Implementation Of The K-Nearest Neighbors (KNN) Algorithm For Malnutrition Prediction
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Abstract
Malnutrition is a serious problem in developing countries, caused by a lack of food intake containing essential substances such as protein and energy. The implementation of machine learning algorithms can provide an accurate diagnosis of malnutrition health conditions in toddlers, facilitating early detection and appropriate interventions. The purpose of this study is to determine the performance of the K-Nearest Neighbors (KNN) algorithm in predicting malnutrition based on clinical characteristics possessed by toddlers. The data used are clinical characteristics of malnutrition sourced from a nutritionist. From the research results, the most optimal accuracy value in predicting malnutrition is 87%. With the existing dataset, it can be proven that the K-Nearest Neighbors (KNN) algorithm is able to classify malnutrition into 2 conditions, namely marasmus and kwashiorkor.
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[3] D. P. Lestari, “Upaya Pencegahan Risiko Gizi Buruk pada Balita: Literature Review,” Jurnal Ilmiah Universitas Batanghari Jambi, vol. 22, no. 1, p. 532, Mar. 2022, doi: 10.33087/jiubj.v22i1.1828.
[4] A. S. R. Sinaga and D. Simanjuntak, “Sistem Pakar Deteksi Gizi Buruk Balita Dengan Metode Naïve Bayes Classifier,” Jurnal Inkofar, vol. 1, no. 2, Jan. 2020, doi: 10.46846/jurnalinkofar.v1i2.110.
[5] dr. Kartika Mayasari, “Berbagai Penyakit Akibat Malnutrisi yang Perlu Diwaspadai,” Klikdokter. Accessed: Sep. 04, 2024. [Online]. Available: https://www.klikdokter.com/ibu-anak/kesehatan-anak/7-penyakit-yang-disebabkan-karena-malnutrisi
[6] V. Viransyah and B. Sugiarto, “Sistem Pakar Diagnosis Penyakit Gizi Buruk Pada Balita Menggunakan Metode Naïve Bayes Berbasis Website,” Digital Transformation Technology, vol. 3, no. 2, pp. 569–576, Nov. 2023, doi: 10.47709/digitech.v3i2.3074.
[7] G. Gimnastiar, Y. I. Syuhardi, and I. Vandini, “Expert System Aplikasi Monitoring dan Pengendalian Gizi Berbasis Android,” JRKT (Jurnal Rekayasa Komputasi Terapan), vol. 1, no. 01, Apr. 2021, doi: 10.30998/jrkt.v1i01.4003.
[8] D. A. Ferliandini and S. Risnanto, “Aplikasi Prediksi Status Gizi Balita Berbasis Web Menggunakan Metode K-Nearest Neighbor,” Prosiding Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik, vol. 5, p. 622, Dec. 2023, doi: 10.32897/sobat.2023.5.0.3136.
[9] S. Lonang, A. Yudhana, and M. K. Biddinika, “Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting,” Jurnal Media Informatika Budidarma, vol. 7, no. 4, p. 2109, Oct. 2023, doi: 10.30865/mib.v7i4.6553.
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[12] M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” SMATIKA JURNAL, vol. 10, no. 02, pp. 71–76, Dec. 2020, doi: 10.32664/smatika.v10i02.455.
[13] S. Lonang and D. Normawati, “Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination,” Jurnal Media Informatika Budidarma, vol. 6, no. 1, p. 49, Jan. 2022, doi: 10.30865/mib.v6i1.3312.
[14] M. M. Bejani and M. Ghatee, “A systematic review on overfitting control in shallow and deep neural networks,” Artif Intell Rev, vol. 54, no. 8, pp. 6391–6438, Dec. 2021, doi: 10.1007/s10462-021-09975-1.
[15] A. Yudhana, S. Sunardi, and A. J. S. Hartanta, “Algoritma K-NN Dengan Euclidean Distance untuk Prediksi Hasil Penggergajian Kayu Sengon,” Transmisi, vol. 22, no. 4, pp. 123–129, Nov. 2020, doi: 10.14710/transmisi.22.4.123-129.
[16] Y. D. Kirana and S. al Faraby, “Sentiment Analysis of Beauty Product Reviews Using the K-Nearest Neighbor (KNN) and TF-IDF Methods with Chi-Square Feature Selection,” 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:245750721
[17] A. Putri et al., “Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 1, pp. 20–26, May 2023, doi: 10.57152/malcom.v3i1.610.
[18] A. R. Wulandari and D. Avianto, “Sistem Pakar Diagnosa Kelainan Stunting Balita Menggunakan Metode KNN Berbasis Web,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 5, no. 1, pp. 1064–1072, Jan. 2024, doi: 10.35870/jimik.v5i1.587.
[19] F. Safitri, R. Taufiq Subagio, and L. Norhan, “Implementasi Metode K-Nearest Neighbor (K-NN) dan Forward Chaining untuk Monitoring Tumbuh Kembang Balita,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 5, pp. 3491–3496, Jan. 2024, doi: 10.36040/jati.v7i5.7464.