Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination
DOI:
https://doi.org/10.30865/mib.v6i1.3312Keywords:
Classification, Stunting, K-Nearest Neighbor, Backward Elimination, Data MiningAbstract
The main problem regarding nutrition faced by Indonesia is stunting, where Indonesia is ranked fifth in the world with the largest stunting prevalence rate in 2017, which is 29.6% of all Indonesian children. Stunting is a child under five years who has a z-score value of less than -3 standard deviations (SD). Stunting has a negative impact, namely it can disrupt the physical and intellectual development of toddlers in the future. In this case, the examination of stunting status by medical personnel is still carried out manually which takes a long time and is prone to inaccuracies. This study aims to classify stunting status in toddlers by applying the K-Nearest Neighbor method using the Backward Elimination feature selection to get fast and accurate results. Based on the results of this study, the average accuracy produced by the K-Nearest Neighbor algorithm at k=5 is 91.90% with 9 attributes and the average accuracy produced by the K-Nearest Neighbor algorithm with the addition of Backward Elimination is 92.20%. with 8 attributes. These results indicate that the application of Backward Elimination can increase the accuracy value of the K-Nearest Neighbor algorithm and also perform attribute selection.References
Kementerian Kesehatan Republik Indonesia, “Buletin Jendela Data dan Informasi Kesehatan: Situasi Balita Pendek (Stunting) di Indonesia,†Kementeri. Kesehat. RI, vol. 301, no. 5, pp. 1163–1178, 2018.
TNP2K, 100 Kabupaten/Kota Prioritas untuk Intervensi Anak Kerdil (Stunting): Tim Nasional Percepatan Penanggulangan Kemiskinan, vol. 2, no. c. 2017.
A. Rahmawati, T. Nurmawati, and L. Permata Sari, “Faktor yang Berhubungan dengan Pengetahuan Orang Tua tentang Stunting pada Balita,†J. Ners dan Kebidanan (Journal Ners Midwifery), vol. 6, no. 3, pp. 389–395, 2019.
L. Andiani, S. Sukemi, and D. P. Rini, “Analisis Penyakit Jantung Menggunakan Metode KNN Dan Random Forest,†in Annual Research Seminar (ARS), 2020, vol. 5, no. 1, pp. 165–169.
A. D. Ghani, N. Salman, and Mustikasari, “Algoritma k-Nearest Neighbor Berbasis Backward Elimination Pada Client Telemarketing,†in Prosiding Seminar Ilmiah Sistem Informasi dan Teknologi Informasi, 2019, vol. 8, no. 2, pp. 141–150.
A. Bode, “K-Nearest Neighbor Dengan Feature Selection Menggunakan Backward Elimination Untuk Prediksi Harga Komoditi Kopi Arabika,†Ilk. J. Ilm., vol. 9, no. 2, pp. 188–195, 2017.
A. Byna and N. Anisa, “Backward Elimination Untuk Meningkatkan Akurasi Kejadian Stunting Dengan Analisis Algortima Support Vector Machine,†Din. Kesehat. J. Kebidanan Dan Keperawatan, vol. 9, no. 2, pp. 217–225, 2018.
H. Harafani and H. A. Al-Kautsar, “Meningkatkan Kinerja K-Nn Untuk Klasifikasi Kanker Payudara Dengan Forward Selection,†J. Pendidik. Teknol. dan Kejuru., vol. 18, no. 1, p. 99, 2021.
T. Prasetiya, I. Ali, C. L. Rohmat, and O. Nurdiawan, “Klasifikasi Status Stunting Balita Di Desa Slangit Menggunakan Metode K-Nearest Neighbor,†Informatics Educ. Prof., vol. 4, no. 2, pp. 93–104, 2020.
J. Zeniarja, K. Widia, and R. R. Sani, “Penerapan Algoritma Naive Bayes dan Forward Selection dalam Pengklasifikasian Status Gizi Stunting pada Puskesmas Pandanaran Semarang,†JOINS (Journal Inf. Syst., vol. 5, no. 1, pp. 1–9, 2020.
Y. A. PRATAMA, “ANALISIS METODE SELEKSI FITUR UNTUK MENINGKATKAN AKURASI PADA VARIANT METODE KLASIFIKASI K-NEAREST NEIGHBOR (kNN),†pp. 7–37, 2019.
I. A. Nikmatun and I. Waspada, “Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,†Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 10, no. 2, pp. 421–432, 2019.
X. Zhang, Z. Shi, X. Liu, and X. Li, “A hybrid feature selection algorithm for classification unbalanced data processsing,†Proc. - 2018 IEEE Int. Conf. Smart Internet Things, SmartIoT 2018, pp. 269–275, 2018.
P. Haryandi, “Implementasi Seleksi Fitur dengan Backward Elimination untuk Klasifikasi Prediksi Perceraian,†Semin. Nas. Mhs. Ilmu Komput. dan Apl., 2021.
A. Bode, “Seleksi Fitur Untuk Prediksi Rating Film Hollywood Menggunakan Model K-Nearest Neighbor,†Jupiter, vol. 5, no. 1, pp. 1–8, 2019.
S. Srinidhi, “Backward Elimination for Feature Selection in Machine Learning,†Towar. Data Sci., 2019.
S. R. Raysyah, Veri Arinal, and Dadang Iskandar Mulyana, “Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode Knn Dan Pca,†JSiI (Jurnal Sist. Informasi), vol. 8, no. 2, pp. 88–95, 2021.
A. Ariani and S. Samsuryadi, “Klasifikasi Penyakit Ginjal Kronis menggunakan K-Nearest Neighbor,†in Annual Research Seminar (ARS), 2020, vol. 5, no. 1, pp. 148–151.
F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive Bayes pada Penentuan Status Gunung Berapi dengan K-Fold Cross Validation,†J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, p. 577, 2018.
D. Normawati and D. P. Ismi, “K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining,†Signal Image Process. Lett., vol. 1, no. 2, pp. 23–35, 2019.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
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