A Web based Zakat collection and distribution system using K-Nearest Neighbors

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dc.contributor.authorSamatar, Fatuma Abdullahi
dc.date.accessioned2022-10-03T11:02:03Z
dc.date.available2022-10-03T11:02:03Z
dc.date.issued2021
dc.descriptionA Thesis Submitted to the School of Computing and Engineering Science in partial fulfillment of the requirements for the award of Master of Science in Computing and Information Systems Degree.en_US
dc.description.abstractThis research focusses on Zakat and how inefficiency in the process of Zakat collection and distribution impacts poverty. This research studies how the problem of Zakat management is handled in various parts of the world as well as takes a deep look into previous research and proposed solutions in order to come up with a system that attempts to improve efficiency and transparency of the process while building on previous research in the area. The researcher utilized the Agile methodology using a scrum approach to develop the system. The system included a front-facing rule-based calculator to improve the zakat collection process and a machine learning API, built using the K-Nearest Neighbors algorithm, to improve the efficiency of zakat distribution. As such, the model was built using the K- Nearest Neighbors algorithm as it outperformed the other common classification algorithm such as Decision Trees, Naïve Bayes and Support Vector Machine. This process leveraged existing libraries and tools in both the Python and the JavaScript ecosystem. This research concludes that inefficiency in the zakat process could be improved by systemizing the whole process and suggests the developed system as a starting point.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12941
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectZakat en_US
dc.subjectZakat Management Systemen_US
dc.subjectZakat distributionen_US
dc.subjectZakat calculatoren_US
dc.subjectMachine learningen_US
dc.subjectAlgorithmsen_US
dc.titleA Web based Zakat collection and distribution system using K-Nearest Neighborsen_US
dc.typeThesisen_US
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MSc. CIS Theses and Dissertations (2021)