Continuing study to a college is a hope for prospective students. However, that is an easy problem, because that needs to be considered in choosing such as, the cost during the lecture took place, facilities, facilities and infrastructure, cooperation between universities and others. By using The Decision Support System, the calculation of all criteria can be able to determine the best college to assist prospective students in selecting and choosding the college. The Decision Support System uses the Simple Additive Weighting (SAW) method. The Decision Support Issues are basically for the selection of alternative actions enabling prospective students in choosing the college which they want, and will produce a best decision for prospective students for continuing in the best collage

Andewi, Widi

Anggraeni, Elisabet Yunaeti

Oktafianto, Oktafianto

English

Elisabet Yunaeti Anggraeni

Widi Andewi

Oktafianto Oktafianto

Directory of Open Access Journals

SIMPLE ADDITIVE WEIGHTING FOR MODELING DSS TO DETERMINE THE BEST COLLEGE IN PRINGSEWU

STMIK Pringsewu Lampung (Sekolah Tinggi Manajemen Informatika dan Komputer): Portal OJS

IJISCS | 8       SIMPLE ADDITIVE WEIGHTING FOR MODELING DSS TO DETERMINE THE BEST COLLEGE IN PRINGSEWU  Elisabet Yunaeti Anggraeni*1, Widi Andewi2, Oktafianto3 1,2Jl. Wisma Rini 9 Pringsewu Lampung.  1,2Program Studi Manajemen Informatika,  3Program Studi Sistem Informasi      E-mail : e-mail: *1elisabet.sugianto@yahoo.co.id, 2widiandewi.91@gmail.com, 3oktafianto@stmikpringsewu.ac.id   Abstract Continuing study to a college is a hope for prospective students. However, that is an easy problem, because that needs to be considered in choosing such as, the cost during the lecture took place, facilities, facilities and infrastructure, cooperation between universities and others. By using The Decision Support System, the calculation of all criteria can be able to determine the best college to assist prospective students in selecting and choosding the college. The Decision Support System uses the Simple Additive Weighting (SAW) method. The Decision Support Issues are basically for the selection of alternative actions enabling prospective students in choosing the college which they want, and will produce a best decision for prospective students for continuing in the best collage.  Keywords: DSS, College, Simple Additive Weighting (SAW)   1. INTRODUCTION 1.1. Background of the Problem At this time, The Information of Technology (IT)  in the world is very increase and favored by the graduate students from senior high school, to be able to continue their study in the best college. One of the efforts of educational institutions to ensure the quality of graduation and teaching and learning process is to improve the quality of lecturer's performance in teaching and learning process. The quality of educational institutions is determined by three factors, namely students, lecturers and teaching learning facilities. These three factors are interrelated and mutually supportive of one another in creating a good learning process [1]. According to RI Law NO. In 2005, lecturers are professional educators and scientists with the primary task of transforming, developing and disseminating science, technology, and the arts through education, research, and community service [2]. In addition to the elements that have been mentioned above there are other elements that are very important in the process of supporting learning in universities namely sara and infrastructure. With good facilities and infrastructure and supported with qualified lecturers qualified then the college will be qualified. To find out how the quality of universities will be in the rankings of colleges using SAW method. Therefore, many best colleges are running and prospective students are difficult to determine or choose which one the college that will be a means of seeking their knowledge.  Based on the problems, the researchers used Simple Additive Weighting method, for helping prospective students in determining the best college.  Full Paper pISSN : 2598-0793 eISSN : 2598-246x  International Journal Information System and Computer Science (IJISCS) IJISCS | 9   1.2. Formulation of the Problem  How to determine and select a collage in Pringsewu that fit the criteria of prospective students by using Simple Additive Weighting (SAW) method?  