scholarly journals Penentuan Wali Kelas Yang Ideal Menggunakan Metode TOPSIS

2019 ◽  
Vol 5 (2) ◽  
pp. 85
Author(s):  
Nafis Sururi ◽  
Kusrini Kusrini ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Decision Making ◽  
School Environment ◽  
Formal Education ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Ideal Solution ◽  
Topsis Method ◽  
Order Preference ◽  
The Ideal

Pendidikan merupakan salah satu hal yang penting dalam suatu negara. Pendidikan bisa didapatkan pendidikan formal, informal maupun nonformal. Contoh pendidikan formal adalah sekolah dan pergururan tinggi, di dalam sekolahan terdapat wali kelas yang bertanggung jawab terhadap peserta didik di satu kelas atau ruang belajar di lingkungan sekolah. Dalam menentukan wali kelas yang ideal kepala sekolah dapat melihat karakteristik dan kemampuan yang dimiliki guru secara objektif. Multiple Criteria Decision Making (MCDM) merupakan salah satu metode yang paling banyak digunakan dalam pengambilan keputusan. Salah satu metode MCDM adalah Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Penelitian ini menggunakan metode TOPSIS yang dapat menganalisis keputusan multi-kriteria dimana metode tersebut dapat memilih alternatif terbaik dengan jarak terdekat dari alternatif ideal. Hasil yang diperoleh dari penelitian ini adalah Penentuan wali kelas dilakukan dengan cara mencari nilai preferensi setiap guru yang paling besar dari 3 kelas yang digunakan. Perhitungan yang dilakukan menggunakan bobot berbeda pada setiap kelas agar mendapatkan nilai prefensi sebagai acuan penentuan wali kelas yang ideal.Kata Kunci—Wali kelas, Ideal, TOPSISEducation is one of the important things in a country. Education can be obtained from formal, informal or non-formal education. Examples of formal education are schools and high schools, in schools there is a homeroom teacher who is responsible for students in one class or study room in a school environment. In determining the ideal homeroom, the principal can see the characteristics and abilities of the teacher objectively. Multiple Criteria Decision Making (MCDM) is one of the most widely used methods in decision making. One of the MCDM methods is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This study uses the TOPSIS method which can analyze multi-criteria decisions where the method can choose the best alternative with the closest distance from the ideal alternative. The results obtained from this study are Determination of the homeroom teacher is done by finding the preference value of each teacher, the largest of the 3 classes used. Calculations performed using different weights for each class in order to get the value of the prefix as a reference for determining the ideal homeroom teacher.Keywords—Homeroom teacher, Ideal, TOPSIS

scholarly journals A Multiple Criteria Decision Making Method Based on Relative Value Distances

2015 ◽  
Vol 40 (4) ◽  
pp. 299-315 ◽  
Author(s):  
Huan-jyh Shyur ◽  
Liang Yin ◽  
Hsu-shih Shih ◽  
Chi-bin Cheng
Keyword(s):  
Decision Making ◽  
Solution Method ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Relative Value ◽  
Seeking Behavior ◽  
Order Preference ◽  
Topsis Technique ◽  
The Ideal

Abstract This paper proposes a new multiple criteria decision-making method called ERVD (election based on relative value distances). The s-shape value function is adopted to replace the expected utility function to describe the risk-averse and risk-seeking behavior of decision makers. Comparisons and experiments contrasting with the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method are carried out to verify the feasibility of using the proposed method to represent the decision makers’ preference in the decision making process. Our experimental results show that the proposed approach is an appropriate and effective MCDM method.

A New Index for TOPSIS based on Relative Distance to Best and Worst Points

2020 ◽  
Vol 19 (03) ◽  
pp. 695-719
Author(s):  
S. A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Decision Making ◽  
Similarity Index ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Relative Distance ◽  
Ideal Solution ◽  
Topsis Method ◽  
Numerical Examples ◽  
Shortest Distance ◽  
Negative Ideal Solution

The technique for order performance by similarity to ideal solution (TOPSIS) is one of the most well-known methods in multiple criteria decision making (MCDM) problems. The classical TOPSIS method employs a similarity index to rank alternatives. However, the chosen alternative sometimes does not have the shortest distance to the positive ideal solution (PIS) and remotest distance from the negative ideal solution (NIS), simultaneously. Besides, in some cases, TOPSIS cannot assign a unique rank to alternatives. The purpose of this paper is to propose a new similarity TOPSIS index based on the relative distance to the best and worst points. In the proposed method, by treating the separations of an alternative from the PIS and the NIS as negative criterion and positive criterion, respectively, we reduce the original MCDM problem to a new one with two criteria. The proposed index, based on different weights, in optimistic, pessimistic, and apathetic cases, easily determines the score of each alternative. Finally, we illustrate the proposed index using four numerical examples. The results are compared with those published in the literature.

