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This study aims to develop a predictive model using linear regression to identify potential arrhythmias in the elderly based on electrocardiogram (ECG) data. Data were collected through observations at healthcare facilities from elderly patients with indications of arrhythmia, then preprocessed such as cleaning, normalization, feature selection, and outlier checking were carried out. The features used include PR interval, QRS duration, QT interval, and heart rate. The dataset was divided into training data (80%) and test data (20%) to build and evaluate the model. The training results showed that the model was able to predict the risk of arrhythmia with a Mean Squared Error (MSE) value of 0.15 and a coefficient of determination (R²) close to 1. Evaluation using a confusion matrix showed an accuracy of 76.19%, precision of 82.80%, recall of 76.19%, and F1 score of 72.70%. These results prove that linear regression can be used as an initial approach in the early detection of arrhythmias non-invasively in the elderly. This study provides a foundation for the development of ECG data-based clinical decision support systems and suggests future exploration of more complex models and integration with real-time monitoring technologies.

This research examines the application of the Elliptic Curve Digital Signature Algorithm (ECDSA) in a blockchain system as a security solution for digital payment systems in Indonesia. Using a descriptive-qualitative approach based on literature review and conceptual simulations using Python, this study discusses the working principles of ECDSA, its advantages over other digital signature algorithms, and the challenges of its adoption in Indonesia. The results show that ECDSA provides high cryptographic efficiency, maintains transaction authenticity and integrity, and supports a transparent decentralized system. The academic simulations include not only KYC processes, top-ups, transactions, validation by validators, and block recording, but also demonstrates the formation of an interconnected multi-level blockchain and tests scenarios for rejecting manipulated or invalid transactions. The contribution of this research lies not only in the theoretical review but also in the implementation illustrations that can be used as a basis for education and the initial development of blockchain-based digital payment systems. The research results show that ECDSA is capable of providing a high level of efficiency in the encryption and transaction verification process, maintaining data integrity and authenticity, and supporting a decentralized and transparent system. The academic simulations included the KYC process, wallet creation using ECDSA keys, balance top-ups through bank integration, transaction creation and validation, and block recording in the blockchain. Specifically, the simulations successfully demonstrated how new blocks are added to the chain by referencing the previous block's hash, as well as how transactions with corrupted signatures, manipulated amounts, or insufficient balances are automatically rejected by the validator consensus mechanism, maintaining system integrity. This research contributes not only theoretically, but also through conceptual representations that can be used as an educational foundation and for the initial development of blockchain-based digital payment systems in Indonesia.

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.

Decision Support System couple the intellectual resource of individuals with the capabilities of the computer to improve the quality of decision. It's a computer based support system for management decision makers who deal with semi-structured problem. An integrated decision support system for use in an machine mollen product has been developed. It incorporates a linear Programming model that represents the contribution optimal and optimizes the production water pump and mollen machine. An optimization model is performed using a management scient model called linear programming approach in older to determine media selection. To use this model, the DSS needs ti interface with another software. Mathematical Programming is a technique used in mathematical models, particularly optimization models, to assist in decision making. The Simplex Method is "a systematic procedure for generating and testing candidate vertex solutions to a linear program." (Gill, Murray, and Wright, p. -137) It begins at an arbitrary corner of the solution set. At each iteration, the Simplex Method selects the variable that will produce the largest change towards the minimum (or maximum) solution. The  development of computer programs to be used as Decision Support Systems involves several tasks such as mathematical modeling, technical and data collection and development of a user friendly interface.