1.3. Scope of the problem  Based on the formulation of the problem, this research would be conducted to capture prospective students in determining the appropriate college in Pringsewu. By processing the data from the desired criteria using Simple Additive Weighting (SAW), it can be an accurate decision support.  1.4. Research Purpose The purpose of this research is to make the decision support application to determine the best college in Pringsewu by using Simple Additive Weighting (SAW) method.  1.5. Objective of the Research The objective of this research is to make prospective students for determining the best college easily. 2. FRAME OF THEORY 2.1. The definition of Decision Support System   Decision Support System (DSS) is a system capable of providing problem solving and communications capabilities for problems with semi-structured and unstructured conditions. This system is used to assist decision making in semi-structured situations and unstructured situations, where nobody knows exactly how decisions should be made (Turban, 2005).   DSS aims to provide information, guide, provide predictions and lead to users of information in order to make better decisions. DSS is an implementation of decision-making theories that have been introduced by sciences such as operation research and science management, the difference is we would look for problem solving that we have to manually calculate iteration (usually to find the minimum, maximum, or optimum) first, now PC has offered its ability to solve the same problem in a relatively short time.   Sprague and Watson define Decision Support System (SPK) as a system that has five main characteristics (Sprague et.al, 1993): 1. Computer-based systems. 2. Used to assist decision makers. 3. To solve complicated problems that is impossible to do with manual calculations. 4. Through interactive simulation. 5. Where data and model analysis as the main component.  2.2. Fuzzy Multiple Attribute Decision Making (FMADM)  Fuzzy Multiple Attribute Decision Making (FMADM) is a method used to find the optimal alternative of a number of alternatives with certain criteria. The core of FMADM is to determine the weight value for each attribute, then proceed with the ranking process which will select the alternatives already given. Basically, there are 3 approaches to finding attribute weight value, that is subjective approach, objective approach and approach of integration between subjective & objective. Each approach has its advantages and disadvantages. In a subjective approach, the weighted value is determined by the subjectivity of the decision-makers, so that several factors in the alternative ranking process can be determined freely. Whereas in the objective approach, the weight value is calculated mathematically so that it ignores the subjectivity of the decision maker. (Kusumadewi, 2013). There are several methods that can be used to solve FMADM problems. among others : a. Simple Additive Weighting Method (SAW); b. Weighted Product (WP); c. Elimination Et Choix Traduisant la Realite (ELECTRE); d. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS); e. Analytic Hierarchy Process (AHP) IJISCS | 10   2.3. Simple Additive Weighting  Method Simple Additive Weighting method is often also known as weighted summing method. The basic concept of the SAW method is to find the weighted sum of performance ratings on each alternative on all attributes. SAW method requires the process of normalizing the decision matrix (X) to a scale comparable to all existing alternative ratings.    (1) Which: rij = normalized performance rating value xij = attribute value of each criterion Max xij = the largest value of each criterion i Min xij = the smallest value of each criterion i Benefit = if the greatest value is best Cost = if the smallest value is best  where rij is the normalized performance rating of the alternative Ai Ai on the attribute Cj; i=1,2,...,m and j=1,2,...,n. The preference value for each alternative (V i) is given as:    (2) Which: Vi = rank for each alternative wj = weighted value of each criteria rij = normalized performance rank values Greater Vi values indicate that Ai alternatives are more elected.    