, and the prognosis of patients can be difficult to predict. Artificial intelligence (AI) has brought a wide range of opportunities to improve the management of cardiovascular diseases, from advanced imaging analysis to machine-learning (ML)-based predictive models, and medical data management using natural language processing (NLP). The aim of this review is to summarize and discuss current techniques based on AI that have been proposed for the diagnosis and the evaluation of the prognosis in patients with PAD. The review focused on clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients. Through evaluation of study design, we discuss model choices including variability in dataset inputs, model complexity, interpretability, and challenges linked to performance metrics used. In the light of the results, we discuss potential interest for clinical decision support and highlight future directions for research and clinical practice.

The factors influencing self-management in patients with depression: a qualitative research from China

BackgroundAs depressed patients often fail to seek help and obtain treatment on time for reasons such as stigma and lack of treatment resources, research on self-management for depressed patients is crucial. This study aimed to explore the factors influencing self-management in depression patients from the perspectives of both patients and psychiatric nurses.MethodsSix depression patients and twelve psychiatric nurses were chosen for semi-structured interviews through a purposive sampling method from a tertiary Grade A psychiatric hospital in Shandong Province, China. The group of depressed patients included in the study had a male to female sex ratio of 1:1 with a mean age of 46years. The psychiatric nurse group had a male to female ratio of 1:2 with a mean age of 42.42 years. We used descriptive qualitative research techniques. Nvivo 12.0 software was used for the data management and analysis using thematic analysis.ResultsThe factors influencing the self-management of depression patients were summarized into three themes and eight sub-themes: disease impact (symptoms, stigma), the degree of symptom control (medication adherence, depression’s cognitive status, its treatment and rehabilitation as well as additional factors), and support (self-efficacy, external support).ConclusionsThis study found that multiple factors influence the self-management of depression patients, including disease impact, the degree of symptom control, and support. In addition to helping patients integrate social resources and seek social support, medical staff should also pay attention to changes in the patient’s condition and disease control. Moreover, they should help patients increase their self-efficacy and enhance their self-management abilities through health education and other means.Clinical trial numberNot applicable.

HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization.

Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment. The use of deep learning algorithms for predicting WSIs has opened up new avenues for advanced medical diagnostics. Additionally, stain normalization can reduce the color and intensity variations present in WSI from different hospitals. As a result, deep learning classification accuracy improves. However, WSI reading and color normalization are still largely performed by using CPUs, leading to sub-optimal performance. We proposed a High-Performance Heterogeneous Computing Toolkit for Tissue Image (HCTTI) that integrates multiple computer system-level optimizations and encompasses WSI reading, tile normalization, and tile saving. We explored the potential advantages and limitations of different WSI readers and color normalization techniques in WSI analysis and the performance of different tile serialization formats for saving tiles. We found that HCTTI is 7 faster than OpenSlide for reading WSIs, GPU implementation of the Macenko normalization algorithm is 9 faster than TIAToolbox implementation, and HDF5 is faster than png and Zarr for storing normalized images in both writing (13 acceleration compared to png) and reading (2 acceleration compared to png), Specifically, HDF5 provides superior performance in handling large, complex datasets due to its efficient chunking and compression capabilities, as well as its broad support for hierarchical data management, making it very suitable for workloads like deep learning training I/O pattern that involves randomly reading large amount of small files in each training epoch. We also achieved linear acceleration in our multi-node distributed GPU implementation. To our knowledge, HCTTI is the first comprehensive toolkit that comprises distributed WSI reading, normalization, and serialization. It is 13 speedup compared to TIAToolbox implementation for normalizing a single WSI. Our findings could help pave the way for more effective and efficient deep learning-based approaches to WSI analysis, with the potential to transform medical diagnosis and treatment for a wide range of conditions. The source code of HCTTI is available at https://github.com/wangbo00129/HCTTI .

Building Public Health Quantitative Methods Capacity and Networks in sub-Saharan Africa: An Evaluation of a Faculty Training Program.

