Zakat Classification with Naïve Bayes Method in BAZNAS

Yuslena Sari, Muhammad Alkaff, Eka Setya Wijaya, Gusti Nizar Syafi'i


The National Amil Zakat Agency (BAZNAS) of the Banjar Regency, is the regional Zakat Management Agency of the Banjar Regency. BAZNAS Banjar Regency distributes the required alms according to the target to mustahik that is under the criteria or following the provisions of the Shari'a. However, BAZNAS often experiences difficulties in determining mustahik (people who are entitled to receive zakat) due to limited distribution funds and excessive data on Fakir and miskin people who are the main priority. The existence of a system that can determine two groups of recipients of the Fakir and miskin zakat based on data from the underprivileged population can help the distribution of zakat to these 2 groups. In this case, using the Naive Bayes method is very suitable in the classification of the BAZNAS mustahik determination so that it can be used to determine the prospective recipient of zakat. Based on the results of tests conducted on the Naïve Bayes classification with the Confusion Matrix calculation, the accuracy value reached 92.30%.


BAZNAS; mustahik; Fakir; miskin; naïve bayes

Full Text:



Abiodun, Oludare Isaac et al. 2018. “State-of-the-Art in Artificial Neural Network Applications: A Survey.” Heliyon 4(11): e00938.


Asa, Ringga Sentagi. 2019. “Identifikasi Penyaluran Zakat Menggunakan Algoritma C4.5 (Studi Kasus Di BAZNAS Kabupaten Agam).” Jurnal Sains dan Informatika.

Bakar, Mahyudin Haji Abu. 2011. “Towards Achieving the Quality of Life in the Management of Zakat Distribution to the Rightful Recipients ( The Poor and Needy ).” International Journal of Business and Social Science 2(4): 237–45.

Besimi, Nuhi, Betim Çiço, and Adrian Besimi. 2017. “Overview of Data Mining Classification Techniques: Traditional vs. Parallel/Distributed Programming Models.” 2017 6th Mediterranean Conference on Embedded Computing, MECO 2017 - Including ECYPS 2017, Proceedings (June): 2–5.

Cilimkovic, Mirza. 2010. “Neural Networks and Back Propagation Algorithm.” Fett.Tu-Sofia.Bg. BOOK 1/Circuits and Systems/173 Paper-V_Skorpil.pdf.

Dangi, Abhilasha, and Sumit Srivastava. 2015. “Educational Data Classification Using Selective Naïve Bayes for Quota Categorization.” Proceedings of the 2014 IEEE International Conference on MOOCs, Innovation and Technology in Education, IEEE MITE 2014: 118–21.

Fairi, Maulana Ihsan. 2020. “Comparative Study in Zakat Management between Pusat Zakat Sabah and Badan Amil Zakat DIY.” Journal of Islamic Economics Lariba 6: 63–88.

Farid, Dewan Md et al. 2014. “Hybrid Decision Tree and Naïve Bayes Classifiers for Multi-Class Classification Tasks.” Expert Systems with Applications.

Jalota, Chitra, and Rashmi Agrawal. 2019. “Analysis of Educational Data Mining Using Classification.” Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019: 243–47.

Kapita, Syarifuddin N, Samlan Mahdi, and Firman Tempola. 2020. “Penilaian Pengetahuan Siswa Dengan Jaringan Syaraf Tiruan Algoritma Perceptron.” TECHNO: JURNAL PENELITIAN (09): 372–81.

Katarya, Rahul, Vivek Gangwar, and Ishita Jaisia. 2018. “A Study on Different Data Mining Classifiers.” 2018 International Conference on Computer Communication and Informatics, ICCCI 2018: 1–6.

Lee, Dongwoo, Sybil Derrible, and Francisco Camara Pereira. 2018. “Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling.” Transportation Research Record.

Lefebvre-Ulrikson, Williams, G. Da Costa, L. Rigutti, and I. Blum. 2016. “Data Mining.” In Atom Probe Tomography: Put Theory Into Practice,.

Mustakim et al. 2018. “Algorithm Comparison of Naive Bayes Classifier and Probabilistic Neural Network for Water Area Classification of Fishing Vessel in Indonesia.” Journal of Theoretical and Applied Information Technology 96(13): 4114–25.

Ren, Jiangtao et al. 2009. “Naive Bayes Classification of Uncertain Data.” Proceedings - IEEE International Conference on Data Mining, ICDM (60703110): 944–49.

Tajbakhsh, Nima, and Kenji Suzuki. 2017. “Comparing Two Classes of End-to-End Machine-Learning Models in Lung Nodule Detection and Classification: MTANNs vs. CNNs.” Pattern Recognition 63: 476–86.

Ul Hassan, Ch Anwar, Muhammad Sufyan Khan, and Munam Ali Shah. 2018. “Comparison of Machine Learning Algorithms in Data Classification.” ICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing (September): 1–6.

Wu, Jia et al. 2015. “Self-Adaptive Attribute Weighting for Naive Bayes Classification.” Expert Systems with Applications.

Xhemali, Daniela, Christopher J Hinde, and Roger G Stone. 2009. “Naïve Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages.” IJCSI International Journal of Computer Science.

Zaenal, Muhammad Hasbi et al. 2016. “Principles of Amil Zakat and Best Practice Recommendations for Zakat Institutions.” (December): 1–16.

Zhang, Wei, and Feng Gao. 2013. “Performance Analysis and Improvement of Naïve Bayes in Text Classification Application.” 2013 IEEE Conference Anthology, ANTHOLOGY 2013: 1–4.



  • There are currently no refbacks.

Copyright (c) 2021 yuslena sari, Muhammad Alkaff, Eka Setya Wijaya, Gusti Nizar Syafi'i

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.



TECHNO: Jurnal Penelitian

ISSN 1978-6107 (Print)

ISSN 2580-7129 (Elektronik)

Published by: LPPM Universitas Khairun

Jalan Yusuf Abdurrahman Kampus II Unkhair, Kelurahan Gambesi, 97722 Kecamatan Kota Ternate Selatan, Provinsi Maluku Utara




Creative Commons License
Techno Jurnal Penelitian is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License.