Klasifikasi Penerima Zakat Berdasarkan Ciri Dominan Menggunakan Learning Vector Quantization

Nurfaizi, Randika (2019) Klasifikasi Penerima Zakat Berdasarkan Ciri Dominan Menggunakan Learning Vector Quantization. Tugas Akhir thesis, University of Technology Yogyakarta.

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Abstract

Excess assets in Islam are obliged to be issued as part of zakat. There are 8 groups of recipients of zakat, namely Fakir, Poor, Amil, Riqab, Ghorimin, Sabilillah, and Ibn Sabil. In determining whether the recipient of the zakat has the right or not to receive zakat, it is necessary to know from several criteria. A system is needed to classify the recipient of zakat. The Learning Vector Quantization (LVQ) method is one method for conducting learning or training in supervised, competitive exercises. The results obtained from the competitive layer depend on the distance between input vectors. System testing in this study can be done by classification of zakat recipients. The results of the Zakat recipient classification accuracy score with the provisions of 6 attributes (percentage, assessment, dependents, home conditions, education and health support) show the percentage of truth level of 62,857%, while the results of zakat recipient trials with the provisions of 2 attributes (age and yield) show that the percentage of the level of truth is 64,286%. Based on the results of the trial, the best truth value is in the second model, which is 64,286%. Keywords: classification, zakat recipient, artificial neural network, vector quantization learning

Item Type: Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Teknologi Informasi dan Elektro > S1 Informatika
Depositing User: Kaprodi S1 Informatika UTY
Date Deposited: 01 Apr 2019 00:45
Last Modified: 01 Apr 2019 00:45
URI: http://eprints.uty.ac.id/id/eprint/2643

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