IMPLEMENTASI METODE MULTI ATTRIBBUTE DECISION MAKING (MADM) DAN SIMPLE ADDITIVE WEIGHTING (SAW) DALAM PENDUKUNG KEPUTUSAN SELEKSI BEASISWA BANTUAN BIAYA PENDIDIKAN PPA ( BBP - PPA)

Beasiswa merupakan dukungan biaya yang diberikan kepada mahasiswa untuk mengikuti dan/atau menyelesaikan pendidikan tinggi berdasarkan pertimbangan utama prestasi atau keterbatasan ekononomi. Proses seleksi penentuan penerima beasiswa masih mengunakan skala prioritas sesuai buku pedoman umum beasiswa dan bantuan biaya pendidikan peningkatan prestasi akademik agar mendapatkan calon penerima beasiswa. Metode Multiple Attribute Decision Making (MADM) dan metode Simple Additive Weighting (SAW) dapat digunakan untuk pendukung keputusan. Implementasi Multiple Attribute Decision Making (MADM) digunakan untuk mencari alternatif dari sejumlah alternatif dengan kriteria-kriteria tertentu. selanjutnya dilakukan dengan mencari bobot untuk setiap attribute kemudian menghasilkan perangkingan untuk menentukan nilai alternatif terbaik dengan menggunakan metode Simple Additive Weighting (SAW). Dengan menggunakan sistem pendukung penerima beasiswa dapat membantu proses seleksi beasiswa dengan kriteria yang disyaratkan antara lain IPK, Prestasi mahasiswa, Jumlah anggota keluarga, Pendapatan Orang Tua, Status Pengajuan Beasiswa, dan Semester. Berdasarkan ujicoba sistem pendukung keputusan seleksi Beasiswa Bantuan Biaya Pendidikan PPA (BBP-PPA) yang dibuat dengan metode manual memiliki hubungan mendekati sempurna dari nilai koefisien korelasi Spearman yang diperoleh. Berdasarkan ujicoba tersebut dapat disimpulkan bahwa ada hubungan positif antara sistem yang dibuat dengan metode manual dalam proses seleksi calon penerima beasiswa. Kata Kunci: MADM, SAW, Beasiswa, Seleksi, Spearman. Scholarships are given financial support to students to follow and / or complete higher education based on the considerations or limitations ekononomi major achievement. The selection process of determining recipients still using a scale of priority according to the general guidelines book tuition assistance scholarships and academic achievement in order to get the scholarship recipients. Methods Multiple Attribute Decision Making (MADM) and Simple Additive weighting method (SAW) can be used for decision support. Implementation of Multiple Attribute Decision Making (MADM) is used to look for alternatives from a number of alternatives with certain criteria. then performed by searching the weights for each attribute and then generate rangkings to determine the best alternative by using Simple Additive weighting method (SAW). By using the support system the grantee can help the scholarship selection process with the required criteria include GPA, student achievement, number of family members, income Parent, Filing Status Scholarship, and Semester. Based on the test selection decision support system Education Costs Scholarship Assistance PPA (BBP-PPA) made with the manual method has a near-perfect correlation of Spearman correlation coefficient values were obtained. Based on these trials can be concluded that there is a positive relationship between the system created by manual methods in the selection process of the applicants. Keywords : MADM , SAW , Scholarships, Selection , Spearman

A multi-attribute decision making procedure using fuzzy numbers and hybrid aggregators

The classical Analytical Hierarchy Process (AHP) has two limitations. Firstly, it disregards the aspect of uncertainty that usually embedded in the data or information expressed by human. Secondly, it ignores the aspect of interdependencies among attributes during aggregation. The application of fuzzy numbers aids in confronting the former issue whereas, the usage of Choquet Integral operator helps in dealing with the later issue. However, the application of fuzzy numbers into multi-attribute decision making (MADM) demands some additional steps and inputs from decision maker(s). Similarly, identification of monotone measure weights prior to employing Choquet Integral requires huge number of computational steps and amount of inputs from decision makers, especially with the increasing number of attributes. Therefore, this research proposed a MADM procedure which able to reduce the number of computational steps and amount of information required from the decision makers when dealing with these two aspects simultaneously. To attain primary goal of this research, five phases were executed. First, the concept of fuzzy set theory and its application in AHP were investigated. Second, an analysis on the aggregation operators was conducted. Third, the investigation was narrowed on Choquet Integral and its associate monotone measure. Subsequently, the proposed procedure was developed with the convergence of five major components namely Factor Analysis, Fuzzy-Linguistic Estimator, Choquet Integral, Mikhailov‘s Fuzzy AHP, and Simple Weighted Average. Finally, the feasibility of the proposed procedure was verified by solving a real MADM problem where the image of three stores located in Sabak Bernam, Selangor, Malaysia was analysed from the homemakers‘ perspective. This research has a potential in motivating more decision makers to simultaneously include uncertainties in human‘s data and interdependencies among attributes when solving any MADM problems

