Employee rankings can be done for various needs, one of which relates to year-end bonuses. So far, the awarding of year-end bonuses to research objects has only relied on one criterion, namely targets and achievements. In fact, there are many other indicators that can be assessed, such as attendance, discipline, communication style, cooperation, and initiative. This study aims to provide an alternative computation-based employee ranking method with multi-attribute decision making (MADM). The Elimination and Choice Translation of Reality (ELECTRE) technique is used in research to rank employee data based on their attribute values. The results of the study show that, of the ten employees assessed, four alternatives (employees) are recommended to be selected based on the results of a comparison of the dominant aggregate values. In this study, it can also be seen that alternative 6 (Alt-6) is the strongest alternative to be recommended for selection. Because alternative 6 (Alt-6) is not only better than alternatives 1, 4, 8, 9, and 10, but also better than alternative 3 (Alt-3) and alternative 4 (Alt-4). The order of the second, third, and fourth alternatives, respectively, are alternatives 5, 7, and 8. The recommendations of these four employees can be used as decision-making material for policymakers, given the need to award year-end bonuses.