This project aims to analyze the alignment of legislation within the scope of investment in Indonesia using the TF-IDF (Term Frequency-Inverse Document Frequency) method. It addresses the challenge of numerous and often complex regulations affecting the investment landscape in Indonesia.

The project originated from the need to assess the harmony between various legislations, an important aspect given the complexity and frequent revisions in regulations.

The methodology involves several steps:

• Text extraction from legal documents.
• Text preprocessing, including text conversion, line break replacement, and removal of non-essential elements.
• Application of the TF-IDF algorithm for text analysis.
• Visualization using heatmaps and graphs.

The project successfully implements the TF-IDF algorithm, providing an effective and scalable approach to analyzing legal documents. It also introduces a user-friendly interface that doesn't require coding knowledge, accessible anytime and anywhere.

Project Demo

Future enhancements could include integrating POS-Tagging, Named Entity Recognition (NER), and Dependency Parsing. Additionally, exploring other similarity algorithms like Jaccard, Euclidean, or Manhattan distance is suggested for further evaluation steps.

TF-IDF is a method that can be said to be strong for analyzing the harmonization of regulations in laws and regulations related to investment in Indonesia. This project provides useful insights and visualizations, making it a valuable tool for legal analysis.