Analisis Pengaruh Faktor Kemiskinan Terhadap Tingkat Kesehatan Dan Gaya Hidup Masyarakat Desa Suralaga, Lombok Timur, Menggunakan Algoritma Support Vector Machine (SVM)

Muhammad Wasil, Mahpuz Mahpuz

Abstract


The level of poverty in Indonesia is currently an important task for the government, both in cities and in villages. As time goes by and the nation's economy is arguably unstable, the poverty level of the community cannot be controlled properly. Especially for rural areas or remote and remote villages, such as in the province of NTB, East Lombok Regency, especially in Suralaga Village. The people in Suralaga Village, who generally only have income from farming and raising livestock, are unable to meet their daily needs, which are increasingly soaring. Not only to meet economic needs, they may find it difficult to fulfill their educational needs. The results of farming or raising them, which has a grace period from planting to harvest, require a lot of money. Therefore, the income they have is not stable. This instability causes the economy of the community in the village to be classified as middle to lower class. To find out the extent of the influence of the poverty factor on the level of health, an analysis of the influence of the poverty factor on the health level and lifestyle of the people of Suralaga Village, East Lombok was carried out using the Support Vector Machine (SVM) algorithm. The experimental results shown, have concluded that data processing to determine the purpose of this study, using the Support Vector Machine algorithm, poverty to the health level of the Suralaga Village community is very large and provides an illustration that the average Suralaga Village community is included in the category of people who do not pay attention to the elements. health with an accuracy rate of 73.77%, when viewed based on the distribution of data used

Keywords


Poverty; Health; SVM; Accuracy

Full Text:

PDF

References


S. Niell et al., “Beehives biomonitor pesticides in agroecosystems: Simple chemical and biological indicators evaluation using Support Vector Machines (SVM),” Ecol. Indic., vol. 91, no. January, pp. 149–154, 2018, doi: 10.1016/j.ecolind.2018.03.028.

W. Xiong, J. Xu, Z. Xiong, J. Wang, and M. Liu, “Degraded historical document image binarization using local features and support vector machine (SVM),” Optik (Stuttg)., vol. 164, pp. 218–223, 2018, doi: 10.1016/j.ijleo.2018.02.072.

X. Sui, K. Wan, and Y. Zhang, “Pattern recognition of SEMG based on wavelet packet transform and improved SVM,” Optik (Stuttg)., vol. 176, no. July 2018, pp. 228–235, 2019, doi: 10.1016/j.ijleo.2018.09.040.

H. Sun, G. Lv, J. Mo, X. Lv, G. Du, and Y. Liu, “Application of KPCA combined with SVM in Raman spectral discrimination,” Optik (Stuttg)., vol. 184, no. January, pp. 214–219, 2019, doi: 10.1016/j.ijleo.2019.02.126.

mahpuz Yahya, “Penggunaan Algoritma K-Means Untuk Menganalisis Pelanggan Potensial Pada Dealer SPS Motor Honda Lombok Timur Nusa Tenggara Barat,” Infotek, vol. 2, no. 2, p. 373426, 2019.

reni zuliana Yahya, “Prediksi Jumlah Penggunaan BBM Perbulan Menggunakan Algoritma Decition Tree (C4.5) Pada Kantor Dinas Lingkungan Hidup dan Kebersihan Kecamatan Selong Kabupaten Lombok Timur,” vol. 1, no. 1, pp. 430–439, 2018.

Y. H. Hui et al., “PENERAPAN ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI JUMLAH PRODUKSI BARANG BERDASARKAN DATA PERSEDIAAN DAN JUMLAH PEMESANAN PADA CV. PAPADAN MAMA PASTRIES. Volume 1.,” J. Mantik Penusa, vol. 1, no. 2, pp. 16–21, 2017, [Online]. Available: https://ezp.lib.unimelb.edu.au/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ffh&AN=2008-10-Aa4022&site=eds-live&scope=site.

muhammad wasil mahpuz, yahya, “Implementasi Algoritma Decision Tree Untuk Mengetahui Faktor Kredit Macet Dan Lancar di Koperasi Serba Usaha Daruzzakah Rensing Lombok Timur,” Infotek, vol. 3, no. 2, pp. 9–20, 2020.

W. P. H. Yahya, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Efektivitas Penjualan Vape ( Rokok Elektrik ) pada ‘ Lombok Vape On ’ Pendahuluan dihasilkan tidak stabil dan tidak mampu diprediksi Dari penelitian yang dilakukan , berusaha untuk mengklasifikasika,” Infotek, vol. 3, no. 2, pp. 21–31, 2020.

Y. A. Setianto, K. Kusrini, and H. Henderi, “Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur,” Creat. Inf. Technol. J., vol. 5, no. 3, p. 232, 2019, doi: 10.24076/citec.2018v5i3.179.


Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Infotek : Jurnal Informatika dan Teknologi

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

View My Stats