Klasifikasi Metode Naïve Bayes dan K-Nearest Neighbor untuk Menentukan Keluarga Tidak Mampu
Keywords:
Comparison of Classifications, Naïve Bayes, K- Nearest NeighborAbstract
The problem of poverty has a critical role in social life, especially for the government associated with all forms of programs to eradicate poverty. The classification of low-income families also serves as a point to prioritize all forms of assistance in government programs. In these problems, it is quite apparent that the distribution of aid is not well-targeted. In this study, we are looking for for the classification method with the best performance in classifying low-income families. This study limits the classification method to the Naïve Bayes classification method and the k-Nearest Neighbor classification method. The dataset used is more than 800 families spread overtwo labels with 12 parameters, where 30 percent of family data is used as training data, and 70 percent of family data becomes as test data. The test results show that the average accuracy of the Naïve Bayes calcification method is 82.68%, while the K- Nearest Neighbor classification method is 85.57%. This study concludes that the best method for classifying low- income families is the Naïve Bayes method
References
R. B. Akindola, “Towards a definition of poverty: Poor people’s perspectives and implications for poverty reduction,” Journal of Developing Societies, vol. 25, no. 2, pp. 121–150, Apr 2009, doi: 10.1177/0169796X0902500201.
“Badan Pusat Statistik.” https://www.bps.go.id/pressrelease/2020/01/ 15/1743 /persentase-penduduk-miskin-september- 2019- turun-menjadi-9-22-persen.html (accessed May 02,2020).
A. Mahdi, A. Razali, and A. Alwakil, “Comparison of Fuzzy Diagnosis with K- Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis.”
Manav Rachna International University. Faculty of Engineering and Technology. Department of Computer Science and Engineering and Institute of Electrical and Electronics Engineers, Proceedings of the 2014 International Conference on Reliability, Optimization & Information Technology?: ICROIT 2014?: 6-8 February 2014.
S. D. Jadhav and H. P. Channe, “Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques,” 2013. [Online]. Available: www.ijsr.net.
W. C. Org and S. S. Nikam, “): Pgs. 13-19 An International Open Free Access,”Peer Reviewed Research Journal Published By, vol. 8, no. 1, 2015, [Online]. Available: www.computerscijournal.org.
J. Han, M. Kamber, and J. Pei, “Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems),” 2011.
T. R. Patil and M. S. S. Sherekar, “Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification,” International Journal Of Computer Science And Applications, vol.6, no. 2,2013,[Online]. Available: http://www.cs.bme.hu/~kiskat/adatb/bank- data-.
R. Arian, A. Hariri, A. Mehridehnavi, A. Fassihi, and F. Ghasemi, “Protein Kinase Inhibitors’ Classification Using K-Nearest Neighbor Algorithm,” Computational Biology and Chemistry, p. 107269, Apr. 2020, doi: 10.1016/j.compbiolchem.2020.107269.
Z. E. Rasjid and R. Setiawan, “Performance Comparison and Optimization of Text Document Classification using k-NN and Naïve Bayes Classification Techniques,” in Procedia Computer Science, 2017, vol. 116, pp.107–112,doi: 10.1016/j.procs.2017.10.017.
G. Gunadi, D. I. Sensuse “ Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori dan Frequent Patern Growth (FP- Growth): Studi Kasus Percetakan PT.Gramedia “, 2012.
A. A. Fajrin, A. Maulana “ Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma FP- Growth Pada Data Transaksi Penjualan Spare Part Motor “, 2018.
W. D. Septiani “ Komparasi Metode Klasifikasi Data Mining Algoritma C4.5 Dan Naïve Bayes Untuk Prediksi Penyakit Hepatitis “, 2017.
Maharani, N. A. Hasibuan, N. Silalahi, S. D. Nasution, Mesran, Suginam, D. U. Sutiksno,
H. Nurdiyanto, E. Buulolo, Yuhandri “ Implementasi Data Mining Untuk Pengaturan Layout Minimarket Dengan Menerapkan Association Rule “, 2017.
Mrs. R. Sumithra, MCA, M.Phil., (Ph.D.), Dr (Mrs). S. Paul MCA, M.Phil, Ph.D. “Using distributed apriori association rule and classical apriori mining algorithms for grid based knowledge discovery “, 2010.
Shashi Kant Shankar, Amritpal Kaur “Constraint Data Mining using Apriori Algorithm with And Operation “, 2016.
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