Perbandingan Metode Smart dan Maut untuk Pemilihan Karyawan pada Merapi Online Corporation

<p>Penelitian ini merancang sistem untuk menentukan pemilihan karyawan terbaik menggunakan Sistem Pendukung Keputusan (SPK). Perhitungan sistem menggunakan metode SMART dan MAUT. SMART merupakan metode pengambilan keputusan multiatribut yang setiap alternatif terdiri dari sekumpulan atribut dan setiap atribut mempunyai nilai-nilai. Sedangkan MAUT didasarkan pada konsep dimana pembuat keputusan dapat menghitung utilitas dari setiap alternatif menggunakan fungsi MAUT dan dapat memilih alternatif dengan utilitas tertinggi. Metode SMART digunakan karena perhitungannya lebih sederhana dan memungkinkan penambahan serta pengurangan alternatif tanpa mempengaruhi perhitungan pembobotan mengingat jumlah karyawan bisa berkurang dan bertambah secara tidak teratur. Sedangkan metode MAUT digunakan karena memunculkan hasil urutan peringkat dimana akan muncul hasil nilai terbesar sampai nilai terkecil sehingga dapat diketahui karyawan dengan terbaik dengan nilai tertinggi. Sehingga dapat mengambil keputusan dengan efektif atas persoalan yang kompleks dengan menyederhanakan dan mempercepat proses pengambilan keputusan. Metode penelitian yang digunakan adalah metode pengembangan sistem model waterfall, metodologi ini terdapat tahapan-tahapan kegiatan yang harus dilakukan dalam merancang suatu sistem. Perhitungan menggunakan 30 sampel data karyawan dan empat kriteria penilaian. Empat kriteria tersebut adalah presensi dengan bobot 40, masa kerja dengan bobot 30, ijin dengan bobot 20, dan disiplin dengan bobot 10. Data karyawan yang digunakan adalah karyawan yang sama dalam kedua metode serta mempunyai data penilaian yang sama. Hasil perhitungan menggunakan SMART dan MAUT menunjukkan bahwa keduanya dapat diimplementasikan dan berfungsi dengan baik untuk menentukan karyawan terbaik. Dengan menggunakan data alternatif, nilai alternatif, dan bobot kriteria yang sama diperoleh hasil bahwa metode SMART memberikan hasil yang lebih baik dengan 22 peringkat, sedangkan metode MAUT menghasilkan 18 peringkat. Semakin banyak jumlah peringkat yang muncul maka semakin baik karena mampu meminimalisir nilai preferensi yang sama, sehingga perankingan alternatif dapat dilakukan dengan baik.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>This study designed a system to determine the best employee selection using a Decision Support System (SPK). System calculations using the SMART and MAUT methods. SMART is a multi-attribute decision making method in which each alternative consists of a set of attributes and each attribute has values. Whereas MAUT is based on the concept where decision makers can calculate the utility of each alternative using the MAUT function and can choose alternatives with the highest utility. The SMART method is used because the calculation is simpler and allows the addition and subtraction of alternatives without affecting the weighting calculation given the number of employees can be reduced and increased irregularly. While the MAUT method is used because it raises the ranking order results in which the largest value will appear until the smallest value so that it can be known by the employee with the highest value. So that they can make decisions effectively on complex issues by simplifying and accelerating the decision making process. The research method used is the method of developing the system waterfall model, this methodology there are stages of activities that must be carried out in designing a system. The calculation uses 30 employee data samples and four assessment criteria. The four criteria are presence with a weight of 40, tenure with a weight of 30, permission with a weight of 20, and discipline with a weight of 10. Employee data used are the same employees in both methods and have the same assessment data. The results of calculations using SMART and MAUT indicate that both can be implemented and function properly to determine the best employees. By using alternative data, alternative values, and the same criteria weights, the results obtained that the SMART method gives better results with 22 ratings, while the MAUT method yields 18 ratings. The more number of ratings that appear, the better because it is able to minimize the same preference value, so that alternative ranking can be done well.</em></p><p><em><strong><br /></strong></em></p><p class="Abstrak"> </p>

Fuzzy-AHP MOORA approach for vendor selection applications

Vendor selection is a critical activity in order to support the achievement of company success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, an evaluation is needed to see which vendors match the company's criteria. The purpose of this study is to evaluate and select the proposed vendor in a web-based decision support system (DSS) by using the fuzzy-AHP MOORA approach. The fuzzy-AHP method is used to determine the importance level of the criteria, while the MOORA method is used for alternative ranking. The results showed that vendor 4 has the highest score than other alternatives with a value of 0.2536. Sensitivity analysis showed that the proposed DSS fuzzy-AHP MOORA concept was already solid and suitable for this problem, with a low rate of change.

