ANALISA DATA MINING DALAM MEMPREDIKSI MASYARAKAT KURANG MAMPU MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Nurdin Nurdin

doi:10.23960/jitet.v12i2.4131

Nurdin Nurdin
Universitas Malikussaleh

DOI: https://doi.org/10.23960/jitet.v12i2.4131

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Abstract

Poverty is one of the fundamental issues that is center of attention of the government in a country. One important aspect to support the poverty reduction strategi is the availability of accurate and targeted poverty data. One of the main problems that often hinders the success of these government programs is the availability of appropriate data on the targeting of the poor. This study aims to design an application than can predict the poor using the K-Nearest Neighbor Algorithm with the five main indicators being the type of work, number of dependents, age income and condition of the household head of the family. This prediction provides data on poor families that are suitable for receiving various assistance from the government. The data used for predictions are sample data from Pegasing District. In this study, the K-NN Algorithm was analyzed which was developed based on the web. The working principle of K-Nearest Neighbor is to find the shortest distance between the evaluated data and training data. The results of the evaluation using the confusion matrix obtained the resulting accuracy for 216 training data with 93 testing data with a ratio of 70:30 and five attributes used produced an accuracy of 86,02%, Recall 61,90%, Precision 72,22%, and F1-Score 66,04%.

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References

T. Asril, “Prediction of Students Study Period using K-Nearest Neighbor Algorithm, “ International Journal of Emerging Trends in Engineering Research, vol. 8. No.6, pp. 2585–2593, 2020.https://doi.org/10.30534/ijeter/2020/60862020

F. Kurnia, J. Kurniawan, I. S. Fahmi, I, and S. Monalisa, “Klasifikasi Keluarga Miskin Menggunakan Metode K-Nearest Neighbor Berbasis Euclidean Distance” Vol. 12, 2019.

S. Yani, F. S. Jumeilah, and M. Kadafi, “Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus : Kelurahan Karya Jaya),” J. Inf. Technol. Ampera, vol. 1, no. 2, pp. 75–87, 2020, doi: 10.51519/journalita.volume1.isssue2.year2020.page75-87.

Nurdin, and D. Astika, “Penerapan Data Mining Untuk Menganalisis Penjualan Barang Dengan Menggunakan Metode Apriori Pada Supermarket Sejahtera Lhokseumawe,” Jurnal Techsi., vol. 6, no. 1, pp. 133–155, 2015.

N. Nurdin, M. Suhendri, Y. Afrilia, and R. Rizal, “Klasifikasi Karya Ilmiah (Tugas Akhir) Mahasiswa Menggunakan Metode Naive Bayes Classifier (NBC),” Sistemasi, vol. 10, no. 2, p. 268, 2021, doi: 10.32520/stmsi.v10i2.1193.

Nurdin, E. Susanti, H. Al-Kautsar Aidilof, and D. Priyanto, “Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19,” Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 1, pp. 217–230, 2022.

N. Nurdin, C. C. Pradita, and F. Fadlisyah, “Implementasi Data Mining Untuk Menganalisis Kategori Kompetisi Mahasiswa Menggunakan Algoritma Apriori,” Sisfo: Jurnal Sistem Informasi, vol. 7, no. 2, pp. 28–45, 2023

A. Khairi, A. F. Ghozali, and A. D. N. Hidayah, “Implementasi K-Nearest Neighbor (KNN) untuk Mengklasifikasi Masyarakat Pra-Sejahtera Desa Sapikerep Kecamatan Sukapura,” TRILOGI J. Ilmu Teknol. Kesehatan, dan Hum., vol. 2, no. 3, pp. 319–323, 2021, doi: 10.33650/trilogi.v2i3.2878.

R. K. Dinata, H. Akbar, and N. Hasdyna, “Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus, ” ILKOM Jurnal Ilmiah, vol. 12, no. 2, pp. 104–111, 2020, https://doi.org/10.33096/ilkom.v12i2.539.104-111.

N. Nurdin, Z. Aulia, Risawandi, and L. Rosnita, “Application of the K-Nearest Neighbor Method to Determine Recipients of Non-Cash Food Assistance,” Jurnal Ilmu Komputer, vol. 16, no.2, pp. 115–126, 2023.

M. Qamal, I. Sahputra, N. Nurdin, M. Maryana, and M. Mukarramah, “Sistem Pendukung Keputusan Penentuan Penerimaan Bantuan PKH Menggunakan Metode Naïve Bayes,” TECHSI - J. Tek. Inform., vol. 14, no. 1, pp. 21, 2023, doi: 10.29103/techsi.v14i1.6960.

R. D. Rasyada, N. Nurdin, and F. Fajriana, “Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations,” Sistemasi, vol. 13, no. 1, pp. 83-95, 2024, doi:10.32520/stmsi.v13i1.3161

D. Prasad, S. Kumar Goyal, A. Sharma, A. Bindal, and V. Singh Kushwah, “System model for prediction analytics using / (K-nearest neighbors algorithm. Journal of Computational and Theoretical Nanosceince, vol. 16, no.10, pp. 4425–4430, 2019. https://doi.org/10.1166/jctn.2019.8536

A. Razi, “Klasifikasi Peineirima Beiasiswa Aceih Carong (Aceih Pintar) Di Uiniveirsitas Malikuissaleih Meingguinakan Algoritma Knn (K-Neiareist Neiighbors),” Juirnal Tika, vo. 7, no.1, pp. 79–84, 2022. https://doi.org/10.51179/tika.v7i1.1116

R. L. Hasanah, M. Hasan, W. E. Pangesti, F. F. Wati, and W. Gata, “Klasifikasi Penerima Dana Bantuan Desa Menggunakan Metode Knn (K- Nearest Neighbor)” Jurnal Techno Nusa Mandiri, vol. 16, no. 1, pp. 1–6, 2019, https://doi.org/10.33480/techno.v16i1.25

H. Putri, A. I. Purnamasari, A. R. Dikananda, O. Nurdiawan, and S. Anwar, “Penerima Manfaat Bantuan Non Tunai Kartu Keluarga Sejahtera Menggunakan Metode NAÏVE BAYES dan KNN,” Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 331–337, 2021, doi: 10.47065/bits.v3i3.1093.

E. Firasari, U. Khultsum, M. N. Winnarto, and R. Risnandar, “Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1231, 2020, doi: 10.25126/jtiik.0813087.

A. Khairi, A. F. Ghozali, and A. D. N. Hidayah, “Implementasi K-Nearest Neighbor (KNN) untuk Mengklasifikasi Masyarakat Pra-Sejahtera Desa Sapikerep Kecamatan Sukapura,” TRILOGI J. Ilmu Teknol. Kesehatan, dan Hum., vol. 2, no. 3, pp. 319–323, 2021, doi: 10.33650/trilogi.v2i3.2878.

ANALISA DATA MINING DALAM MEMPREDIKSI MASYARAKAT KURANG MAMPU MENGGUNAKAN METODE K-NEAREST NEIGHBOR

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