IMPLEMENTATION OF SIMPLE ADDITIVE WEIGHTING (SAW) ALGORITHM IN DECISION SUPPORT SYSTEM FOR DETERMINING WORKING AREA FOR COOPERATIVE

Today, the cooperative in Medan has difficulty in determining the work area for employees. The difficulty in determining the work area of ​​employees is because the number of employees does not meet the needs. In addition, the employees' lack of knowledge about the work area will be a problem. This has resulted in obstruction of the performance of employees at the Medan Cooperative. To overcome this problem, a decision support system is needed that can assist in making decisions to determine the employee's work area. Using the Simple Additive Weighting (SAW) method is expected to help companies to be able to assess and determine employees who meet the criteria and can work for a long time. The Decision Support System for selecting the work area that is currently being designed is the Decision Support System for Determining Work Areas in Cooperatives in Medan Using the Simple Additive Weighting (SAW) Method. Where with this system it can make it easier for companies to place employee work areas based on the area where the employee lives. The system designed can be used for all employees because it is very easy to use, no special computer skills are needed. So that employees can use this system to generate reports about their work area based on residence. Decision support system application designed using the Simple Additive Weighting (SAW) method at the Cooperative in Medan, can determine the work area of ​​employees

WEBIRA - comparative analysis of weight balancing method

The attributes weight establishing problem is one of the most important MCDM tasks. This study summarizes weight determining approach which is called WEBIRA (WEight Balancing Indicator Ranks Accordance). This method requires to solve complicated optimization problem and its application is possible by carrying out non trivial calculations. The efficiency of WEBIRA and other MCDM methods – SAW (Simple Additive Weighting) and EMDCW (Entropy Method for Determining the Criterion Weight) compared for 4 different data normalization methods. The re-sults of the study revealed that more sophisticated WEBIRA method is significantly efficient for all considered numbers of alternatives. Efficiency of all methods decreases with increasing number of alternatives, but WEBIRA is still applicable, while appli-cation of other methods is impossible as the number of alternatives is greater than 11. WEBIRA is the least affected by the data normalization, while EMDCW is the most affected method

Exploring Simple Addictive Weighting (SAW) for Decision-Making

The Simple Additive Weighting (SAW) method is a well-known and widely utilized approach for decision-making in various disciplines. The SAW method involves a step-by-step process that enables decision-makers to evaluate and rank alternatives based on their respective attribute values and assigned weights. Within this context, this paper aims to provide a comprehensive exploration of SAW for decision-making. This study demonstrates the utilization of the SAW method in supplier selection, which aims to streamline and optimize the supply chain management process for organizational business.The resultsderived from the study have revealed its practicality, effectiveness, and adaptability in handling multi-criteria decision problems, by examining its principles, advantages, limitations, and application based on real situations. It's important to note that the method's reliance on accurate weight assignment to criteria poses a challenge. This process can be subjective and intricate, especially when faced with conflicting objectives. However, SAW stands as a valuable addition to the decision-maker's toolkit, providing a structured and transparent framework for making well-informed choices amidst complexity

A multiple criteria decision-making approach for increasing the preparedness level of sales departments against COVID-19 and future pandemics: A real-world case

The impact of the pandemic and the lockdown has been more devastating than expected on the world economy. It is essential to formulate strategies in real-time. In this research, a multicriteria decision-making model for increasing the preparedness level of sales departments when facing COVID-19 waves and future pandemics is proposed. The model is comprised of 8 criteria, 29 sub-criteria, and 7 alternatives. The study is based on the integration of the AHP and TOPSIS techniques. AHP is used for calculating the criteria and sub-criteria weights. While, TOPSIS is used for calculating the preparedness level, ranking the companies, and identifying the weaknesses that should be addressed for increasing their effectiveness in the current market scenario. The model is developed with the aid of an experts’ group from the electrical appliance sector and studies from the reported literature. This application is completely novel in the literature and has been applied in the wild with remarkable companies in Colombia. A case study in the electrical appliance sector is presented as a pilot study but it should be noted that the methodology is flexible and scalable in any scenario

Logistic regression for criteria weight elicitation in PROMETHEE-based ranking methods