Developing MCDM Approach Based on GRA and TOPSIS

2010 ◽  
Vol 34-35 ◽  
pp. 1931-1935 ◽  
Author(s):  
Qi Bing Wang ◽  
An Hua Peng
Keyword(s):  
Decision Making ◽  
Comprehensive Evaluation ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Comprehensive Evaluation ◽  
Multiple Criteria ◽  
Shape Similarity ◽  
Ideal Solution ◽  
Relational Analysis ◽  
Order Preference ◽  
Grey Relational

Multiple criteria decision making(MCDM) is widely used in selection from a set of available alternatives with multiple criteria, approaches to which includes fuzzy comprehensive evaluation(FCE), grey relational analysis(GRA), and technique for order preference by similarity to ideal solution(TOPSIS), and so on. First analyzes the limitations of various methods: only considering the overall effect of attribute indicators in the method of FCE, only considering the shape similarity of data curve between comparative scheme and ideal solution in GRA and only considering position approximation in TOPSIS. Second proposes a new method of comprehensive evaluation which takes into account both shape similarity and position approximation. The validity of this method has been further proved by an example of suppliers selection.

scholarly journals Sistem Pendukung Keputusan Untuk Seleksi Penerimaan Dan Penentuan Posisi Karyawan

2016 ◽  
Vol 10 (1) ◽  
pp. 62
Author(s):  
Devi Martha Ariyanti ◽  
Fahrul Agus ◽  
Dyna Marisa Khairina
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Ideal Solution ◽  
Order Preference

Sumber Daya Manusia (SDM) merupakan sumber daya yang paling penting bagi organisasi. Salah satu proses yang paling penting bagi perusahaan adalah proses rekrutmen dan seleksi sumber daya manusia. Pada kenyataannya pengambilan keputusan secara efisien dan efektif pada saat melakukan seleksi terhadap sumber daya manusia bukanlah hal yang mudah, maka diperlukan suatu Sistem Pendukung Keputusan (SPK) untuk membantu memecahkan masalah tersebut. Dalam hal ini Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) dan Fuzzy Multiple Criteria Decision Making (FMCDM) digunakan sebagai metode untuk memberikan penilaian calon karyawan yang akan diseleksi. Dari beberapa data yang diujikan terhadap aplikasi ini menunjukkan bahwa calon karyawan ideal terhadap suatu posisi bukan hanya memiliki nilai kedekatan pada kriteria ideal yang diinginkan oleh perusahaan, tapi juga memiliki nilai dengan rentang menjauhi kriteria ideal yang tidak diinginkan oleh perusahaan.

MULTIPLE CRITERIA CONSTRUCTION MANAGEMENT DECISIONS CONSIDERING RELATIONS BETWEEN CRITERIA / DAUGIATIKSLIAI STATYBOS VALDYMO SPRENDIMAI ATSIŽVELGIANT Į RODIKLIŲ TARPUSAVIO PRIKLAUSOMYBĘ

2010 ◽  
Vol 16 (1) ◽  
pp. 109-125 ◽  
Author(s):  
Jurgita Antuchevičienė ◽  
Edmundas Kazimieras Zavadskas ◽  
Algimantas Zakarevičius
Keyword(s):  
Decision Making ◽  
Mahalanobis Distance ◽  
Construction Management ◽  
Distance Measure ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique

Decision making in construction management has been always complicated especially if there were more than one criterion under consideration. Multiple criteria decision making (MCDM) has been often applied for complex decisions in construction when a lot of criteria were involved. Traditional MCDM methods, however, operate with independent and conflicting criteria. While in every day problems a decision maker often faces interactive and interrelated criteria. Accordingly, the need of improving and supplementing the methodology of compromise decisions arose. It was proposed to supplement TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) method and integrate the Mahalanobis distance in the usual algorythm of TOPSIS. Mahalanobis distance measure offered an option to take the correlations between the criteria into considerations while making the decision. A case study of building redevelopment in Lithuanian rural areas was presented that demonstrated the application of the proposed methodology. The case study proved that the proposed TOPSIS‐M (TOPSIS applying Mahalanobis distance measure) method could have substantial influence in carrying the proper decision. Santrauka Statybos valdymo spendimų priėmimas visuomet yra komplikuotas, ypač jei turime atsižvelgti į daugelį rodiklių. Kompleksiniams statybos sprendimams, kurie apibūdinami daugeliu rodiklių, taikomi daugiatiksliai sprendimų priėmimo metodai (MCDM ‐ Multiple Criteria Decision Making). Šie metodai skirti sprendimams priimti tuomet, kai vertinami konfliktuojantys bei nepriklausomi rodikliai. Tačiau realiose situacijose, priešingai, nuolat susiduriame su saveikaujančiais ir tarpusavio priklausomybę turinčiais rodikliais. Dėl šios priežasties kyla poreikis patobulinti sprendimų metodologiją. Straipsnyje siūloma papildyti variantų racionalumo nustatymo metoda TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), taikant Mahalanobio metoda atstumams nustatyti. Mahalanobio atstumų nustatymo metodas suteikia galimybę įvertinti koreliacinės rodiklių priklausomybės priimant daugiatikslį sprendimą. Siūlomos metodologijos taikymas įliustruojamas sprendžiant apleistų pastatų Lietuvos kaimo vietovėse racionalaus sutvarkymo uždavinį. Pateiktas pavyzdys patvirtina, kad TOPSIS‐M metodo (t. y. TOPSIS naudojant Mahalanobio atstuma) taikymas gali turėti esminę įtaka priimant sprendimą.

