<![CDATA[The selection of the best practicum assistants is traditionally done through a conventional method, which involves voting by active students attending lab classes. However, upon evaluation, it was found that the results were not accurate. Some cases revealed that assistants were chosen based solely on popularity or recognition among the students, possibly influenced by physical appearance or public speaking skills in front of the class, while other important aspects were not considered. This situation could lead to social jealousy. The problem lies in the difficulty of combining evaluation criteria and determining the relative weights for each criterion in the process of selecting the best practicum assistants at the college. Additionally, there is a lack of objectivity in decision-making during the selection process, resulting in an unstructured and immature decision-making process. Therefore, this research aims to enhance the process of selecting the best practicum assistants at the college through optimizing the profile matching algorithm using the Analytic Hierarchy Process (AHP) method. AHP's role involves checking the weights and making paired comparisons to evaluate each criterion and determine the criterion weights. AHP is also utilized to ensure consistency in determining the weights. On the other hand, the role of profile matching is to provide accurate rankings or comparisons based on the suitability scores between the profiles of potential assistants and the reference profile. The combination of these two algorithms is expected to result in a more accurate selection of practicum assistants by effectively measuring the decision criteria weights. Therefore, the difficulty of combining evaluation criteria and determining the relative weights for each criterion can be minimized. Furthermore, optimizing the profile matching algorithm will enable a more objective decision-making process for selecting the best practicum assistants through more accurate rankings or comparisons based on the suitability scores with the reference profile. Based on this optimization, the collaboration of the two algorithms can achieve comparison results with an accuracy rate of 90%.]]>
