IMPLEMENTATION OF K-NEAREST NEIGHBOR (K-NN) ALGORITHM IN CLASSIFICATION NUTRITIONAL STATUS OF TODDLERS
Keywords:
Classification, Toddler Nutrition, k-NN, Confusion MatrixAbstract
Toddlers are children under the age of 5 years or 0-60 months. This age is included in a group that is at high risk of disease. Nutritional status in toddlers is an important factor that must be considered because the development of toddlers is very important for their bodies which are still very vulnerable to the name malnutrition.The purpose of this study is to group the nutritional status of toddlers by utilizing the k-NN algorithm and knowing the level of accuracy. The method used in this research is quantitative using the k-NN algorithm. While the evaluation uses Confusion Matrix The results showed an AUC value of 85.1%. Based on these results, it can be concluded that the k-NN algorithm is well used for the classification of toddler nutrition in the future.
References
D. Janner Lubis and G. Karunia Gusti, “Teknois :
Jurnal Ilmiah Teknologi Informasi dan Sains [58]
Penerapan Algoritma Naïve Bayes Untuk
Penentuan Balita Penerima Makanan Tambahan
(PMT) Berdasarkan Status Gizi Di Pos Pelayanan
Terpadu (POSYANDU),” vol. 13, no. 1, pp. 58–66,
, doi: 10.36350/jbs.v13i1.
N. Rahmawati and Y. Novianto, “Klasifikasi Kondisi
Gizi Balita Menggunakan Metode Naive Bayes
(Studi Kasus Posyandu Melati IV),” 2020.
A. Z. Zami, O. Nurdiawan, and G. Dwilestari,
“Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun
Pada Posyandu Melati Dengan Menggunakan
Algoritma Decision Tree,” Jurnal Sistem Komputer
dan Informatika (JSON), vol. 3, no. 3, p. 305, Mar.
, doi: 10.30865/json.v3i3.3892.
H. Hafizan and A. N. Putri, “Penerapan Metode
Klasifikasi Decision Tree Pada Status Gizi Balita Di
Kabupaten Simalungun,” 2020.
R. Rizqi Robbi Arisandi, B. Warsito, and A.
Rachman Hakim, “APLIKASI NAÏVE BAYES
CLASSIFIER (NBC) PADA KLASIFIKASI STATUS
GIZI BALITA STUNTING DENGAN PENGUJIAN KFOLD CROSS VALIDATION,” vol. 11, no. 1, pp.
–139, 2022, [Online]. Available:
https://ejournal3.undip.ac.id/index.php/gaussian/
R. Darma Rusdiyan Yusron and I. Machfud,
“Klasifikasi dan Monitoring Status Gizi Balita Melalui
Penerapan Metode Naïve Bayes Classification
Berbasis GIS Classification and Monitoring of
Toddler Nutrition Status Through Application of GISBased Naïve Bayes Classification Method,” Jurnal
Ilmiah Intech : Information Technology Journal of
UMUS, vol. 4, no. 02, pp. 161–168, 2022.
A. Yoseva Simanjuntak and I. Septian Salomo
Simatupang, “IMPLEMENTASI DATA MINING
MENGGUNAKAN METODE NAÏVE BAYES
CLASSIFIER UNTUK DATA KENAIKAN PANGKAT
DINAS KETENAGAKERJAAN KOTA MEDAN,”
[Online]. Available:
http://jurnal.goretanpena.com/index.php/JSSR
M. Yudhi Putra and D. Ismiyana Putri,
“Pemanfaatan Algoritma Naïve Bayes dan KNearest Neighbor Untuk Klasifikasi Jurusan Siswa
Kelas XI.
G. Abdurrahman, “Jurnal Sistem dan Teknologi
Informasi Klasifikasi Penyakit Diabetes Melitus
Menggunakan Adaboost Classifier,” vol. 7, no. 1,
, [Online]. Available:
http://jurnal.unmuhjember.ac.id/index.php/JUSTIN
DO
M. Ula, R. Maulana, and V. Ilhadi, “Terbit online
pada laman web jurnal:
https://ejurnalunsam.id/index.php/jicom/ Penerapan
KNN Penentuan Pelanggan Baru PDAM dan
Clustering K-Means Berdasarkan Wilayah”,
[Online]. Available:
https://ejurnalunsam.id/index.php/jicom/
A. MUHARIYA, “Pengelompokkan Komentar Pada
Media Sosial Instagram Menggunakan Metode KMeans Clustering Untuk Identifikasi Awal
Cyberbullying,” 2022.
“Baby Nutrition Classification | Kaggle.”
https://www.kaggle.com/datasets/mjalaluddinassuy
uti/baby-nutrition-classification (accessed Apr. 11,
.
J. Astri, J. Karman, and N. K. Daulay, “Prediksi
Kelulusan Mahasiswa Menggunakan Metode KNearest Neigbor (KNN) pada Fakultas Ilmu Teknik,
Univeritas Bina Insan,” vol. 8, pp. 169–173, [Online].
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