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The digital era has brought about a major transformation in the way organizations manage and utilize data. Data management is a key strategy in supporting decision-making based on accurate, fast, and relevant information. However, the rapid growth of data volume, diversity of sources, and complexity of data integration and security pose challenges in its management. These challenges include issues of data quality, inconsistency, duplication, and limitations in infrastructure and human resource capabilities. In addition, demands for compliance with regulations such as GDPR and the Personal Data Protection Act add to the complexity of ethical and responsible data management. On the other hand, technological developments such as big data analytics, artificial intelligence, the Internet of Things (IoT), and cloud computing present great opportunities to improve the efficiency and effectiveness of data management processes. Organizations that are able to adopt a data-driven approach and apply good data governance principles will gain competitive advantage, accelerate innovation, and improve customer satisfaction. This article comprehensively discusses the challenges and opportunities in data management from a data management perspective, and presents a framework for building an adaptive and sustainable data management strategy in the digital era. With a literature analysis and case study approach, this article aims to provide conceptual and practical contributions for organizations that want to optimize the potential of data as a strategic asset.

This research aims to design a web server-based heart rate monitoring system, which enables real-time heart rate monitoring from a remote location. The system utilizes a heart rate sensor connected to a microcontroller to measure and transmit heart rate data to a web server via an internet connection. The data obtained can be accessed directly by the user through a web-based interface, thus facilitating the monitoring of individual health conditions in an efficient manner. In addition, the system is equipped with a notification feature that alerts the user if any irregularities in the heart rate are detected, such as beating too fast or slow. As such, the system has the potential to increase alertness and speed up medical action if needed. The development of this system shows significant potential in supporting technology-based health applications, both for personal use and in a broader healthcare context. It is hoped that the system can contribute towards the development of more integrated and accessible health monitoring solutions.      

The rapid development of the Internet of Things (IoT) in Indonesia brings new challenges in personal data protection. This study aims to analyze the suitability of the legal framework in Indonesia in protecting personal data collected by IoT devices. This study identifies data collection and use practices, as well as uncovers existing legal loopholes. This study intends to conduct an in-depth analysis of the level of compliance with personal data protection laws in the context of IoT solutions. The normative legal research method with the library search technique is to search for journal or article materials related to the title and theme that the author is studying. Therefore, this study has a strategic goal to investigate the extent to which applicable regulations can be effectively implemented in the IoT ecosystem, with a special focus on security and privacy aspects that are the main pillars of personal data protection regulations. Based on these findings, this study provides policy recommendations to improve personal data protection in the IoT era, such as the need for a revision of the ITE Law, strengthening supervision by related institutions, or increasing public awareness.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

The rapid evolution of smart cities, driven by the integration of technologies such as the Internet of Things (IoT) and blockchain, has brought about significant advancements in urban infrastructure and services. However, these developments also introduce new cybersecurity challenges. Introduction: Smart cities are increasingly vulnerable to cyber threats due to the extensive use of interconnected devices and systems. A key security concern is the management of digital identities, which is essential for maintaining the integrity and reliability of city services. Literature Review: Traditional centralized identity management systems face significant security issues, including a single point of failure, data breaches, and limited user control over personal information. In contrast, decentralized solutions, particularly blockchain-based systems, offer enhanced security through their distributed nature, eliminating vulnerabilities associated with centralized models. Materials and Method: This research focuses on blockchain technology’s application in smart city identity management. A decentralized framework is proposed, leveraging cryptographic techniques, consensus mechanisms, and smart contracts to ensure data security, integrity, and privacy. Results and Discussion: The implementation of blockchain for identity management significantly improves attack tolerance, data integrity, and transparency. The decentralized approach mitigates the risks associated with central authorities, ensuring that user data remains secure and verifiable. However, scalability, interoperability, and regulatory compliance challenges remain. Blockchain solutions must be optimized for large-scale smart city applications and aligned with legal standards to achieve widespread adoption. Future research should focus on overcoming these challenges to create a more secure and resilient smart city infrastructure.

This study analyzes and optimizes production systems in the Industry 4.0 context, examining the fundamental shift from centralized, push-based production models to decentralized, adaptive, pull-based approaches. The research employs a mixed-method approach combining comprehensive literature review and multiple case studies across manufacturing sectors. Findings reveal that integration of Internet of Things (IoT), cyber-physical systems, artificial intelligence, and big data analytics enables real-time communication between production components, product personalization, and faster decision-making. Despite significant benefits in efficiency, flexibility, and competitiveness, implementation challenges persist, including high initial investment, employee resistance, technical expertise limitations, and integration complexity. Optimization approaches such as mixed-integer linear programming, digitally-integrated Lean Six Sigma, and digital twin simulations effectively enhance performance indicators including flexibility, reliability, and energy efficiency. The study concludes that successful production system transformation requires an integrated strategy encompassing process engineering, digital competency development, change management, and continuous evaluation to ensure sustainable optimization in the digital era