ac

The Comparison of Decision Tree and K-Nearest Neighbor Performance for Determining Mustahik

Authors

  • Noor Amelia Politeknik Negeri Tanah Laut
  • Winda Aprianti Politeknik Negeri Tanah Laut

DOI:

10.47709/brilliance.v3i2.2953

Keywords:

AUC, Confusion Matrix, Decision Tree, KNN, Zakat

Dimension Badge Record



Abstract

The problem of poverty is one of the fundamental issues of concern to the Indonesian government. One of the methods used by Islam to alleviate poverty is through zakat from Badan Amil Zakat Nasional (BAZNAS). Currently, the distribution of zakat is divided into two, namely in the form of consumptive zakat and productive zakat. Productive zakat is aimed at people who need business capital. To assist zakat managers in managing their funds, a mechanism is needed that can process mustahik data so that it can be selected more quickly and precisely using data mining. In this research, the data mining methods that will be used are K-nearest neighbor (KNN) and Decision Tree. The dataset used in this research is data obtained from BAZNAS and has been preprocessed to obtain a dataset with 7 attributes and 144 records. Decision trees, KNN Manhattan, and KNN Euclidean are used to predict mustahik candidates who are worthy of receiving zakat. The performance of the third method was tested using AUC and confusion matrix namely Accuracy, Precision, Recall, and F1 in each dataset split scenario of 70%:30%, 75%:25%, and 80%:20%. Based on the number of false positive and false negative results, the best performance obtained is KNN Euclidean with a dataset division scenario of 80%:20%.

Google Scholar Cite Analysis
Abstract viewed = 143 times

References

(2023, April). Retrieved from Badan Amil Zakat Nasional: http://baznas.go.id

(2023, Januari 16). Retrieved from Badan Pusat Statistik: http://www.bps.go.id

Ahmad, A. A., Saharuna, Z., & Raharjo, M. F. (2020). Pemanfaatan Data Mining dalam Penentuan Rekomendasi Mustahik (Penerima Zakat). Elektron Jurnal Ilmiah, 12(1), 67-73. doi:https://doi.org/10.30630/eji.12.2.182

Amelian, N., Machfiroh, I. S., & Fitriyani, Y. (2020). Analisis Pengaruh Penyaluran Dana Zakat terhadap Perkembangan Usaha Kecil dan Menengah (UKM) Mustahik. Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis, 8(1), 45-51. doi:https://doi.org/10.30871/jaemb.v8i1.1707

Firdaus, B. A. (2021). Penentuan Masyarakat Miskin Penerima Zakat Menggunakan Algoritma K-Nearest Neighbor. JASISFO (Jurnal SIstem Informasi), 2(2), 191-204.

Han, J., Pei, J., & Tong, H. (2022). Data Mining: Concepts and Techniques, Fourth Edition. Cambridge: Morgan Kaufmann.

Hasanah, M., & Mahdiyah, E. (2021). Sistem Informasi Penentuan Penerima Dana Zakat Menggunakan Algoritma C4.5 di BAZNAS Kota Padang Panjang. Riau: Universitas Riau.

Hassonah, M. A., Rodan, A., Al-Tamimi, A.-K., & Alsakran, J. (2019). Churn Prediction: A Comparative Study Using KNN and Decision Trees. 2019 Sixth HCT Information Technology Trends (ITT) (pp. 182-186). Ras Al Khaimah, United Arab Emirates: IEEE. doi:doi: 10.1109/ITT48889.2019.9075077

Kamila, I. (2020). Penerapan Data Mining dan Smarter Model untuk Klasifikasi dan Pendukung Keputusan Penerima Zakat Baznas. Riau: Universitas Islam Negeri Sultan Sayarif Kasim.

Oktafriani, Y., Firmansyah, G., Tjahjono, B., & Widodo, A. M. (2023). Analysis of Data Mining Applications for Determining Credit Eligibility Using Classification Algorithms C4.5, Naïve Bayes, K-NN, and Random Forest. Asian Journal of Social and Humanities, 1(12), 1139-1158. doi:https://doi.org/10.59888/ajosh.v1i12.119

Pathak, A., & Pathak, S. (2020). Study on Decision Tree and KNN Algorithm for Intrusion Detection System. International Journal of Engineering Research & Technology (IJERT), 9(5), 376-381. doi:10.17577/IJERTV9IS050303

Rajaguru, H., & R, S. C. (2019). Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer. Asian Pacific Journal of Cancer Prevention, 20(12), 3777-3781. doi:10.31557/APJCP.2019.20.12.3777

Safradji. (2018). Zakat Konsumtif dan Zakat Produktif. Tafhim Al-'Ilmi Jurnal Pendidikan dan Pemikiran Islam, 59-66.

Sari, Y., Maulida, M., Gunawan, E., & Wahyudi, J. (2021). Artificial Intelligence Approach For BAZNAS Website Using K-Nearest Neighbor (KNN). 2021 Sixth International Conference on Informatics and Computing (ICIC) (pp. 1-4). Jakarta, Indonesia: IEEE. doi:doi: 10.1109/ICIC54025.2021.9632954

Setiadi, T., Parnolo, A., & Prayitno. (2017). Klasifikasi Potensi Zakat di Lazismu DIY Menggunakan Metode K-Nearest Neighbor (K-NN) Berbasis Web Framework. Jurnal Sarjana Teknik Informatika, 5(3), 1-9. doi:http://dx.doi.org/10.12928/jstie.v5i3.12370

Yulianto, L. D., Triayudi, A., & Sholihati, I. D. (2020). Implementation Educational Data Mining For Analysis of Student. Jurnal Mantik, 4(1), 441-451.

Downloads

ARTICLE Published HISTORY

Submitted Date: 2023-10-14
Accepted Date: 2023-10-15
Published Date: 2023-10-23

How to Cite

Amelia, N., & Aprianti, W. (2023). The Comparison of Decision Tree and K-Nearest Neighbor Performance for Determining Mustahik. Brilliance: Research of Artificial Intelligence, 3(2), 81-86. https://doi.org/10.47709/brilliance.v3i2.2953