Vol. 2 No. 4 (2023): IJRVOCAS – Special Issues – INCOSTIG – PP. 168~172 Print ISSN 2777-0168| Online ISSN 2777-0141| DOI prefix: 10.53893 https://journal.gpp.or.id/index.php/ijrvocas/index
168
Implementation of Simple Additive Weighting (SAW) Method in Decision Support System to Determine
the Best University in Medan
Erwinsyah Simanungkalit, Jenny Sari Tarigan, Dewi Comala Sari& Annalisa Sonaria Hasibuan Department of Commercial Administration, Politeknik Negeri Medan, Indonesia
ABSTRACT
Continuing studies to university are a hope for prospective students. However, this is an easy problem, because what needs to be considered in choosing such as, accreditation, lecturers, buildings, laboratory, cost of education, and percentage of alumn and others. By using the Decision Support System, the calculation of all the criteria can determine the best university to assist prospective students in choosing and choosing university. The Decision Support System uses the Simple Additive Weighting (SAW) method. Decision Support Problems are basically for the selection of alternative actions that allow prospective students to choose the university they want and will produce the best decision for prospective students to continue at the best university.
Keywords:
DSS University
Simple Additive Weighting
Corresponding Author:
Erwinsyah Simanungkalit,
Department of Commercial Administration, Politeknik Negeri Medan,
Almamater Road No 1, Padang Bulan, Medan, North Sumatera, Indonesia.
Email: [email protected]
1. INTRODUCTION
The development of Information Technology (IT) in the world today is increasing and is in demand by high school graduates, to be able to continue their studies at the best universities. One of the efforts of educational institutions to ensure the quality of graduation and the teaching and learning process is to improve the quality of lecturers' performance in the teaching and learning process. The quality of educational institutions is determined by three factors, namely students, lecturers and teaching and learning facilities. These three factors are interrelated and mutually support each other in creating a good learning process [1]. According to RI Law NO. In 2005, lecturers were professional educators and scientists with the main task of transforming, developing, and disseminating science, technology, and art through education, research, and community service. In addition to the elements mentioned above, there are other elements that are very important in the process of supporting learning in higher education, namely infrastructure and facilities. With good facilities and infrastructure and supported by qualified lecturers, the higher education will be qualified. To find out how the quality of higher education will be in the ranking of university using the SAW method. Therefore, many of the best university are running and prospective students find it difficult to determine or choose which university will be a means of seeking knowledge. The problem of decision making is a form of selection from various alternative actions that can be selected through certain mechanisms in the hope of producing the best decision.
By determining the best decision, several methods can be used to build a decision support system, one of which is Simple Additive Weighting (SAW)[2]. The SAW method is a method used in dealing with Fuzzy Multiple
Attribute Decision Making (FMADM) situations or decision making by finding the optimal alternative from a number of alternatives with certain criteria [3].
2. RESEARCH METHOD
In carrying out this research, clear and structured stages are needed, in order to facilitate the process, it is necessary to make a diagram design such as the diagram below:
Figure 1. Diagram of Methods and Research Stages 2.1. Simple Additive Weighting
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. The SAW method requires a decision matrix normalization process (X) to a scale that can be compared with all existing alternative ratings [4]. 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 attribute. The rating of each attribute must be dimension-free in the sense that it has passed the previous matrix normalization process [5]. 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.
3. RESULTS AND ANALYSIS
These are the criteria needed for decision making, the criteria namely:
C1 = College accreditation C2 = Lecturer
C3 = Building / study space C4 = Laboratory availability C5 = Cost of education C6 = Percentage of alumn
From the existing criteria, then the importance level criteria are made based on the weight value that has been determined into the fuzzy ratio, the rating is in accordance with each alternative on each criterion as follows:
a. Very low (SR) = 0;
b. Low (R) = 0.2;
Method
Discussion Problem Identification
Problem Analysis
Results, and Conclusions
c. Medium (S) = 0.4;
d. Middle (T1) = 0.6;
e. Height (T2) = 0.8;
f. Very high (ST) = 1;
Manual Calculation based on the Example of Case (Simulation) The three colleges that would be assessed, they have the following data:
Alternative Creteria
C1 C2 C3 C4 C5 C6
A1(PTS1) 0.75 0.75 0.50 0.50 1.00 1.00 A2(PTS2) 0.50 0.75 0.50 0.75 0.75 1.00 A3(PTS3) 0.75 1.00 0.50 0.25 0.75 1.00
Decision making gives weight, based on the level of importance of each of the required criteria as follows:
Weight. Vector: W = [0.15, 0.10, 0.15, 0.20, 0.20, 0.20].
