The Simple Additive Weighting (SAW) method is often known as the weighted addition method. The basic concept of the SAW method is to find a weighted sum of the performance ratings for each alternative on all attributes (Fishburn, 1967) (MacCrimmon, 1968). The SAW method requires a decision matrix normalization process (X) to a scale that can be compared with all existing alternative ratings[8]. This SAW method requires the decision-maker to determine the weight for each attribute. The total score for the alternatives is obtained by adding up all the multiplication results between the rating (which can be compared across attributes) and the weight of each Problem Identification Problem Analysis Method Discussion Results, and Conclusions Simple Additive Weighting (SAW) method in determining beneficiaries...(Muhammad Iqbal Panjaitan) 21 attribute. The rating of each attribute must be dimension-free in the sense that it has passed the previous matrix normalization process[9]. The steps for completing the SAW are as follows: a. Determine the criteria that will be used as a reference in making decisions, namely Ci. b. Determine the suitability rating of each alternative for each alternative. c. Making a decision matrix based on the criteria (Ci), then normalizing the matrix based on the equation adjusted for the type of attribute (profit attribute or cost attribute) in order to obtain a normalized matrix R. d. The final result is obtained from the ranking process, namely the addition and multiplication of the normalized matrix R with the weight vector so that the largest value is chosen as the best alternative (Ai) as a solution. The formula for carrying out the normalization is as follows[7]: Rij= { 𝑋𝑖𝑗 max 𝑋𝑖𝑗 min 𝑋𝑖𝑗 𝑋𝑖𝑗 Where Rij is a normalized performance rating; Xij is the attribute value of each criterion; Max Xij is the greatest value of each criterion; Min Xij is the smallest value of each criterion; Benefit is the greatest value is the best; Cost is the smallest value is the best. Rij is the normalized performance rating of the alternatives Ai on attribute Cj; i = 1,2,..., m and j = 1,2,..., n. The preference value for each alternative (Vi) is given as: 𝑉𝑖 = ∑ 𝑤𝑗 𝑅𝑖𝑗 𝑛 𝑗=1 Where Vi is the ranking for each alternative, Wj is the weighted value of each criterion; Rij is the normalized performance rating value. A larger Vi value indicates that the alternative Ai is preferred.