Application of Python-Based Topsis Method for Financial Performance Assessment of Idx Companies

Abstract

This study applies the Python-based TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to assess the financial performance of companies listed on the Indonesia Stock Exchange (IDX) during the period. In the context of an ever-growing and digitalized economy, objective assessment of financial performance is very important for strategic decision making. This study uses financial ratio data such as Return on Assets (ROA), Return on Equity (ROE), Current Ratio, Debt to Equity Ratio (DER), and Net Profit Margin (NPM) which are grouped into three main categories: liquidity, solvency, and profitability. Each ratio is given a criterion direction (benefit/cost) and weight based on its level of importance. The analysis process includes data pre-processing, normalization, weighting, determining positive and negative ideal solutions, calculating distances, and calculating preference values or Closeness Coefficients (CC). The results of the study show that LPPS companies have the best financial performance (CC: 0.699214), while TIRT is ranked lowest (CC: 0.476411). This study proves that the Python-based TOPSIS method is able to provide systematic, efficient, and reliable financial performance evaluations, and can be a valuable tool for investors, analysts, and management in decision making.