scholarly journals Penerapan Multi Factor Evaluation Process Dalam Penerimaan Asisten Dosen Pada STMIK Triguna Dharma

2019 ◽  
Vol 3 (3) ◽  
pp. 183
Author(s):  
Puji Sari Ramadhan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Calculation Method ◽  
Scoring System ◽  
Evaluation Process ◽  
Factor Evaluation ◽  
Process Method

In helping the role of a lecturer in carrying out learning as well as an effort in improving the quality of learning it is necessary to appoint a Lecturer Assistant who is also in charge of replacing the role of the lecturer who is unable to attend. In the acceptance process of the Lecturer Assistant, it is necessary to use objective and accurate criteria and assessment using the calculation method to have a universal concept in the science of Decision Support Systems. For this reason, it is necessary to build a scoring system that can later produce decisions in selecting prospective Lecturer Assistants. Based on these views, the acceptance of the Lecturer Assistant will use one method in the Decision Support System that can produce a decision based on the ranking or ranking of each alternative, namely the Multi-Factor Evaluation Process method. With the application of this method that will be applied in an application, the system can help the institution in determining the feasibility of the acceptance process of the Lecturer Assistant so that later the Lecturer Assistant will be chosen based on the ranking of values that have been generated through the Multi-Factor Evaluation Process.

scholarly journals Implementasi Multi Factor Evaluation Process untuk Penentuan Tempat Pembuangan Akhir Berbasis Web Aplication

2020 ◽  
Vol 4 (2) ◽  
pp. 103-111
Author(s):  
Rahmat Hidayat ◽  
◽  
Ade Irmayanti ◽  
Muhammad Tommy ◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Web Application ◽  
Evaluation Process ◽  
Disposal Site ◽  
Prototype Model ◽  
Initial Weight ◽  
Final Disposal ◽  
Factor Evaluation

Determining the final waste disposal site is a complex problem for Lamandau Regency, which is a developing district, the more people there are every year, the more waste is produced. However, determining the location is still done subjectively without considering the influencing factors and is still manual. In problems like this, the decision support system can be used as a solution to help make decisions. This study aims to implement a decision support system in determining the final disposal site using the Multi-Factor Evaluation Process (MFEP) method which is applied in the form of a Web Application using a prototype model. In determining the final disposal site, there are 5 criteria to be assessed, namely: Cover Land with an initial weight of 0.2, Rain Intensity with an initial weight of 0.1, Nature Reserve with an initial weight of 0.2, Agriculture with an initial weight of 0.3 and Entrance roads with an initial weight of 0.2, and the number of alternatives consists of 5 locations. The findings show that the error rate of this system is below 5%. After testing all modules or system components, all of them were successful and feasible to be used as a tool in determining the final place of development.

scholarly journals Decision Support System using Multi-Factor Evaluation Process Algorithm

2021 ◽  
Vol 1933 (1) ◽  
pp. 012016
Author(s):  
Bertha Jean Que ◽  
Sulistyo Andarmoyo ◽  
Eka Hendrayani ◽  
Romindo ◽  
Dahlan Abdullah ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Evaluation Process ◽  
Factor Evaluation

scholarly journals PERBANDINGAN METODE SAW DAN MFEP SISTEM PENDUKUNG KEPUTUSAN PENYELEKSIAN ATLET PENCAK SILAT

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Siti Qomariah ◽  
Andi Yusika Rangan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Evaluation Process ◽  
Factor Evaluation ◽  
Simple Additive Weighting ◽  
Hierarchy Process

Pencaksilat salah satu bentuk identitas seni beladiri nusantara kebudayaan Indonesia berisitentang pendidikan yang berkembang dalam masyarakat. Dalam dunia modern, silatbukan  hanya  sebagai  alat  seni  bela  diri  tetapi berkembang  menjadi  sebuah  upayadalam  memelihara  kesehatan  melalui  olahraga.  Decision support system (DSS) adalah sistem yang dibangun untuk membantu dalam pengabilan keputusan. DSS dapat dibangun dengan mengunakan beberapa metode seperti AHP (Analitycal Hierarchy Process ) SAW (Simple Additive Weighting), MFEP (Multi Factor Evaluation Process),Weighted Product dan lain-lain. Silat salah satu bentuk identitas seni beladiri nusantara kebudayaan Indonesia berisi tentang pendidikan yang berkembang dalam masyarakat.Penelitian ini bertujuan membandingkan hasil dari implementasi metode SAW dengan MFEP.

