Menentukan Nilai Gizi pada Balita Menggunakan Algoritma Support Vektor Machine (SVM) di Posyandu Kelurahan Ciherang

Silvia Dini Widianti, Rini Astuti, Fadhil Muhamad Basysyar

Abstract


Determining nutritional status in toddlers is based on age, weight and height. The process is still done manually, resulting in the resulting data being less relevant. This research serves to provide information about determining the nutritional status of toddlers so that the community and officers at Posyandu Ciherang Village. The problem of this study is to determine the growth and development of nutritional status in toddlers at Posyandu Ciherang Village. Data obtained from Posyandu at the village level whose activities are carried out once a month by cadres under the technical guidance of the puskesmas. Based on the existing problems, a system for determining the nutritional status of toddlers is needed to make it easier to get the right results. The method to be used is Support Vector Marchine (SVM) which is a method of classifying data and providing a basis for early preventive action in overcoming nutritional problems in toddlers. The purpose of this study is to determine the nutritional status of toddlers there are 3 criteria needed, namely the age of toddlers, weight and height. The Support Vector Marchine (SVM) algorithm is considered more optimal because it is able to analyze the best results. The results of this study are expected to provide better insight into determining nutritional values in toddlers. Based on the results show True Less (TK) on pred.NORMAL is 31 records classified as malnutrition and True Normal (TN) on pred.NORMAL is 267 records classified as normal nutrition with the smallest result of class recall 76.52% and the smallest result of class precision 76.52%. From these results it can be concluded that the accuracy rate with the Support Vector Marchine (SVM) algorithm is 85.58%.


Keywords


Support Vector Marchine (SVM), Nutritional value, Nutritional status of toddlers, Classification

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References


Adriansyah, I., Mahendra, M. D., Rasywir, E., & Pratama, Y. (2022). Perbandingan Metode Random Forest Classifier dan SVM Pada Klasifikasi Kemampuan Level Beradaptasi Pembelajaran Jarak Jauh Siswa. Bulletin of Informatics and Data Science, 1(2), 98–103. https://ejurnal.pdsi.or.id/index.php/bids/index

Ahmad. (2017). Identifikasi Dan Klasifikasi Kemurnian Susu Sapi Berdasarkan Pemrosesan Sinyal Video Menggunakan Metode Local Binary Pattern ( Lbp ) Dan Learning Vector Quantization ( Lvq ) Identification and Classification Purity of Cow Milk Based on Video Signal Proces. E-Proceeding of Engineering, 4(2), 1758–1765.

Celena, A. K. K., Furqon, M. T., & Ridok, A. (2022). Klasifikasi Berat Badan Lahir Rendah (BBLR) menggunakan Metode Support Vector Machine dengan Teknik SMOTE. Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(7), 3442–3451. http://j-ptiik.ub.ac.id

Fajariati, S., Matulatan, T., & Uperiati, A. (2021). Klasifikasi Status Gizi Terhadap Pertumbuhan Balita Menggunakan Metode Naive Bayes. Student Online Journal (SOJ) UMRAH-Teknik, 2(1), 220–229. https://soj.umrah.ac.id/index.php/SOJFT/article/view/1008%0Ahttps://soj.umrah.ac.id/index.php/SOJFT/article/download/1008/880

Fauzan Adzim, Budianita, E., Nazir, A., & Syafria, F. (2023). Klasifikasi Status Stunting Balita Menggunakan Metode C4.5 Berbasis Web. ZONAsi: Jurnal Sistem Informasi, 5(3), 215–225. https://doi.org/10.31849/zn.v5i3.15828

Hananti, H., & Sari, K. (2021). Perbandingan Metode Support Vector Machine (SVM) dan Artificial Neural Network (ANN) pada Klasifikasi Gizi Balita. Seminar Nasional Official Statistics, 2021(1), 1036–1043. https://doi.org/10.34123/semnasoffstat.v2021i1.1014

Labolo, A. Y., Mooduto, S., Bode, A., & Drajana, I. C. R. (2022). Penerapan Algoritma Spport Vector Machine dan K-Nearest Neighbor Menggunkan Feature Selection Backward Elimination Untuk Prediksi Status Penderita Stunting Pada Balita. Jurnal Tecnoscienza, 6(2), 374–388. https://doi.org/10.51158/tecnoscienza.v6i2.713

Ramon, E., Nazir, A., Novriyanto, N., Yusra, Y., & Oktavia, L. (2022). Klasifikasi Status Gizi Bayi Posyandu Kecamatan Bangun Purba Menggunakan Algoritma Support Vector Machine (Svm). Jurnal Sistem Informasi Dan Informatika (Simika), 5(2), 143–150. https://doi.org/10.47080/simika.v5i2.2185

Setiawan, R., & Triayudi, A. (2022). Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K- Nearest Neighbor Berbasis Web. 6(2), 777–785. https://doi.org/10.30865/mib.v6i2.3566

Sulaehani, R., & Bahrin, B. (2023). Klasifikasi Tingkat Kepuasan Masyarakat Program RTP2S Menggunakan Metode SVM Berbasis Backward Elimination. Jambura Journal of Electrical and Electronics Engineering, 5(1), 115–121. https://doi.org/10.37905/jjeee.v5i1.17204




DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10274

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