There is a shortage of individuals trained in using quantitative methods in biomedical research in sub-Saharan Africa (SSA). Improving public health in SSA requires new ways to promote quantitative knowledge and skills among faculty in biomedical research and better-integrated network systems of support. We describe the development, implementation, and evaluation of an innovative faculty training and support program in SSA from December 2017-June 2020, using courses in monitoring and evaluation, data management, and complex surveys as prototypical examples. Indicators were selected to follow the 4 levels outlined in the Kirkpatrick evaluation model: reaction, learning, behavior, and results. We used survey data from faculty fellows and students and reported median change and interquartile ranges (IQR). The training program created an international community of 26 faculty members working collaboratively to lead the training of 3 quantitative methods courses. The program increased faculty members' knowledge of the course content (median increase 17 percentage points [IQR: 0, 20]). Faculty members, in turn, trained 380 students at institutions of higher education in 8 SSA countries (Botswana, Ethiopia, Ghana, Nigeria, Rwanda, South Africa, Tanzania, and Uganda). The program relied on collaborative funding from participating institutions and focused on individual capacity-strengthening. In the future, the program will be scaled to include other emerging areas, such as data science, will integrate institutional support and feedback, and will move some of the training and mentoring activities to an online platform. Finally, to ensure that faculty have both improved confidence and improvement in competence, in future iterations, the program will include competency evaluation at the start and end and pair fellows who need additional training with those who excelled to co-teach.

Navigating ethics in HIV data and biomaterial management within Black, African, and Caribbean communities in Canada

BackgroundThis study explored the ethical issues associated with community-based HIV testing among African, Caribbean, and Black (ACB) populations in Canada, focusing on their perceptions of consent, privacy, and the management of HIV-related data and bio-samples.MethodsA qualitative community-based participatory research (CBPR) approach was employed to actively engage ACB community members in shaping the research process. The design included in-depth qualitative interviews with 33 ACB community members in Manitoba, Canada. The study was guided by a Community Guiding Circle, which contributed to study design, data analysis, and interpretation. A diverse sample was recruited through community agencies, social media, and flyers, with considerations for variations in age, gender, sexual orientation, and geographical location. The study employed iterative inductive thematic data analysis.FindingsParticipants expressed significant concerns about the collection, sharing, and use of HIV data from healthcare encounters, revealing mistrust towards institutions like police, child welfare, and immigration accessing their health information. Their worries centered on the handling of biological samples, data misuse, potential human rights violations, HIV criminalization, deportations, challenging consent, privacy, and bodily autonomy principles. While open to contributing to medical research, they unanimously demanded greater transparency, informed consent, and control over the secondary use of their health data.ConclusionsThe study underscores the need for culturally safe approaches in HIV testing and ethical governance in healthcare for ACB communities. It highlights the importance of prioritizing participant empowerment, ensuring transparency, practicing informed consent, and implementing robust data security measures to balance effective HIV information management with the protection of individual rights.

Toward Questionnaire Complexity Reduction by Decreasing the Questions

Data analysis can unearth important insights like patterns, trends, and deductions. In education, it can be utilized to tailor teaching methods to suit student traits or devise new activities to foster different skills or reinforce existing ones, for example. Understanding the particular context and past experiences can aid in this endeavor. Surveys and questionnaires yield a wealth of data. Yet, the sheer volume of questions can lead to challenges in data management for teachers and a decline in student interest due to the time-consuming nature of fulfilling these tasks. This work presents a methodology designed to decrease the number of questions in questionnaires. This method can be applied to general questionnaires that consist of closed-ended questions with a set number of response choices, where each question can have a varying number of options compared to the other questions in the form. This methodology has been adapted into a newly developed software tool for examining learning styles based on a specific learning styles questionnaire: the Honey–Alonso Learning Styles Questionnaire (CHAEA). This software is available to the public and integrated with Moodle, arguably the most extensively used learning management system globally. To evaluate the effectiveness of the proposed method, it has been used across various subjects in Computer Sciences Engineering degrees over different academic years. The outcomes from this case study validate the appropriateness of the technique. Consequently, these findings could establish patterns that could assist in devising more suitable learning methodologies for students.

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach to harnessing these technologies to enhance human and animal health by supporting innovative products and methods. Key areas of focus include regulatory adaptation, genomic research, and information technology modernization. The Animal and Veterinary Innovation Agenda outlines the Center's commitment to fostering innovation in veterinary medicine while addressing emerging challenges. This includes developing AI/ML-driven tools for antimicrobial resistance research, genome editing safety, and postmarketing safety surveillance. The paper discusses the CVM's participation in the FDA's role in shaping guidance documents for AI in regulatory decision making. In genomic research, the CVM is utilizing AI/ML to study antimicrobial resistance and improve genomic editing techniques. These technologies enhance the understanding of resistance mechanisms and facilitate the precise identification of genetic alterations. Artificial intelligence is also pivotal in information technology modernization efforts, aimed at streamlining data management and enhancing operational efficiency. The paper highlights the efforts to integrate AI/ML in safety surveillance, including signal detection and case processing. It emphasizes the importance of human-led governance, data quality, and model validation in ensuring the ethical deployment of AI technologies. The CVM's initiatives represent a transformative shift toward more efficient and innovative regulatory approaches. The paper concludes with a call for continued collaboration among researchers, industry, and regulatory bodies to advance AI integration and achieve mutual goals in animal health.

