Decision Support System for Potential Stock Selection Recommendations Using AHP and Profile Matching Methods

This study presents the design and implementation of a Decision Support System (DSS) aimed at facilitating the selection of potential banking stocks by novice investors. The system integrates two well-established decision-making methodologies: the Analytical Hierarchy Process (AHP) and Profile Matching. The objective is to provide a structured, data-driven approach that assists users in making informed and objective investment decisions based on critical financial performance indicators. These indicators include Price to Earnings Ratio (PER), Price to Book Value (PBV), Return on Assets (ROA), Return on Equity (ROE), Earnings Per Share (EPS), Book Value Per Share (BVPS), Debt Ratio (DR), and Dividend Yield (DY). In this system, AHP is employed to calculate the relative weight or importance of each financial criterion through pairwise comparisons, incorporating users judgment in the weighting process. Once the weights are determined, the Profile Matching method is used to assess and rank the alternative banking stocks based on how closely they align with the ideal profile defined by the criteria. The results of the analysis identified Bank Mandiri (BMRI) as the top-ranked stock, followed by Bank Rakyat Indonesia (BBRI) and Bank Central Asia (BBCA), indicating their strong fundamental performance according to the selected indicators. To validate the system's functionality, black-box testing was conducted on 21 different modules, all of which yielded valid outcomes. This confirms that the application operates correctly and reliably. Overall, the study concludes that the DSS is effective, user-friendly, and valuable as a decision support tool, especially for beginner investors targeting the banking sub-sector.