HYBRID METHOD USING ITARA AND MACONT FOR SELECTING THE BEST CUSTOMERS IN A DECISION SUPPORT SYSTEM - Junhai Wang; Riska Aryanti; Setiawansyah Setiawansyah (UNIB Press, 2026)
This research is motivated by the company's challenges in objectively identifying high-value customers due to the numerous assessment criteria, heterogeneous data, and the use of conventional methods that are prone to subjective bias and ranking instability. To address these challenges, this study develops a decision support system based on the hybrid ITARA–MACONT method, where ITARA is used to determine criteria weights rationally based on indifference threshold deviation, while MACONT is applied to perform compromise aggregation in the alternative ranking process. The results show that the system can produce clear and consistent customer rankings, with Customer TY achieving a score of 0.7141 and ranking first, followed by Customer RD with a score of 0.6561 in second place, and Customer AH with a score of 0.5859 in third place. These findings indicate that the integration of ITARA–MACONT is effective in enhancing the objectivity, transparency, and stability of top customer selection results, thereby supporting strategic decision-making aimed at improving customer loyalty and business profitability.