Determining Digital Maturity in Metropolitan Municipalities: The Example of Sakarya and Kocaeli Metropolitan Municipalities

Digital transformation in municipalities is vital for improving service delivery, increasing operational efficiency and encouraging citizen participation. This study aims to determine the factors that determine the digital maturity levels of institutions and the effects of these factors by focusing on the digital transformation process of Sakarya and Kocaeli Metropolitan Municipalities. In this research, case study design was used to investigate digital maturity in an in-depth and holistic way in the institutional context and to analyze the case of metropolitan municipalities in a real-world context. Data on municipalities' digital initiatives, policies and online platforms were obtained from semi-structured interviews, documents and observation notes. Data were analyzed and interpreted using single-case and cross-case analysis techniques using MAXQDA qualitative analysis software. While both municipalities have made significant progress in their digital transformation efforts, there are significant gaps and areas for improvement. Research findings point to vital factors influencing digital maturity, including leadership commitment, organizational culture, technical infrastructure, data management and citizen engagement. Holistic and comprehensive models that evaluate various dimensions such as technological infrastructure, digital services, data management, and cyber security provide a suitable framework for understanding digital maturity components in municipalities. Each element, from appropriate technological infrastructure and data governance practices to user-friendly e-government services and competent workforce, is critical to raising digital maturity. Municipalities can overcome the complexities of digital transformation through comprehensive digital strategies, user-centered design, and collaboration with stakeholders.

Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study.

Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU). AOU is an ongoing national, multiyear study aimed to build a research cohort of 1 million participants that reflects the diversity of the United States, including minority, health-disparate, and other populations underrepresented in biomedical research (UBR). We collaborated with community members, health care provider organizations (HPOs), and NIH leadership to design, build, and validate a secure, feature-rich digital platform to facilitate multisite, hybrid, and remote study participation and multimodal data collection in AOU. Participants were recruited by in-person, print, and online digital campaigns. Participants securely accessed the DHRP via web and mobile apps, either independently or with research staff support. The participant-facing tool facilitated electronic informed consent (eConsent), multisource data collection (eg, surveys, genomic results, wearables, and electronic health records [EHRs]), and ongoing participant engagement. We also built tools for research staff to conduct remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management. We built a secure, participant-centric DHRP with engaging functionality used to recruit, engage, and collect data from 705,719 diverse participants throughout the United States. As of April 2024, 87% (n=613,976) of the participants enrolled via the platform were from UBR groups, including racial and ethnic minorities (n=282,429, 46%), rural dwelling individuals (n=49,118, 8%), those over the age of 65 years (n=190,333, 31%), and individuals with low socioeconomic status (n=122,795, 20%). We built a participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, and socioeconomic backgrounds and other UBR groups. This DHRP demonstrated successful use among diverse participants. These findings could be used as best practices for the effective use of digital platforms to build and sustain cohorts of various study designs and increase engagement with diverse populations in health research.

Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR

Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management. Commercial software primarily relies on manual classification, which is time-intensive. This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. These equations serve as attribute functions for classifying datasets. Focusing on materials in traditional Minnan architecture on Kinmen Island, controlled indoor experiments and outdoor case studies validate the approach. The results show classification accuracies of 74% for wood, 98% for stone, and 93% for brick, demonstrating this method’s effectiveness in enhancing point cloud data applications and management.

KISAN BUDDY

The Kisan Buddy project is a mobile application designed to empower Indian farmers by addressing critical challenges such as limited market access, outdated market prices, and inefficient trade systems. With agriculture being the backbone of India’s economy, improving farmers’ accessibility to real-time data and optimized trade mechanisms is essential for sustainable growth. Despite existing applications, research highlights significant gaps, including a lack of real-time market updates, user-friendly interfaces, and robust decision-making support. Kisan Buddy bridges these gaps by leveraging Firebase Authentication for secure login and registration, Firebase Realtime Database for seamless data management, and Firestore for structured, scalable data storage. The app offers features like real-time mandi price updates, personalized cost estimation, and intuitive navigation to nearby mandis through Google Maps integration. Methodologically, the application integrates advanced mobile technologies, ensuring offline functionality to cater to the diverse needs of Indian farmers. Early testing has demonstrated improved decision-making, market transparency, and accessibility, fostering better economic outcomes. By addressing issues such as market inefficiencies and digital literacy barriers, the app aligns with the United Nations' Sustainable Development Goals, particularly those related to economic growth and reduced inequalities. Kisan Buddy represents a transformative solution, combining innovative technology with user-centric design to enhance agricultural productivity and profitability. This research underscores the potential of digital tools in revolutionizing rural livelihoods, offering a scalable and adaptable framework for improving the socio-economic conditions of farmers in India. The findings highlight its ability to bridge existing gaps and its role in advancing sustainable agricultural practices. Keywords— Android Application Development, Firebase Authentication, Firebase Realtime Database, Firestore Integration, Mandi Recommendations, Google Maps Navigation

Deep learning reveals diverging effects of altitude on aging.

