Implementation of Fuzzy Multiple Attribute Decision Making (FMADM) and Simple Additive Weighting (SAW) for Selecting the Best Stocks - Nasution, Saddam Ali Habibie; Sriani, Sriani (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2025)
Stock investment is an attractive option for modern society to enhance long-term asset value, despite significant risks, especially for novice investors who often face limited understanding of fundamental stock analysis. Such analysis involves various complex financial indicators. This study combines the Fuzzy Multiple Attribute Decision Making (FMADM) method and the Simple Additive Weighting (SAW) method to assist investors in selecting the best stocks in the banking sector. FMADM is applied to address data uncertainty using a fuzzy approach, while SAW calculates the final score based on criteria weights and performance. The research data were obtained from company financial reports for the 2019–2023 period, focusing on seven key criteria: Return on Assets (ROA), Return on Equity (ROE), Earnings Per Share (EPS), Net Profit Margin (NPM), Price-to-Book Value (PBV), Debt-to-Equity Ratio (DER), and Dividend Yield (DY). The study aims to develop a decision support system to simplify the investment analysis process while reducing the risk of decision-making errors for investors. The findings indicate that alternative A35/Bank BTPN Syariah (BTPS) ranked first with a final score of 0.7120, followed by A42/Bank Mega (MEGA) in second place with a score of 0.7074, and A08/Bank Central Asia (BBCA) in third place with a score of 0.6988. This system provides a practical solution for more structured and efficient investment decisions.