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Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

Information technology is a technology used to manage data, including processing, acquiring, organizing, storing, and manipulating data in various ways to produce high-quality information—namely, information that is relevant, accurate, and timely. This information is used for personal, business, and governmental purposes, serving as strategic information in decision-making. To anticipate changes in weather conditions, particularly rainfall, a valid and accurate report is needed that can be useful for the public. So far, the correlation or relationship between the factors influencing weather conditions—especially rainfall—has not been precisely determined, making it mathematically difficult to create a model that can describe the correlation among all these factors. This is where Artificial Neural Networks (ANN) come into play: to create such models and map out the existing problems purely based on the input data provided. One of the capabilities of neural networks is to make predictions based on previously learned data using the backpropagation method.

This research aims to design and simulate a DC motor speed control system using integral state feedback (KISF) control. This control is designed to maintain DC motor speed stability with high accuracy despite disturbances or load changes. The research stages include mathematical modeling of the DC motor, PID control design, integral state feedback control design, simulation implementation and simulation results analysis. The show that althought integral state feedback control has a rise time 273.501 ms, slightly slower than PID 197.604 ms, integral state feedback compensates with higher slew rate of change 6.540 V/ms compared to PID 4.344 V/ms. Integral state feedback offers greater flexibility through pole placement values with Ackerman’s formula J3 = [-12 -100 -600], indicating that this system has good capability in responding to input changes, thus maintaining motor speed stably and efficiently. This indicates that integral state feedback control is superior in system adaptation and handling complex dynamics.