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Dito Putro Utomo
Politeknik Negeri Medan, Medan
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Journal : Journal of Decision Support System Research

 1 
Penerapan Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) dalam Penentuan Bantuan Siswa Miskin (BSM) pada SMK Mesran, Mesran; Nastiti, Sindy; Sussolaikah, Kelik; Saputra, Imam; Utomo, Dito Putro
Journal of Decision Support System Research Vol. 2 No. 1 (2024): September 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/dss.v2i1.66

Abstract

The BSM Program is a National Program that aims to eliminate barriers for poor students to participate by helping poor students obtain decent educational service requests, preventing dropouts, attracting poor students to return to school, helping students meet their needs in learning activities, supporting the 9-year compulsory education program (even up to high school level), and helping the smooth running of school programs. To obtain the Poor Student Assistance (BSM) funds, the government has set several criteria for who are the students who can be determined and are entitled to receive the Poor Student Assistance (BSM). These criteria will later help schools or educational institutions determine which students can be determined to receive the Poor Student Assistance Fund. Therefore, an educational institution must have a Decision Support System for recommending Poor Student Funds (BSM) using the Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) method, with the existence of a decision support system for determining Poor Student Assistance (BSM) it is hoped that the determination process will run well, be on target, and be received by those who are entitled. The final result determines that Alternative A1 is ranked first with the highest score of 22.2679. Thus, decision makers can compare the performance between the old system and the BSM fund determination decision support system using the Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA) method without having to re-request data on students who will be given Poor Student Assistance funds.
Co-Authors A M Hatuaon Sihite Abdul Karim Ade Ambarwati Br Ginting Aminuddin Aziz Annisa Apriliani Annisa Fadillah Siregar Annisah Annisah Asprina Br Surbakti, Asprina Br Atira Nabila Azlan, Azlan Bernadus Gunawan Sudarsono Bister Purba Boby Septia Pranata Butar Butar, Roi Martin Cici Alfiani Pradika Dita Dewi Maulida Sari Tanjung Dwi Asdini Efori Buulolo Eka Pratiwi Sumantri Faisal Amir Feby Ronauli Lubis, Eka Fince Tinus Waruwu Firman Telaumbanua Ginting, Winda Widia Br Guidio Leonarde Ginting Guidio Leonarde Ginting Hasibuan, Nelly Astuty Hendrikus Daely Ida Rizky Nasution Ihsan Ihsan Ilham Mubarik Ilham, Safarul Imam Saputra Imam Saputra Indini, Dwina Pri Irfan Nainggolan Iskandar Zulkarnain Johanes Mario Purba Keke Annisa Siregar Kurnia Ulfa M Mesran Manik, Lastri Meiliyani Br Ginting, Meiliyani Br Mesran, Mesran Miftahul Khairat Miko Putra Haposan Tinambunan Muhammad Syahrizal Murdani Murdani, Murdani Nainggolan, Dian Wichita Nainggolan, Laksono Nasib Marbun Nasib Sihombing Nastiti, Sindy Nelly Astuti Hasibuan Nona Oktari Noveriang Ndruru Novida Sari, Sri Nurjannah Oktari, Nona Pitriani Piliang Purba, Andrean Saputra Purba, Bister Purba, Roulina Agape Radius Kharisman Ndruru RAHELIYA BR GINTING Raheliya Br Ginting, Raheliya Br Rahmi Danur, Surizar Rama Prameswara Ritonga Refika Ratna Dilla Rian Syahputra Rivalri Kristianto Hondro Rizqi Dwikunti Siregar, Dini Roni Yunis Russy Amelia Samueal Damanik Santri W Pasaribu Saragi, Naomi Labora Saragih, Soumi Rohmah Sarumaha, Lukas Sarwandi Wandi Sawitri Sawitri Selly Armasari Sihotang, Dahner Ismanda Bertenius Simatupang, Meylita Putri Sirait, Pahala Siregar, Tesa Aurelia Siswahyudianto Sitepu, Harun Rivaldo Soeb Aripin Suginam Suharti Suharti Sulistianingsih, Indri Surya Darma Nasution Susi Mardiana Giawa Sussolaikah, Kelik Tesa Aurelia Siregar Ulva Rizky Amanda Virdyra Tasril Yohana Br Ginting, Dewi Zahri Hubby Ramadhani