SISTEM PENDUKUNG KEPUTUSAN DALAM PEMILIHAN HOTEL DI TANGERANG MENGGUNAKAN METODE AHP DAN TOPSIS

Banten Province has a city, namely Tangerang, to be precise in the northern part of Banten City. There are many tourist attractions that we can visit in the city of Tangerang, such as natural scenery, historical places, natural scenery, photo spots and culinary tours. With various tourist attractions in the city of Tangerang, it attracts tourists who want to travel to these places. With so many interested tourists visiting the city of Tangerang, the existing hotel is one of the destinations for tourists who are visited for a place to stay and rest. This has had an impact on the increasing number of hotels in the area, which has led to an increasing variety of choices for tourists. To make it easier for tourists to choose a hotel according to their needs, a decision support system is needed in choosing a hotel to use for a place to stay and rest. Tourists can choose hotels according to their desired needs by using a decision support system with various criteria. The decision support system applies the initial criteria weighting by using the AHP method and hotel alternative ranking using the TOPSIS method. The system has been tested and implemented by distributing questionnaires to 30 respondents using the USE Questionnaire and applying the Sala Likert Method to perform the questionnaire calculations, and the final result obtained in the calculation of the questionnaire is 84.51%.

Kombinasi Metode Simple Additive Weigthing dan Weigthed Product Untuk Seleksi Proposal Program Kreatifitas Mahasiswa

This Research aims to develop a decision support system that can facilitate the proposal selection process and provide an alternative ranking for the selection results of student creativity program proposal selection. This decision support system uses a combination calculation of the Simple Additive Weighting and Weigthted Product methods, hereinafter referred to as Modified SAW. The criteria used in this assessment refer to the 2020 Student Creativity Program Guidebook. The data used in this decision support system uses proposal selection data in the Student Creativity Development Unit of Muhammadiyah University of North Sumatra in 2019 for 2020. This system was developed by determining criteria and weight determination using the Simple Additive Weighting method and then make improvements to the weight and determine the preference value using the Weighted Product method. Each of the SAW and WP methods certainly has advantages and disadvantages. The advantages of SAW with a simple and simple ranking process, can be applied to decision-making cases such as in the recommendation of selecting proposals with various attributes. While the use of Weighted Product (WP) is often used because the weight is calculated based on the level of importance and can evaluate the set of attributes by multiplying all criteria with alternative results as well as the power between weights and alternative multiplication results. This WP method can also be used in assisting in recommendation of proposal selection based on what is needed by the University. By utilizing the advantages and disadvantages of each method, this combination is able to produce an accuracy of 91% for the SAW method, 97% for the accuracy using the WP and 99% for the accuracy value for the combination of the SAW and WP methods. This decision support system using MOD SAW can help facilitate the proposal selection process and provide alternative ranking results. Further research is suggested for the development of a decision support system for proposal selection using a combination of different methods between SAW and other methods.

IMPLEMENTASI METODE HYBRID SAW-TOPSIS DALAM MULTI ATTRIBUTE DECISION MAKING PEMILIHAN LAPTOP

Various specifications and prices for a laptop make potential buyers confused about choosing it. Information technology with its technological developments has produced a system that can provide alternative decisions for decision-making problems.This study aims to develop a system that can select the best laptop from several alternatives. There are 5 parameters used in determining the priority of alternative laptops. They are hard disk drive, RAM, processor, operating system and price.The alternative of the decision making system also consists of 5 alternatives.The method used in the MultiAttribute Decision Making (MADM) research is a combination of 2 methods. These methods are SAW and TOPSIS methods.The SAW method is used to optimize the parameter weighting process and the specific TOPSIS method to complete the alternative ranking process.This hybrid method can produce a more precise MADM process because it uses two methods, each of which has characteristics in accordance with the process specifications. Keywords: laptop; multi attribute decision making; SAW; TOPSIS Abstrak: Spesifikasi dan variasi harga yang beragam dari sebuah laptop membuat calon pembeli menjadi kebingungan dan ragu dalam memutuskan jenis atau tipe laptop mana yang akan dibeli. Teknologi informasi beserta dengan perkembangan teknologinya dapat menghasilkan suatu system untuk membantu memberikan alternative keputusan untuk suatu permasalahan pengambilan keputusan. Pengembangan sistem yang dapat memilih laptop yang tepat dari beberapa alternatif yang ditawarkan merupakan tujuan dari penelitian ini. Ada 5 parameter yang digunakan dalam menentukan prioritas alternatif laptop, yaitu hard disk drive, RAM, prosesor, sistem operasi dan harga. Laptop yang ditawarkan sebagai alternatif juga sebanyak 5 jenis. Metode yang digunakan padaMulti Attribute Decision Making (MADM) adalah kombinasi dari 2 metode, yaitu metode SAW dan TOPSIS. Metode SAW digunakan untuk mengoptimalkan proses pembobotan parameter dan metode TOPSIS spesifik untuk menyelesaikan proses perangkingan alternatif. Metode hybrid ini dapat menghasilkan suatu proses MADM yang lebih tepat karena menggunakan dua metode yang masing-masing mempunyai karakteristik sesuai dengan proses yang dilakukannya. Kata kunci: laptop; multi-attribute decision making; SAW; TOPSIS