scholarly journals AHP Method for Selecting Wood As Furniture Raw Material

2020 ◽  
Vol 13 (1) ◽  
pp. 37-46
Author(s):  
Gatot Setiawan ◽  
Imam Husni Al Amin
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Wood Quality ◽  
Raw Material ◽  
Furniture Industry ◽  
Wood Age ◽  
Hierarchy Process ◽  
Process Method

Wood is an important element determining the quality of a product on furniture. The lack of knowledge of the furniture industry in this industry causes problems to choose a decision in determining the wood material to produce quality and quality furniture products. Development of a decision support system using the Analytichal Hierarchy Process method uses wood quality feasibility parameters consisting of five criteria, namely Physical Physical Properties, Mechanical Mechanical Properties of Wood, Wood Grade, Wood Age and Wood Substance from several samples. The results of this study were to produce a decision from the data to determine a decision, a decision support system for the purchase of wood materials for a furniture industry company using the Analytichal Hierarchy Process method. After testing, it can be concluded that the decision obtained is appropriate for use by staff and superiors and is ready to be implemented. 

SISTEM PENDUKUNG KEPUTUSAN DALAM MEREKOMENDASIKAN TEMPAT LES MUSIK DIPEMATANGSIANTAR MENGGUNAKAN METODE MULTIFACTOR EVALUATION PROCESS (MFEP)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Theresia Siburian ◽  
Rafiqa Dewi ◽  
Widodo Widodo
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Music Education ◽  
Support System ◽  
Evaluation Process ◽  
Music Lesson ◽  
Music Lessons ◽  
Factor Evaluation ◽  
The Right

Keroncong, Pop, Dangdut, and Hip Pop. Music is basically to entertain and express themselves from feelings of sadness or happiness. Along with the increasing popularity of music, a lot of music lessons were made especially in Pematangsiantar. This makes consumers confused and needs information to decide which music lessons are suitable for their needs. So the author uses a Decision Support System with the Multi-Factor Evaluation Process (MFEP) method to facilitate decision making. MFEP is a method with emphasis on various factors and criteria that perform the calculation of weighting system where the calculation will be valuable for each factor that affects the decision making of data data to be processed. The MFEP method is also called a scaled score that requires a comparative norm to be interpreted qualitatively and this makes the advantages of the MFEP method. The previous criteria were Criteria Place C1 (C1), Music Facilities Criteria (C2), Price Criteria (C3), Criteria Schedule / time guidance (C4), Employee Service Criteria (C5). By using 4 Alternative Place of Music Les existed memematangsiantar include: Legato Art Center, Era Music Siantar, Grace Music Studio, C and C Music Education. Results of recommendation of Decision Support System in Recommending Place Les Music dipematangsiantar using method MFEP (Multifactor Evaluation Process). Which we write with first rank that is Legato Art Center .  It is expected that this research can help the consumer to get the right recommendation in choosing the place of music lesson in accordance with the cost of music lesson and music quality.Keywords: Decision Support System, Multi-Factor Evaluation Process, ranking, Music

scholarly journals Option for evaluation of development of decision support system requirements

Connectivity ◽  
2021 ◽  
Vol 149 (1) ◽  
Author(s):  
I. M. Gamanyuk ◽  
◽  
O. V. Nehodenko ◽  
K. P. Storchak ◽  
О. S. Dzyadovych
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Development ◽  
Evaluation Process ◽  
Type I ◽  
System Requirements ◽  
The Creation ◽  
Address System

