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All Journal Building of Informatics, Technology and Science Bulletin of Informatics and Data Science International Journal of Informatics and Data Science
Lubis, Ridha Maya Faza
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Informatics and Data Science

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Decision Support System for Determining New Branch Location Applying the MAUT Method with ROC Weighting Mesran, Mesran; Kusuma, Ade Ayunda; Lubis, Ridha Maya Faza
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.76

Abstract

The new branch location is close to people's activities with the availability of adequate facilities, making it convenient for consumers to access the services/products they need. The determination of the feasibility of a new branch location by several product or service producers still uses an inaccurate system, which can lead to problems in determining a strategic and targeted new branch location. However, there are some challenges in selecting a new branch location, so the utilization of technology is considered efficient, easy, and flexible, widely used by entrepreneurs, especially in determining new branch locations. This is done by using the assistance of a decision support system, which is expected to help determine an efficient and strategic new branch location. The aid comes in the form of a Decision Support System using the MAUT method with ROC weighting. After calculating each criterion and alternative, the best ranking is obtained for alternative A6 with a value of 0.6847. This way, business groups will not have difficulty in determining a new branch location through alternatives and criteria. The use of the MAUT method with ROC weighting is expected to assist in obtaining the best and valid alternatives up to the ranking stage
The Process of Grouping Elementary School Students Receiving PIP Assistance uses the K-Means Algorithm Huang, Jen-Peng; Wang, Pai-Chou; Lubis, Ridha Maya Faza
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.78

Abstract

As part of receiving support from the Smart Indonesia Program (PIP), this study intends to analyze and apply the K-Means algorithm in the process of grouping elementary school students. PIP is a government initiative that attempts to give money to elementary school pupils from disadvantaged or weaker homes. The effective and fair distribution of aid monies depends on the proper grouping of the students. The K-Means approach was selected because it can cluster data, allowing the grouping of pupils based on pertinent traits. Numerous characteristics that can affect kids' financial needs are included in the data utilized in this study, including family income, parental education level, proximity to the school, and other social and economic issues. This study makes use of empirical data from a PIP-affiliated elementary school in an urban setting. The data includes a large number of pertinent features and thousands of pupils. Based on how similar their characteristics are, pupils are divided into numerous clusters using the K-Means technique. The findings of this study will help us better identify the traits of students who are eligible for PIP support. By doing this, the government can allocate funds more wisely and guarantee that aid is given where it is most needed. The PIP program can benefit children in need more by streamlining the process of grouping the students. In addition, this research has broader implications for social aid and education policy. To guarantee effectiveness and equity in resource allocation, the K-Means algorithm can be used in a variety of additional aid initiatives. Data mining-based strategies, like those employed in this study, are becoming more crucial to boost the effectiveness of aid programs like PIP. The findings of this study can help the government and educational institutions improve the efficacy of aid initiatives designed to boost Indonesian children's education
Co-Authors Damanik, Nurafni Elvina, Yuli Hetty Rohayani Huang, Jen-Peng Irvan Siahaan Kiki Khoifin, Kiki Kusuma, Ade Ayunda Kusumaningtyas, Dyah Ayu Mesran, Mesran MULA SIGIRO Nurwahid, Fahri Panjaitan, Joel Sarumaha, Farel Notafelling Simanullang, Ceria D Sitepu, Ade Clinton Wang, Pai-Chou