Challenges and action plans in construction sector owing to COVID-19 pandemic – a case in Indonesia real estates

Purpose All industry has been facing tremendous pressure since the beginning of 2020 owing to COVID-19 crisis, including real estate construction. The research is a case study investigating challenges and action plans faced by stakeholders (developers/consultants/contractors) in real estate construction in some major cities in Indonesia. This study aims to identify and prioritize on action plans related to real estate construction stakeholders. Design/methodology/approach The study gathered challenges and action plans by distributing open questionnaire to expert to get insights in their own verbatim. Data reduction was then performed to get high-level challenges and high-level action plans. High-level action plans were then analyzed using proposed Eisenhower-simultaneous importance performance analysis (SIPA) matrix which help prioritize high-level action plans. Correlation matrix was also constructed to gain insight on relation between action plans to challenges and to three main LEAN elements (Muda, Mura and Muri). Alternative ranking method using “sum-product to 3M” approach was also performed to give complementary insights. Findings Ten action plans fall under category “Act now and become COVID-19 champion company.” One falls under category “prepare and invest now to gain competitive advantage,” one falls under category “external collaboration now to survive,” one falls under category “external collaboration for potential efficiency,” whereas the remaining six action plans fall under category “let-it-go” or “do-nothing.” Research limitations/implications The study gathered only 48 and 64 respondents in its first and second questionnaires. Despite small number, the respondents are experts in their own field, and their valuable insights and responses off-set the limited number of participants. The study gives insightful action plans that can be taken by stakeholders in real-estate construction in Indonesia’s major cities analyzed by proposed Eisenhower-SIPA matrix. Originality/value The novelty of the research lies in the insights from industry experts in dealing with current COVID-19 pandemic in real-estate construction in Indonesia. Added value is also given through the use of Eisenhower-SIPA matrix.

A novel generalized grey target decision method with index and weight both containing mixed types of data

PurposeThis paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.Design/methodology/approachThe decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option.FindingsThe certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method.Research limitations/implicationsThe proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy.Originality/valueThe proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.

A PROMETHEE II-BELIEFapproach for multi-criteria decision-making problems with incomplete information

Multi-criteria decision aid methods consider decision problems in which many alternatives are evaluated on several criteria. These methods are used to deal with perfect information. However, in practice, it is obvious that this information requirement is too strict. In fact, the imperfect data provided by more or less reliable decision makers usually affect decision results, since any decision is closely linked to the quality and availability of information. In this paper, a PROMETHEE II-BELIEF approach is proposed to help multi-criteria decisions based on incomplete information. This approach solves problems with incomplete decision matrix and unknown weights within PROMETHEE II method. On the basis of belief function theory, our approach first determines the distributions of belief masses based on PROMETHEE II’s net flows, and then calculates weights. Subsequently, it aggregates the distribution masses associated with each criterion using Murphy’s modified combination rule in order to infer a global belief structure. The final alternative ranking is obtained via pignistic probability transformation. A case study of a real-world application concerning the location of a treatment center of waste from healthcare activities with infectious risk in the center of Tunisia is studied to illustrate the detailed process of the PROMETHEE II-BELIEF approach. Keywords: multiple criteria aid, incomplete information, PROMETHEE II method, belief function theory.

Implementasi Metode SAW Terhadap Sistem Pendukung Keputusan Memilih Dosen Terbaik Pada Politeknik Ganesha

The selection of the best lecturers gives recognition to lecturers who carry out tridharma higher education activities, whose results can be proud of and are useful for advancing academic and institutional quality. Educational institutions, especially the Ganesha Medan Polytechnic, annually select the best lecturers by conducting assessments by distributing questionnaires to their students. However, the assessment process is still done manually, so it takes a long time to process the data. In addition, the assessment is still not relevant to the actual situation because it only uses one assessment criterion, namely a student assessment questionnaire. Based on this, in this study, a decision support system design was used to select the best lecturers at the Ganesha Medan Polytechnic. Decision support systems built on a web basis using the PHP programming language and MySQL database. The decision making method used is the Simple Additive Weighting (SAW) method. This method is used to perform the best alternative ranking process from a number of alternatives.