scholarly journals A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1554
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Predrag S. Stanimirović ◽  
Muzafer Saračević ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Multiple Criteria Decision Making ◽  
Grey System Theory ◽  
Multiple Criteria ◽  
Decision Making Process ◽  
Grey System ◽  
Criteria Weights

The environment in which the decision-making process takes place is often characterized by uncertainty and vagueness and, because of that, sometimes it is very hard to express the criteria weights with crisp numbers. Therefore, the application of the Grey System Theory, i.e., grey numbers, in this case, is very convenient when it comes to determination of the criteria weights with partially known information. Besides, the criteria weights have a significant role in the multiple criteria decision-making process. Many ordinary multiple criteria decision-making methods are adapted for using grey numbers, and this is the case in this article as well. A new grey extension of the certain multiple criteria decision-making methods for the determination of the criteria weights is proposed. Therefore, the article aims to propose a new extension of the Step-wise Weight Assessment Ratio Analysis (SWARA) and PIvot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods adapted for group decision-making. In the proposed approach, attitudes of decision-makers are transformed into grey group attitudes, which allows taking advantage of the benefit that grey numbers provide over crisp numbers. The main advantage of the proposed approach in relation to the use of crisp numbers is the ability to conduct different analyses, i.e., considering different scenarios, such as pessimistic, optimistic, and so on. By varying the value of the whitening coefficient, different weights of the criteria can be obtained, and it should be emphasized that this approach gives the same weights as in the case of crisp numbers when the whitening coefficient has a value of 0.5. In addition, in this approach, the grey number was formed based on the median value of collected responses because it better maintains the deviation from the normal distribution of the collected responses. The application of the proposed approach was considered through two numerical illustrations, based on which appropriate conclusions were drawn.

scholarly journals A Comparative Study on Decision Making Methods with Interval Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Aditya Chauhan ◽  
Rahul Vaish
Keyword(s):  
Decision Making ◽  
Input Data ◽  
Multiple Criteria Decision Making ◽  
Interval Data ◽  
Cross Entropy ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique ◽  
Selection Of ◽  
Novel Algorithm

Multiple Criteria Decision Making (MCDM) models are used to solve a number of decision making problems universally. Most of these methods require the use of integers as input data. However, there are problems which have indeterminate values or data intervals which need to be analysed. In order to solve problems with interval data, many methods have been reported. Through this study an attempt has been made to compare and analyse the popular decision making tools for interval data problems. Namely, I-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), DI-TOPSIS, cross entropy, and interval VIKOR (VlseKriterijumska Optimiza-cija I Kompromisno Resenje) have been compared and a novel algorithm has been proposed. The new algorithm makes use of basic TOPSIS technique to overcome the limitations of known methods. To compare the effectiveness of the various methods, an example problem has been used where selection of best material family for the capacitor application has to be made. It was observed that the proposed algorithm is able to overcome the known limitations of the previous techniques. Thus, it can be easily and efficiently applied to various decision making problems with interval data.

scholarly journals Comparison of the Economic Performance of Turkish Republics in Central Asia with TOPSIS Method

2015 ◽  
Author(s):  
Ziya Gökalp Göktolga ◽  
Engin Karakış ◽  
Hakan Türkay
Keyword(s):  
Decision Making ◽  
Central Asia ◽  
Economic Performance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Economic Management ◽  
Macroeconomic Indicators ◽  
Order Preference ◽  
Negative Ideal Solution

The aim of this study is to compare the economic performance of Turkish Republics in Central Asia with Multi Criteria Decision Making (MCDM) methods. Turkish Republics have been experiencing a transition from a centrally planned economy towards a market economy since their independence. In this study important macroeconomic indicators are used to determine economic performance. Economic performance evaluation of the country is an important issue for economic management, investors, creditors and stock investors. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method outranks the countries according to the proximity of the positive ideal solution and distance of the negative ideal solution. Economic Performance of Turkish Republics in Central Asia (Azerbaijan, Turkmenistan, Kazakhstan, Kyrgyzstan, and Uzbekistan) are compared with TOPSIS method. İnvestigated with TOPSIS method countries best and worst economic performance years are detected during mentioned period and results are analyzed.

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