NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS

Poverty has many negative effects, for this reason poverty must be alleviated. While the <br /> <br /> resources available to alleviate it still limited. One instrument that is considered potentially to <br /> <br /> alleviate poverty especially in Muslim countries is Zakat....

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Main Author: DEWANGGA WIDHIA (29015018), M.
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/28822
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:28822
spelling id-itb.:288222018-08-13T10:02:20ZNAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS DEWANGGA WIDHIA (29015018), M. Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/28822 Poverty has many negative effects, for this reason poverty must be alleviated. While the <br /> <br /> resources available to alleviate it still limited. One instrument that is considered potentially to <br /> <br /> alleviate poverty especially in Muslim countries is Zakat. Because one of the aims of Zakat is <br /> <br /> to meet the basic needs of the Recipient materially, however there is a big gap between the <br /> <br /> availability and the need of zakat funds. That is why zakat fund needs to be optimized. If the <br /> <br /> Zakat Recipients today develop into a Zakat Payer in the future, then it can reduce the burden <br /> <br /> of zakat funding needs, even it can increase its availability. <br /> <br /> The purpose of this research is to develop a model to predict the most potential Zakat Recipients <br /> <br /> by using the Naïve Bayes classification algorithm. This model is divided into 2 Classes, Not <br /> <br /> Poor and Poor. The model created then validated by Stratified 10-fold cross validation. Equal <br /> <br /> numbers are obtained for all Classes in Classification Accuracy (CA) 75,5% and Area under <br /> <br /> Curve (AUC) 79,7%. While for the Class in average, obtained Precision 75,5% and Recall <br /> <br /> 75,5%. For Not Poor Class, Precision 76,1%, and Recall 71,7%. And for Poor Class, Precision <br /> <br /> 75%, and Recall 79%. <br /> <br /> The author identified the Characteristic of the Patriarch (represented by Gender, Age, and <br /> <br /> Education), Spirituality of the family, Sanitation (represented by Latrines Ownership, Landfills <br /> <br /> Ownership and Water Resources), Savings Ownership, Entrepreneurship represented by <br /> <br /> Business Ownership, and Zakat Programs Obtained (represented by Business, Islamic <br /> <br /> Mentoring and Coaching, Scholarships, and Health) as the variables of Zakat Recipients to find <br /> <br /> knowledge, information, and prediction of potential Zakat Recipients. We hope that this model <br /> <br /> can serve as a preliminary consideration for Zakat Institute, to assess the potential of Zakat <br /> <br /> Recipients. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Poverty has many negative effects, for this reason poverty must be alleviated. While the <br /> <br /> resources available to alleviate it still limited. One instrument that is considered potentially to <br /> <br /> alleviate poverty especially in Muslim countries is Zakat. Because one of the aims of Zakat is <br /> <br /> to meet the basic needs of the Recipient materially, however there is a big gap between the <br /> <br /> availability and the need of zakat funds. That is why zakat fund needs to be optimized. If the <br /> <br /> Zakat Recipients today develop into a Zakat Payer in the future, then it can reduce the burden <br /> <br /> of zakat funding needs, even it can increase its availability. <br /> <br /> The purpose of this research is to develop a model to predict the most potential Zakat Recipients <br /> <br /> by using the Naïve Bayes classification algorithm. This model is divided into 2 Classes, Not <br /> <br /> Poor and Poor. The model created then validated by Stratified 10-fold cross validation. Equal <br /> <br /> numbers are obtained for all Classes in Classification Accuracy (CA) 75,5% and Area under <br /> <br /> Curve (AUC) 79,7%. While for the Class in average, obtained Precision 75,5% and Recall <br /> <br /> 75,5%. For Not Poor Class, Precision 76,1%, and Recall 71,7%. And for Poor Class, Precision <br /> <br /> 75%, and Recall 79%. <br /> <br /> The author identified the Characteristic of the Patriarch (represented by Gender, Age, and <br /> <br /> Education), Spirituality of the family, Sanitation (represented by Latrines Ownership, Landfills <br /> <br /> Ownership and Water Resources), Savings Ownership, Entrepreneurship represented by <br /> <br /> Business Ownership, and Zakat Programs Obtained (represented by Business, Islamic <br /> <br /> Mentoring and Coaching, Scholarships, and Health) as the variables of Zakat Recipients to find <br /> <br /> knowledge, information, and prediction of potential Zakat Recipients. We hope that this model <br /> <br /> can serve as a preliminary consideration for Zakat Institute, to assess the potential of Zakat <br /> <br /> Recipients.
format Theses
author DEWANGGA WIDHIA (29015018), M.
spellingShingle DEWANGGA WIDHIA (29015018), M.
NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS
author_facet DEWANGGA WIDHIA (29015018), M.
author_sort DEWANGGA WIDHIA (29015018), M.
title NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS
title_short NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS
title_full NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS
title_fullStr NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS
title_full_unstemmed NAÏVE BAYES CLASSIFICATION MODEL FOR PREDICTING POTENTIAL ZAKAT RECIPIENTS
title_sort naãƒæ’ã‚âve bayes classification model for predicting potential zakat recipients
url https://digilib.itb.ac.id/gdl/view/28822
_version_ 1822922709500166144