For a PROMETHEE II method used to rank concurrent alternatives both preference functions and weights are required, and if the weights are unknown, they can be elicited by leveraging present or past partial rankings. If the known partial ranking is incorrect, the eliciting methods are ineffective. In this paper a logistic regression method for weight elicitation is proposed to tackle this scenario. An experiment is carried out to compare the logistic regression method performance against a state-of-the-art linear weight elicitation method, proving the validity of the proposed methodology

Implementasi Metode Simple Additive Weighting Program Penerima Bantuan Indonesia Pintar di SMA 6 Pandeglang

Program indonesia pintar merupakan pemberian bantuan tunai pendidikan yang diperuntukan bagi siswa SD,SMP,dan SMA dengan rentang usia 6-21 tahun yang berasal dari keluarga kurang mampu. Namun permasalahan yang muncul pada seleksi calon penerima bantuan program indonesia pintar masih dikerjakan dengan manual dan sering terjadi penerima bantuan tidak tepat sasaran. Tujuan penelitian ini untuk mempermudah dalam menentukan calon penerima bantuan indonesia pintar. Manfaat penelitian ini dapat membantu pihak sekolah dalam menentukan penerima KIP di SMA 6 pandeglang. Metode yang digunakan pada penelitian ini menggunakan Simple Additive Weighting dengan menentukan kriteria-kriteria persyaratan ijasah dan ktp, berprestasi, tanggungan orangtua, yatim piatu. Kelebihan penelitian ini dapat digunakan oleh seluhur civitas yang ada di sekolah. Kekurangan penelitian ini hanya yang dapat mengoperasikan komputer dan paham dalam menjalankan komputer. Hasil peneltian ini berupa Wahyu dengan nilai 0,914495, icha dengan nilai 0,862875, Siti halimatu 0,847741, Zahrotul 0,830887, Bustami 0,826302, Muhamad Hanif 0,724199. Maka yang berhak mendapatkan bantuan Wahyu dengan rangking 1

SAW-TOPSIS Implementation To Determine An Appropriate DBMS Software

Selection an appropriate Database Management Software, is a crucial part to ensure operational excellence businesses firm. Database management software used to organize and manage the company’s data so that they can be efficiently accessed and used to improve operational and decision quality. However, a senior manager as decision maker sometimes lacks the comprehensive knowledge to choose a suitable database management software which meets with business needs. Then, The manager determines a database management software based on a consultant or vendor offer. On the other hand, a consultant or vendor has an interest in to sell their product, so they tend to lead manager to choose their product even though it is not fulfilling business needs. We present a decision support application to help the manager to select an appropriate database management software (DBM) for their company, using Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. We observe SQL Server, MySQL, Oracle, DB2, and PostgreSQL as five top database management software and investigate the detail about cost, storage capacity, security, supported the operating system and supported programming language as key criteria to select best database management software from their official website. Then, we combining SAW and TOPSIS method to choose the best appropriate DBM software based on user requirement through computation program and validate our application performance includes the user interface, usability and accuracy result to 50 database engineers expert as respondent. The results are as follows; 1) 86 % of respondents are satisfied with application user interface, 2) 94% are happy with application usability and 3) 86% are pleased with the accuracy of the computation. Overall, this study provides a decision support application to determine an appropriate database management software based on business needs by combining SAW and TOPSIS methods

PEMILIHAN MAHASISWA BERPRESTASI DENGAN METODE SIMPLE ADDITIVE WEIGHTING

Proses seleksi dan rekrutmen di institusi, pengambil keputusan sangat berperan penting untuk mendapatkan hasil seleksi yang baik. Politeknik Negeri Lampung merupakan institusi yang bergerak dalam bidang pendidikan. Proses pemilihan mahasiswa berprestasi di lingkungan Politeknik Negeri Lampung menggunakan proses standard yang dikeluarkan olen Kemeterian Riset Teknologi dan Pendidikan Tinggi. Setiap tahapan dalam proses pemilihan dilakukan sesuai dengan panduan yang telah ditetapkan Kemristekdikti. Penelitian ini bertujuan untuk seleksi mahasiswa berprestasi di perguruan tinggi dengan metode simple additive weightin