The quality of the decision support system (DSS) is influenced by the process of creating this system. An important part of the DSS creation process is occupied by events that discuss issues related to the definition of system requirements. Both representatives of the customer and representatives of the executor take part in these actions. The difference between the participants creates uncertainty. It is important to understand the weaknesses in measures to address system requirements in the early iterations of system development. This will allow appropriate measures to be taken to improve the quality of measures to address system requirements. This paper proposes the use of Bayesian methods to evaluate the development of requirements for the creation of a decision support system. A model is proposed in which the participants of the events evaluate the measures in terms of addressing all issues and uncertainties. After the implementation of the requirements discussed at the events, the results of testing are evaluated for these activities. The analysis of the assessments provided by the participants of the activities and evaluations, based on the test results, provides an opportunity to draw appropriate conclusions and take appropriate measures. During the evaluation process, type I errors occur — the activities were evaluated by the participants on «3», which meant that not all issues were resolved and problems exist, and as a result no errors were made in implementing the precedents worked on at these events. Type II error — the measures were evaluated by the participants on «5», which meant that all issues were resolved and there were no unresolved issues, and as a result errors were obtained in the implementation of precedents, which were worked out at these events. The article processes the initial data. The historical representation is determined: P(T|D) = P(D,T)/P(D). The posterior representation is determined: P(D|T) = P(D,T)/P(T). Using this mathematical model, we can assess the quality of processing the requirements for the creation of DSS. In the case of obtaining low values of P(D1|T1), P(D2|T2), P(D3|T3) it can be concluded that the measures to process the requirements are not carried out at the appropriate level and may need to be carried out differently. In the case of obtaining low values of P(D3|T1), P(D1|T3) it can be concluded that the measures to process the requirements are carried out at the appropriate level and the probability of errors of I and II kind is quite low. Based on the results of the work on the creation of the first and second stages of DSS, it is possible to draw conclusions and make organizational decisions, and as a result, other stages of the creation of DSS will be better than the first and second stages. More and more activities are moving to the electronic form, the implementation of the function of estimating the processing of the requirement is becoming easier, so research in this area has prospects.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU BERPRESTASI KOTA MEDAN MENERAPKAN METODE PREFERENCES SELECTION INDEX (STUDI KASUS : DINAS PENDIDIKAN KOTA MEDAN)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fajar Syahputra ◽  
Mesran Mesran ◽  
Ikhwan Lubis ◽  
Agus Perdana Windarto
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Formal Education ◽  
Basic Education ◽  
Selection Index ◽  
Main Task ◽  
Selection Of

The teacher is a major milestone in the world of education, the ability and achievement of students cannot be separated from the role of a teacher in teaching and guiding students. Based on the Law of the Republic of Indonesia No. 14 of 2005 concerning Teachers and Lecturers, in Article 1 explained that teachers are professional educators with the main task of educating, teaching, guiding, directing, training, evaluating, and evaluating students in early childhood education through formal education, basic education and education medium. Whereas in Article 4 of the Act, it is explained that the position of teachers as professionals serves to enhance the dignity and role of teachers as learning agents to function to improve the quality of national education.Decision making is an election process, among various alternatives that aim to meet one or several targets. The decision-making system has 4 phases, namely intelligence, design, choice and implementation. These phases are the basis for decision making, which ends with a recommendation.The Preferences Selection Index (PSI) method is a rarely used decision support system method. This method is a method developed by stevanie and Bhatt (2010) to solve the Multi Criteria Decision Making (MCDM). With the right consideration, this method can be one of the tools to determine policies in decision-making systems, especially the selection of outstanding teachers. Determination of policies taken as a basis for decision making, must use criteria that can be defined clearly and objectively.Keywords: Decision Support System, PSI, Selection of Achieving Teachers

scholarly journals A Decision Support System for Public Funding of Experimental Development in Energy Research

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1357 ◽  
Author(s):  
Simon Hirzel ◽  
Tim Hettesheimer ◽  
Peter Viebahn ◽  
Manfred Fischedick
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Public Funding ◽  
Evaluation Process ◽  
Decision Makers ◽  
Market Failures ◽  
Energy Research ◽  
Experimental Development

New energy technologies may fail to make the transition to the market once research funding has ended due to a lack of private engagement to conclude their development. Extending public funding to cover such experimental developments could be one way to improve this transition. However, identifying promising research and development (R&D) proposals for this purpose is a difficult task for the following reasons: Close-to-market implementations regularly require substantial resources while public budgets are limited; the allocation of public funds needs to be fair, open, and documented; the evaluation is complex and subject to public sector regulations for public engagement in R&D funding. This calls for a rigorous evaluation process. This paper proposes an operational three-staged decision support system (DSS) to assist decision-makers in public funding institutions in the ex-ante evaluation of R&D proposals for large-scale close-to-market projects in energy research. The system was developed based on a review of literature and related approaches from practice combined with a series of workshops with practitioners from German public funding institutions. The results confirm that the decision-making process is a complex one that is not limited to simply scoring R&D proposals. Decision-makers also have to deal with various additional issues such as determining the state of technological development, verifying market failures or considering existing funding portfolios. The DSS that is suggested in this paper is unique in the sense that it goes beyond mere multi-criteria aggregation procedures and addresses these issues as well to help guide decision-makers in public institutions through the evaluation process.

Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongming Gao ◽  
Hongwei Liu ◽  
Haiying Ma ◽  
Cunjun Ye ◽  
Mingjun Zhan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Scoring System ◽  
Credit Scoring ◽  
Prediction Performance ◽  
Content Type ◽  
Connected Subgroup ◽  
Source Data ◽  
Credit Scores

PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

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