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The use of blockchain has received great attention in its adoption as a financial instrument in cryptocurrencies. This phenomenon needs to be considered in the sense not only as a form of financial transactions but also in other fields such as health, which is also a challenge for modern society. In addition, several government policies have also supported the provision of health services as a form of improving people’s living standards in the form of insurance. In this study, we try to design the system by using UML diagram and simulate the use of DApps offered by the Vexanium Ecosystem. For example, three basic activities between patients, doctors, and insurance will be simulated in the form of the transaction ledger. This method allows us to speed up the authentication process that previously needed to be performed for a long time with bureaucracy becoming the rule in smart contracts in a matter of minutes. The evaluation of this method will then be compared with eight existing blockchain projects. The result in healthcare processes is cost savings through increased automation, speed, standardization, and efficiency. All of this can be a preliminary analysis of its application in Indonesia, particularly related to the authentication and recording of medical records.

The battery monitoring system (BMoS) is crucial to monitor the condition of the battery in supplying and absorbing the energy when operating and simultaneously determine the optimal limits for achieving long battery life. All of this can be done by measuring the battery parameters and increasing the state of charge (SoC) and the state of health (SoH) of the battery. The battery dataset from NASA is used for evaluation. In this work, the gradient vector is employed to obtain the trend of the energy supply pattern from the battery. In addition, a support vector machine (SVM) is adopted for an accurate battery accuracy index. This is in line with the use of polynomial regression; hence, points V1 and V2 are obtained as the boundaries of the normal-usage phase. Furthermore, testing of the time length distribution is also carried out on the length of time the battery was successfully extracted from the classification. All these stages can be used to calculate the rate of battery degradation during use so that this strategy can be applied in real situations by continuously comparing values. In this case, using the voltage gradient, SVM method, and the suggested polynomial regression, MAPE (%), MAE, and RMSE can be obtained against the battery value graph with values of 0.3%, 0.0106, and 0.0136, respectively. With this error value, the dynamics of the SoC value of the battery can be obtained, and the SoH problem can be resolved with a shorter usage time by avoiding the voltage-drop phase.