Aging is influenced by a complex interplay of multifarious factors, including an individual's genetics, environment, and lifestyle. Notably, high altitude may impact aging and age-related diseases through exposures such as hypoxia and ultraviolet(UV) radiation. To investigate this, we mined risk exposure data (summary exposure value), disease burden data (disability-adjusted life years (DALYs)), and death rates and life expectancy from the Global Health Data Exchange (GHDx) andNational Data Management Center for Health of Ethiopiafor each subnational region of Ethiopia, a country with considerable differences in the living altitude. We conducted a cross-sectional clinical trial involving 227 highland and 202 lowland dwellers from the Tigray region in Northern Ethiopia to gain a general insight into the biological aging at high altitudes. Notably, we observed significantly lower risk exposure rates and a reduced disease burden as well as increased life expectancy by lower mortality rates in higher-altitude regions of Ethiopia. When assessing biological aging using facial photographs, we found a faster rate of aging with increasing elevation, likely due to greater UV exposure. Conversely, analysis of nuclear morphologies of peripheral blood mononuclear cells(PBMCs) in blood smears with five different senescence predictors revealed a significant decrease in DNA damage-induced senescence in both monocytes and lymphocytes with increasing elevation. Overall, our findings suggest that disease and DNA damage-induced senescence decreases with altitude in agreement with the idea that oxidative stress may drive aging.

Public–private partnership in pipelining science of acute care ecosystem: Insights from Taiwan's Presidential Hackathon

AbstractIntroductionThe acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency.MethodsTaiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City.ResultsThe hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre‐hospital emergency care system across multiple administrative regions.ConclusionOur experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross‐sector collaboration.

Intellisearch - Intelligent Employee Data Access System

- The IntelliSearch Intelligent Employee Data Access System leverages advanced machine learning (ML) and natural language processing (NLP) techniques to modernize employee information retrieval. Designed to address inefficiencies in traditional manual methods, IntelliSearch provides secure, real-time responses to queries about roles, attendance, salaries, and performance metrics. Its modular architecture integrates with existing databases, offering robust data security through encryption and role-based access control (RBAC). By streamlining repetitive tasks and enhancing query accuracy, the system fosters organizational productivity and employee satisfaction. Future enhancements include predictive analytics, advanced performance tools, and industry-specific adaptations, paving the way for scalable, intelligent solutions in employee management Key Words: IntelliSearch, Machine Learning, Natural Language Processing, Employee Data Management, Role-based Access Control, Data Security, Organizational Efficiency, Real-time Queries, Scalable Architecture.

ANALISIS STRATEGI CUSTOMER RELATIONSHIP MANAGEMENT DALAM MEMBANGUN LOYALITAS PELANGGAN PADA BISNIS DEARLY CAKE

This study aims to analyze the implementation of Customer Relationship Management (CRM) strategies in building customer loyalty at Dearly Cake. CRM is an essential approach to enhancing customer relationships, maintaining satisfaction, and creating sustainable loyalty. This study uses a qualitative method with a purposive sampling technique to dig deeper into the CRM strategy implemented by Dearly Cake, the factors that influence its effectiveness, and the challenges faced in the implementation process. The results show that Dearly Cake successfully builds customer loyalty through effective communication, personalized service, and consistent product quality. However, challenges such as limited human resources and suboptimal customer data management hinder the maximum implementation of CRM. The study provides recommendations to enhance technology usage in customer data management and ongoing staff training to support more effective CRM implementation. It is expected that this study will provide insights for other businesses aiming to improve customer loyalty through effective CRM strategies.

ChatGPT, Python, and Microsoft Excel

This article examines how the integration of ChatGPT, Python, and Microsoft Excel can significantly enhance the productivity of medical librarians. These tools provide a powerful framework for automating tasks, processing large datasets, and generating actionable insights. ChatGPT, with its advanced natural language processing, helps automate Python script generation and improve data processing in Excel, streamlining workflows and enabling more precise service delivery. Practical applications demonstrate improvements in information retrieval, data management, and research support. The results show that, despite some integration challenges, these technologies have the potential to greatly elevate the efficiency and effectiveness of librarians in managing healthcare data.