3. RESULT AND DISCUSSION The respondents of this research is the colleges in Pringsewu. These are the criteria needed for decision making, the criteria on the questionnaire given to the respondents, namely: C1 = Building / study space C2 = Re-listing fee C3 = Development cost C4 = Tuition fee per semester C5 = Library C6 = Laboratory availability C7 = College accreditation C8 = Linearity of educational scholarship C9 = Percentage of alumni  After the criteria selection process through questionnaires from SMK (The Vocational Senior High School), so based on the answers questionnaire after calculating the percentage of respondents' answers were: IJISCS | 11   C1 = Building / study space C2 = Library C3 = Laboratory availability C4 = College accreditation C5 = Cost of education C6 = Percentage of alumni  From the existing keriteria, then it made a level of importance criteria based on the value of weight that had been determined into the fuzzy ratio, the rating matches each alternative on each criteria as follows: 1. Very low (SR) = 0; 2. Low (R) = 0.2; 3. Medium (S) = 0.4; 4. Middle (T1) = 0.6; 5. Height (T2) = 0.8; 6. Very high (ST) = 1;  The value of the weight is made in a graph to be clearer, as shown in the picture below: SR R S T1 T2ST0 0,2 0,4 0,6 0,8 1µ(W)10W Figure 1. Graph of weight  After the process of selecting criteria through questionnaires from the respondents, the criteria were chosen based on the results of the questionnaire answers after calculating the percentage of respondents' answers were :  C1 = Building / study space C2 = Library C3 = Laboratory availability C4 = College accreditation C5 = Cost of education C6 = Percentage of alumni  Based on the criteria and sub-criteria of each alternative rating on the loyalty of predetermined criteria, then the weighting of each criterion has been converted with fuzzy numbers. 1. Building / study space The value interval of the building / study space that has been converted with fuzzy numbers below can be seen in the table below.  Table 3.1 Index Values Building / study space Building / Study Space Values X = 1:40 0.25 X = 1:35 0.50 IJISCS | 12   X = 1:30 0.75 X = 1:25 1.00  2. Library The library value interval that has been converted with fuzzy numbers can be seen in the table below. Assessment of the library is related to the number of titles of books available in the library.  Table 3.2 Index Value of Library Library Values X < 200 0.25 X >= 200 0.50 X >= 250 0.75 X >= 300 1.00  3. Laboratory availability The interval of labor availability that has been converted with fuzzy numbers can be seen in the table below. Assessment of laboratory availability is related to the availability of laboratories in accordance with the existing study program.  Table 3.3 Laboratory Availability Index Rate Availability of Value Laboratory Values X = 0 0 X = 1 0.50 X > 1 1.00  4. Accreditation of colleges The interval of accredited school values that have been converted with fuzzy numbers can be seen in table below.  Table 3.4 The value of accreditation index of colleges Accreditation of colleges Values Not accredited 0.25 Accreditation C 0.50 Accreditation B 0.75 Accreditation A 1.00  5. Cost of Education in each semester The interval of the value of the educational linearity of the educator that has been converted to the fuzzy number can be seen in table below.  Table 3.5 Cost of Education in each semester Cost of Education in each semester Values >= very expensive 0.25 >= expensive 0.50 IJISCS | 13   >= affordable 0.75 100% 1.00  6. Percentage of alumni The percentage interval of the percentage of students passing a national exam that has been converted with fuzzy numbers is seen in table below. Table 3.6 The Value of Percentage alumni index Percentage of alumni Values >= 70% 0.25 >= 80% 0.50 >= 90% 0.75 100% 1.00   Manual Calculation based on the Example of Case (Simulation) The three colleges that would be assessed, they have the following data: Table 3.7 Simulation of colleges’ calculations Criteria PRIVATE COLLEGES PTS 1 (A1) PTS 2 (A2) PTS 3 (A3) Building / study space  (C1) 1:35 1:30 1:40 Library (C2) >=400 >=250 >=300 Laboratory availability  (C3) 2 1 1 Accreditation of college (C4) B C B Education cost (C5) 100% >=90% >=90% Percentage of alumni  (C6) 100% 100% 100%  Based on the table above, data can be formed matrix decision X that has been converted with fuzzy numbers, as follows:  Alternative Criteria C1 C2 C3 C4 C5 C6 A1 0.50 0.75 0.50 0.75 1.00 1.00 A2 0.75 0.75 0.50 0.50 0.75 1.00 A3 0.25 1.00 0.50 0.75 