Artificial Intelligence for Unstructured Data Processing

Abstract

In the digital era, the volume of unstructured data such as text, images, audio, and video continues to increase exponentially. Processing unstructured data is a major challenge for various industries due to its high complexity and the difficulty of extracting relevant information. Artificial Intelligence (AI) has become an innovative solution in addressing this challenge through techniques such as Natural Language Processing (NLP), Computer Vision, and Machine Learning. This study aims to explore various AI methods used in processing unstructured data and examine their effectiveness in improving the efficiency and accuracy of data analysis. adopts a multidisciplinary approach that combines natural language processing (NLP), machine learning, and data analytics techniques to extract information from unstructured data, especially in the context of electronic medical records (EMR). This study will be conducted in several stages including data collection, data processing, model development, and evaluation of results. The results show that AI is not only able to automate the information extraction process but also improve the accuracy and speed of data analysis, which is very important in the context of decision making in the fields of healthcare, finance, and business. By using deep learning models and advanced algorithms, AI can identify patterns and relationships in complex data, thereby providing deeper insights for better decision making. The results of this study are expected to provide insight for developers and practitioners in optimizing the use of AI to manage unstructured data more effectively and efficiently.