Data-Driven Barangay Services Recommendation System using a Recurrent Neural Network (RNN) Algorithm

Abstract

Purpose - This research focuses on the development of a Data-Driven Barangay Services Recommendation System using Recurrent Neural Networks (RNNs) to enhance the efficiency and responsiveness of Barangay Local Government Units (BLGUs).

Method – The research employs the Recurrent Neural Networks (RNNs) Algorithm in a data-driven recommendation system. Research and Development (R&D) and Descriptive Research methods will both be used in the proposed study.  Survey data will be gathered using the descriptive research method, which attempts to give a thorough and accurate picture of the topic being studied.

Conclusion - The data-driven barangay services recommendation system has a function to create a reliable platform that meets the specific needs of Barangay Local Government Units, providing them with enhanced data security, accuracy, and efficiency in managing documents and records. BLGU can streamline its administrative processes, reducing paperwork, minimizing errors, and optimizing resource utilization.

Recommendation – The study recommends the implementation of the Data-Driven Barangay Services Recommendation System to enhance document management, streamline administrative processes, and ensure data security and authenticity, ultimately creating a more efficient and technologically advanced local government unit.

Practical Implication – The implementation of the RNN algorithm Data-Driven Barangay Services Recommendation System may serve as advancement and innovation in terms of improving the process.