A hybrid multiattribute decision making model for evaluating students’ satisfaction towards hostels

This paper proposes a new hybrid multiattribute decision making (MADM) model which deals with the interactions that usually exist between hostel attributes in the process of measuring the students’ satisfaction towards a set of hostels and identifying the optimal strategies for enhancing their satisfaction. The model uses systematic random stratified sampling approach for data collection purpose as students dwelling in hostels are “naturally” clustered by block and gender, factor analysis for extracting large set of hostel attributes into fewer independent factors, λ-measure for characterizing the interactions shared by the attributes within each factor, Choquet integral for aggregating the interactive performance scores within each factor, Mikhailov’s fuzzy analytical hierarchy process (MFAHP) for determining the weights of independent factors, and simple weighted average (SWA) operator to measure the overall satisfaction score of each hostel. A real evaluation involving fourteen Universiti Utara Malaysia (UUM) hostels was carried out in order to demonstrate the model’s feasibility. The same evaluation was performed using an additive aggregation model in order to illustrate the effects of ignoring the interactions shared by attributes in hostel satisfaction analysis

A Hybrid Multiple Attribute Decision Making Model for Measuring Image Scores of a Set of Stores

Evaluating store image is a challenging task as it incorporates with multiple attributes. Earlier quantitative studies paid minimal attention on assessing the stores based on their image scores and overlooked the interaction aspects between attributes in the process of identifying the optimal strategies for image enhancement. This paper proposes a hybrid multiple attribute decision making model for quantitatively performing image evaluation involving a set of stores. The model uses factor analysis to extract the large set of interacted attributes into fewer independent factors, Sugeno measure to characterize the interactions between attributes, Choquet integral to aggregate the interactive performance scores within each extracted factor, Mikhailo

Multi-criteria trapezoidal valued intuitionistic fuzzy decision making with Choquet integral based TOPSIS

A generalized trapezoidal-valued intuitionistic fuzzy geometric aggregation operator is proposed which is then used to aggregate decision makers' opinions in group decision making process. An extension of TOPSIS, a multi-criteria trapezoidal-valued intuitionistic fuzzy decision making technique, to a group decision environment is also proposed, where inter-dependent or interactive characteristics among criteria and preference of decision makers are under consideration. Furthermore, Choquet integral-based distance between trapezoidal-valued intuitionistic fuzzy values is defined. Combining the trapezoidal-valued intuitionistic fuzzy geometric aggregation operator with Choquet integral-based distance, an extension of TOPSIS method is developed to deal with a multi-criteria trapezoidal-valued intuitionistic fuzzy group decision making problems. Finally, an illustrative example is provided to understand the proposed method

Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time

A decision model to prioritise logistics performance indicators

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPerformance measurement is an important concern that has recently attracted much attention in the logistics area from both practitioners and academics. The performance measurement of logistics companies is based upon diverse performance indicators. However, to date, limited attention has been paid to the performance measurement of logistics companies and, also, performance measurement processes have become more complex for logistics companies due to the existence of numerous performance indicators. In this regard, the way in which decision makers in logistics companies deal with some vaguenesses, such as deciding on the most important indicators holistically and determining interrelationships between performance indicators, has remained an issue that needs to be resolved. This study, therefore, aims to offer a comprehensive decision model for identifying the key logistics performance indicators and determining the interrelationships among these indicators from logisticians’ perspective. In line with this purpose, the research first presents a stakeholder-based Balanced Scorecard (BSC) model which provides a balanced view by including financial and non-financial performance indicators and a comprehensive approach as a response to the major shortcoming of the generic BSC regarding the negligence of various stakeholders. Then, a large number of performance indicators used in logistics are systematically examined under the proposed model, and the key indicators are selected through an online survey conducted in the Turkish logistics industry. Subsequently, since the performance measurement indicators are not independent of each other, it is critical to understand the causal relationships among different indicators. In such cases, group decision making techniques are capable of modelling such complexities. After a systematic comparison of these techniques, a realistic and easy-to-follow multi-criteria decision making technique, the Analytic Network Process (ANP), is revealed as a suitably powerful method to determine the interrelationships among the indicators. Additionally, a case study approach based on the data obtained from three logistics companies is used to illustrate both the applicability of the model and the practicality of the ANP application. Furthermore, the sensitivity of the results about the case companies is also analysed with several relevant ‘what-if’ scenarios. Thus, real-life practices of three case companies are investigated with the proposed approach. Consequently, this research proposes the BSC-ANP integration which provides a novel way and in-depth understanding to evaluate logistics performance indicators for the competitiveness of logistics companies. Thus, in order to address the aforementioned vaguenesses, the proposed model in this study identifies key performance indicators with the consideration of various stakeholders in the logistics industry to decide on the most important indicators, and evaluates the interrelationships among the indicators by using the ANP. The results of the study show that the educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies and four prominent indicators (educated employee, managerial skills, cost, and profitability) need to be primarily considered by logistics companies. In this way, with this integration, not only the performance indicators in logistics, but also different stakeholders of logistics companies are assessed by the ANP method. This means that the results of this research are not only useful for helping logistics companies to decide which indicators should be focused on to become more competitive, but also can be used as a reference model by different stakeholders in their decision-making processes in order to select the best logistics provider. Keywords: Performance measurement; logistics performance indicators; balanced scorecard (BSC); analytic network process (ANP); multi-criteria decision making (MCDM); stakeholder

e-Process selection using decision making methods : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Palmerston North, New Zealand

The key objective of this research is to develop a selection methodology that can be used to support and aid the selection of development processes for e-Commerce Information Systems (eCIS) effectively using various decision methods. The selection methodology supports developers in their choice of an e-Commerce Information System Development Process (e-Process) by providing them with a few different decision making methods for choosing between defined e-Processes using a set of quality aspects to compare and evaluate the different options. The methodology also provides historical data of previous selections that can be used to further support their specific choice. The research was initiated by the fast growing Information Technology environment, where e-Commerce Information Systems is a relatively new development area and developers of these systems may be using new development methods and have difficulty deciding on the best suited process to use when developing new eCIS. These developers also need documentary support for their choices and this research helps them with these decision-making processes. The e-Process Selection Methodology allows for the comparison of existing development processes as well as the comparison of processes as defined by the developers. Four different decision making methods, the Value-Benefit Method (Weighted Scoring), the Analytical Hierarchy Process, Case-Based Reasoning and a Social Choice method are used to solve the problem of selecting among e-Commerce Development Methodologies. The Value-Benefit Method, when applied to the selection of an e-Process from a set of e-Processes, uses multiple quality aspects. Values are assigned to each aspect for each of the e-Processes by experts. The importance of each of the aspects, to the eCIS, is defined in terms of weights. The selected e-Process is the one with the highest score when the values and weights are multiplied and then summed. The Analytic Hierarchy Process is used to quantify a selection of quality aspects and then these are used to evaluate alternative e-Processes and thus determining the best matching solution to the problem. This process provides for the ranking and determining of the relative worth of each of the quality aspects. Case-Based Reasoning requires the capturing of the resulting knowledge of previous cases, in a knowledge base, in order to make a decision. The case database is built in such a way that the concrete factual knowledge of previous individual cases that were solved previously is stored and can be used in the decision process. Case-based reasoning is used to determine the best choices. This allows the user to either use the selection methodology or the case base database to resolve their problems or both. Social Choice Methods are based on voting processes. Individuals vote for their preferences from a set of e-Processes. The results are aggregated to obtain a final result that indicates which e-Process is the preferred one. The e-Process Selection Methodology is demonstrated and validated by the development of a prototype tool. This tool can be used to select the most suitable solution for a case at hand. The thesis includes the factors that motivated the research and the process that was followed. The e-Process Selection Methodology is summarised as well as the strengths and weaknesses discussed. The contribution to knowledge is explained and future developments are proposed. To conclude, the lessons learnt and reinforced are considered