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:     0.50  0.75  0.50  075  1.00  1.00 X =    0.75  0.75  0.50  0.50  0.75  1.00    0.25  1.00  0.50  0.75  0.75  1.00  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: IJISCS | 14    A1). r11 =          r12 =                    r13 =          r14 =           r15 =          r16 =   A2). r21 =          r22 =                    r23 =          r24 =           r25 =           r26 =   A3). r31 =          r32 =                    r33 =          r34 =           r35 =           r36 =  Second, making normalization of matrix R obtained from result of normalization of matrix X as follows:    0.66  0.75  1.00  1.00  1.00  1.00 X= 1.00  0.75  1.00  0.66  0.75  1.00  0.33  1.00  1.00  1.00  0.75  1.00  IJISCS | 15   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*0.66)+(0.10*0.75)+ (0.15*1) + (0.20*1) + (0.20*1) + (0.20*1)       = (0.099 + 0.075 + 0.15 + 0.20 + 0.20 + 0.20)       = 0.924 V2 = (0.15*1)+(0.10*0.75)+(0.15*1)+(0.20*0.66)+ (0.20*0.75) + (0.20*1)      = (0.15 + 0.075 + 0.15 + 0.132 + 0.15 + 0.20)      = 0.857  V3 =(0.15*0.33)+ (0.10*1) + (0.15*1) + (0.20*1) + (0.20*0.75) + (0.20*1)      = (0.0495 + 0.10 + 0.15 + 0.20 + 0.15 + 0.20)      = 0.8495  From multiplication of matrix W * R then it got the result as follows: V1 = 0.924, V2 = 0.857, V3 = 0.8495, largest value of the summation of the above matrix is V1 so that alternative A1 (PTS A) is a PTS deserving the best PTS. The criteria of colleges above, they are based on the following intervals: 0.00 – 0.33 = Worse 0.34 – 0.66 = Good 0.67 – 1.00 = Best  4. CONCLUSIONS From the calculation of the case examples above, it can be concluded that the colleges are eligible to be selected based on the SAW method with an affordable cost assessment, adequate facilities, and good accreditation. The SAW application for the best DSS selection modeling can make a result in a rational and optimal decision making   5. SUGGESTIONS This research is still very far from perfect value, the researchers suggest for further research to: 1. The application can be added another criteria. 2. The application can be developed again for an example by adding fuzzy logic so that the inappropriate data can be tolerated and the results given more accurate again.   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. [2] Muslihudin, Muhamad., et. al. 2017. Pembuatan Model Penilaian Indeks Kinerja Dosen Menggunakan Metode Fuzzy Simple Additive Weighting. SEMNASTEKNOMEDIA. Vol.5 No.1. Universitas AMIKOM Yogyakarta. Yogyakarta. [3] Kusumadewi, Sri., Hartati, S., Harjoko, A., dan Wardoyo, R. 2013. Fuzzy Multi-Attribute Decision Making (FUZZY MADM). Yogyakarta: Penerbit Graha Ilmu. [4] Kusumadewi, Sri. 2004. Fuzzy Quantification    Theori untuk Analisis Hubungan antara Penilaian Kinerja Dosen oleh Mahasiswa, Kehadiran Dosen, dan Nilai  Kelulusan. Media Informatika. Jurusan Teknik Informatika, Fakultas Teknologi Industri Universitas Islam Indonesia. [5] Nandang Hermanto. 2012. Sistem Pendukung Keputusan Menggunakan Metode  Simple Additive Waighting (SAW) untuk menentukan Jurusan Pada SMK Bakti Purwokerto. [6] Sri Eniyati. 2011. Perancangan Sistem Pendukung Pengambilan Keputusan untuk Penerimaan Beasiswa dengan Metode SAW (Simple Additive Weighting). IJISCS | 16   [7] Uswatun Chasanah . Sistem Pendukung Keputusan Pemilihan Kualitas Padi Sawah Di Dinas Pertanian Demak  Menggunakan Metode Simple Additive Weighting (SAW). [8] Faisal., Silvester Dina Handy Permana., 2015. Sistem Pendukung Keputusan Pemilihan Sekolah Menengah Kejuruan Teknik Komputer dan Jaringan yang Terfavorit dengan Menggunakan Multi-Criteria Decision Making. JTIIK, Fakultas Telematika, Universitas Trilogi. [9] Fatih,  D.R.,  et, al. (2011).  DSS  untuk rekomendasi  pemilihan  jurusan  pada  perguruan  tinggi  bagi siswa SMU. Jurnal Politeknik Elektronika Negeri Surabaya. [10] Pare, Selfina; Kati, Oktavianus, (2012). Sistem pendukung keputusan pemilihan program studi pada perguruan tinggi. USAMUS Merauke. [11] Turban. 2005. Decision  Support  Systems  and Intelligent Jilid 1. Andi Offset. Yogyakarta.   

SIMPLE ADDITIVE WEIGHTING FOR MODELING DSS TO DETERMINE THE BEST COLLEGE IN PRINGSEWU

Abstract

Similar works

Full text

Available Versions

Directory of Open Access Journals

STMIK Pringsewu Lampung (Sekolah Tinggi Manajemen Informatika dan Komputer): Portal OJS