Ridha Maya Faza Lubis
Southern Taiwan University of Science and Technology

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Decision Support System for Determining New Branch Locations Applying the Multi Attribute Utility Theory (MAUT) Method Muhammad Zakaria Lubis; Ruziana; Rizkah Fadillah; Ridha Maya Faza Lubis
International Journal of Informatics and Data Science Vol. 1 No. 1 (2023): December 2023
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The location of new branches that are close to community activities and have adequate facilities makes it easier for consumers to get the services and products they need. Determining the feasibility of new branch locations from several product or service producers still uses a system that is not accurate, which can cause problems in determining the location of new strategic and targeted branches. However, there are several obstacles in the selection of new branch locations, so technological assistance is needed in determining the location, product analysis, marketing management, and other matters concerning the development of the business being carried out. Technology that is considered efficient, easy, and flexible and is used by entrepreneurs, especially in determining the location of new branches using a decision support system using the MAUT method, is expected to help the location of new branches that are efficient and strategic. The decision support system is a conclusion and determination of the best using some data and computerized testing in each criterion so as to get valid results. After calculating each criterion and alternative, the best ranking is obtained in alternative A1 with a value of 0.7925 on Pertahanan Street.
Comparative Analysis of MCDM Methods in Employee Award Ranking Roznim binti Mohamad Rasli; Mesran Mesran; Ridha Maya Faza Lubis
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.141

Abstract

Awards are an important form of appreciation for outstanding employees, but the process of selecting recipients is often faced with various challenges, especially in assessing subjective aspects of performance. To overcome this, the Multi-Criteria Decision Making (MCDM) method can be an effective solution. MCDM offers a systematic framework for evaluating various alternatives (in this case, employees) based on a number of relevant criteria. By using the MCDM method, the process of selecting award recipients can be carried out more objectively and transparently. Some commonly used MCDM methods, such as MAUT, OCRA, and CoCoSo, have their own advantages and disadvantages. This study aims to compare the three methods specifically in the context of selecting employee award recipients. The final results obtained show that the best alternative is A6, where the results of the three methods look the same position or location in the ranking. After a comparative analysis of the three methods, it can be concluded that the OCRA method is the best method in terms of ranking consistency compared to the other two methods. Thus, it is hoped that recommendations for the most suitable MCDM method can be obtained to be applied in similar situations
Sistem Pendukung Keputusan Pendataan Warga Penerima Bantuan Raskin dengan Menerapkan Metode Weight Aggregated Sum Product Assesment (WASPAS) Mesran Mesran; Rosmita Sari; Ridha Maya Faza Lubis; Muhammad Syahrizal
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2642

Abstract

Raskin (rice for the poor) is a rice program for the poor. The Raskin program is one of the government's efforts to reduce the burden of expenditure on poor families. However, in practice, decision-making for determining the criteria for rice recipients usually does not refer to the criteria of poor families, resulting in misdirected rice distribution. To address this issue, a decision support system will be developed to assist in the targeted distribution of Raskin using the Weighted Aggregated Sum Product Assessment (WASPAS) method. This research was conducted by finding the weight value for each attribute, then a ranking process was carried out to determine the best alternative. The criteria used were: Type of Employment, Income, House Condition, Family Size, Age. The results of the study recommend that alternative 4, with the highest score of 0.676, be selected to receive Raskin assistance
Sistem Pendukung Keputusan Pemilihan Hotel Terbaik dengan Menggunakan Metode Simple Additive Weighting (SAW) Hardi Giat Sofian Manalu; Safrin Zulkarnain Tarigan; Gunawan Batubara; Mesran Mesran; Ridha Maya Faza Lubis
Bulletin of Artificial Intelligence Vol 4 No 1 (2025): April 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v4i1.228

Abstract

The booming tourism sector offers travellers many accommodation options. However, this diversity can make it difficult to find the right hotel. Therefore, to assist travellers in this difficult decision-making process, a Decision Support System (DSS) for Best Hotel Selection was created. This SPK uses a sophisticated algorithm called ‘method in use’, which can analyse hotel data in multiple dimensions. To make the best hotel recommendation, various factors are considered, including price, location, facilities, guest reviews, and travellers' personal preferences. Travellers get many great benefits from the application of this SPK, including, Time efficiency, Right decision and Better experience. This research uses the SAW method in making a decision in determining the best hotel. Based on the results of research using the SAW method, it can be concluded that this method is very helpful in the process of making hotel selection decisions in Dumai. By giving weight to each criterion that is considered important by users, such as price, location, and facilities, SAW produces a final value for each hotel alternative. Through this calculation, users can easily compare and choose the hotel that best suits their needs. In this study, Hotel C obtained the highest score of 0.850, followed by Hotel B and Hotel G with final results of 0.820 and 0.810 respectively