Creating the X decision matrix, it created from the match table as follows:
𝑥 = {
0,75 0,75 0,50 0,50 0,75 0,50
0,75 1 0,50
0,50 1 1 0,75 0,75 1 0,25 0,75 1 }
The first normalization of X matrix to calculate the value of each criterion based on the criteria is assumed as the criterion of profit or cost as follows:
𝑟11 = 0.75
𝑀𝑎𝑥 (0.75 0.50 0.75)
𝑟12 = 0,75
𝑀𝑎𝑥 (0.75 0.75 1)
𝑟13 = 0,50
𝑀𝑎𝑥 (0.50 0.50 0.50)
𝑟14 = 0,50
𝑀𝑎𝑥 (0.50 0.75 0.25)
𝑟15 = 1
𝑀𝑎𝑥 (1 0.75 0.75)
𝑟16 = 1
𝑀𝑎𝑥 (1 1 1)
𝑟21 = 0,50
𝑀𝑎𝑥 (0.75 0,50 0,75)
𝑟22 = 0,75
𝑀𝑎𝑥 (0.75 0.75 1)
𝑟23 = 0.50
𝑀𝑎𝑥 (0.50 0.50 0.50)
𝑟24 = 0,75
𝑀𝑎𝑥 (0.50 0.75 0.25)
𝑟25 = 0.75
𝑀𝑎𝑥 (1 0.75 0.75)
𝑟26 = 1
𝑀𝑎𝑥 (1 1 1)
𝑟31 = 0,75
𝑀𝑎𝑥 (0.75 0.50 0.75)
𝑟32 = 1.00
𝑀𝑎𝑥 (0.75 0.75 1)
𝑟33 = 0.50
𝑀𝑎𝑥 (0.50 0.50 0.50)
𝑟34 = 0.25
𝑀𝑎𝑥 (0.50 0.75 0.25)
𝑟35 = 0.75
𝑀𝑎𝑥 (1 0,75 0,75)
𝑟36 = 1
𝑀𝑎𝑥 (1 1 1)
Second, making normalization of matrix R obtained from result of normalization of matrix X as follows:
𝑥 = {
1,00 1,00 1,00 0,66 1,00 1,00 1,00 0,75 1,00
1,00 1,00 1,00 1.50 0,75 1,00 0,25 0,75 1,00 }
Next, it will be made multiplication matrix W * R and calculate the results multiplication to obtain the best alternative by doing ranking the largest value as follows:
V1 = (0.15*1.00)+(0.10*1.00)+ (0.15*1.00) + (0.20*1.00) + (0.20*1.00) + (0.20*1.00) = (0.15 + 0.10 + 0.15 + 0.15+ 0.20 + 0.20) = 1,85
V2 = (0.15*0,66)+(0.10*1.00)+(0.15*1.00)+(0.20*1,50)+ (0.20*0.75) + (0.20*1) = (0.099 + 0.10+ 0.15 + 0.3 + 0.15 + 0.20) = 0.999
V3 =(0.15*1,00)+ (0.10*0,75) + (0.15*1) + (0.20*0,25) + (0.20*0.75) + (0.20*1) = (0.15 + 0.075 + 0.15 + 0.05 + 0.15 + 0.20) = 0.775
From multiplication of matrix W * R then it got the result as follows: V1 = 1.85, V2 = 0.999, V3 = 0.775 largest value of the summation of the above matrix is V1 so that alternative A1 (PTS A) is a PTS deserving the best PTS
4. CONCLUSION
From the calculation of the case examples above, it can be concluded that universities are eligible to be selected based on the SAW method with an assessment of accreditation, lecturers, buildings, laboratory, cost of education, and percentage of alumn. The SAW for modeling the best DSS selection can produce rational and optimal decision making.
REFERENCES
[1] Muslihudin, Muhamad and A, Wulan, Arumita. (2016). Pembuatan Model Penilaian Proses Belajar Mengajar Perguruan Tinggi Menggunakan Fuzzy Simple Additive Weighting (Saw)(Sudi: Stmik Pringsewu).
SEMNASTEKNOMEDIA. Vol.4 No.1 Hal 31-36. AMIKOM Yogyakarta
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[3] F. Frieyadie, “PENERAPAN METODE SIMPLE ADDITIVE WEIGHT (SAW) DALAM SISTEM PENDUKUNG KEPUTUSAN PROMOSI KENAIKAN JABATAN,” J. Pilar Nusa Mandiri, 2016, doi: 10.33480/pilar.v12i1.257 [4] M. A. Mude, “Perbandingan Metode SAW dan TOPSIS pada kasus UMKM,” Ilk. J. Ilm., 2016, doi:
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How to Cite
Simanungkalit, E., Tarigan, J. S., Sari, D. C. ., & Hasibuan, A. S. (2023). Implementation of Simple Additive Weighting (SAW) Method in Decision Support System to Determine the Best University in Medan. International Journal of Research in Vocational Studies (IJRVOCAS), 2(4), 168–172. https://doi.org/10.53893/ijrvocas.v2i4.190
