Klasifikasi Data Penerimaan Zakat dengan Algoritma K-Nearest Neighbor
DOI:
https://doi.org/10.30865/klik.v4i3.1528Keywords:
BAZNAS; Data Mining; Classification; K-Nearest NeighbourAbstract
National Amil Zakat Agency (BAZNAS) is an institution responsible for managing zakat established by the government. BAZNAS has a presence in every district or city, and one of them is the BAZNAS in the city of Pekanbaru. BAZNAS in Pekanbaru city is responsible for distributing zakat to various empowerment programs, one of which is the Pekanbaru Cares program. Currently, BAZNAS in Pekanbaru city is facing issues related to the method of distributing zakat, where the process of determining the criteria for zakat recipients is still being done manually by the committee of BAZNAS in the city of Pekanbaru. This condition is considered inefficient and poses one of the challenges that need to be addressed. To overcome the mentioned constraints, steps are needed to improve the effectiveness and efficiency of data collection for potential zakat recipients. One of the solutions is to implement a classification system to facilitate the data collection process, using the K-Nearest Neighbor (KNN) method. This approach functions as a tool to classify data for potential beneficiaries. This research aims to classify data and measure the accuracy in assessing the eligibility of zakat recipients based on predetermined criteria, utilizing the K-Nearest Neighbor (K-NN) algorithm. A total of 602 data from BAZNAS in the city of Pekanbaru were used in this study, by dividing the training and test data, namely divided 90:10, 80:20, and 70:30 splits. The evaluation results from the confusion matrix of k=3, k=5, k=7, k=9, and k=11 show that the highest accuracy is achieved at k=5 with an 80:20 split, with an accuracy rate of 89.3%. Furthermore, a precision of 87.3% and a recall of 91.4% can also be attained through this approach.
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