RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN PENERIMA BANTUAN COVID-19 JARING PENGAMAN SOSIAL MENGGUNAKAN METODE MULTI-ATTRIBUTE BORDER APPROXIMATION AREA COMPARISON (MABAC) DI KELURAHAN AIR RAJA KOTA TANJUNGPINANG BERBASIS WEB

Septira Nurul Hidayah, NIM.: 17106050036 (2022) RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN PENERIMA BANTUAN COVID-19 JARING PENGAMAN SOSIAL MENGGUNAKAN METODE MULTI-ATTRIBUTE BORDER APPROXIMATION AREA COMPARISON (MABAC) DI KELURAHAN AIR RAJA KOTA TANJUNGPINANG BERBASIS WEB. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The spread of COVID-19 in the world causes material losses that affect social, economic and community welfare aspects. The economic impacts caused by the pandemic include layoffs and price increases. In order to ease the economic burden of the community, the government provides social assistance to reduce daily costs by providing social assistance in the form of basic food assistance to the community. However, the government's efforts are deemed not optimal, manual data collection of prospective recipients can also lead to repetition of recipient data without the officer realizing it. So that repetition or overlapping of data includes problems that occur in this COVID-19 social assistance. To overcome this, we need a system that can assist the decision-making process. This system will select beneficiaries who deserve assistance, according to the Multi-Attribute Border Approximation Area Comparison (MABAC) method. This method was chosen because it provides a stable (consistent) and reliable solution for rational decision making based on predetermined criteria. There are 6 criteria used in this study, namely PKH, DTKS, occupation, home condition, address, and KK nunbers. The result of this study is that a decision support system by applying the MABAC method can produce recommendations for COVID-19 beneficiaries who are eligible for assistance. From the results of functionality testing, about 100% of respondents agree with the system, while the results of interface testing show that 82.5% of testers strongly agree that the system interface is easy to understand, and 17.5% of testers agree that the system interface is easy to u

Item Type: Thesis (Skripsi)
Additional Information: Pembimbing: Ir. Maria Ulfah Siregar, S.Kom., MIT., Ph.D
Uncontrolled Keywords: COVID-19, Penerima Bantuan, Sistem Pendukung Keputusan, Multi-Attribute Border Approximation Area Comparison (MABAC)
Subjects: Sistem Informasi
Covid-19
Divisions: Fakultas Sains dan Teknologi > Teknik Informatika (S1)
Depositing User: Muh Khabib, SIP.
Date Deposited: 22 Feb 2022 11:23
Last Modified: 22 Feb 2022 11:23
URI: http://digilib.uin-suka.ac.id/id/eprint/49442

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