Classification of Big Data Stunting in North Sumatra Using Support Vector Regression Method

Maradona Jonas Simanullang, Rika Rosnelly, Bob Subhan Riza

Abstract


Stunting in children is a serious issue in society, especially in areas with high levels of malnutrition like North Sumatra. Therefore, it is important to develop an effective approach to identify the factors contributing to stunting and predict its risks in children, considering the high prevalence of stunting in this region. The high rate of stunting in North Sumatra indicates the urgency of this problem, making research on Big Data classification using Support Vector Regression (SVR) methods highly important. This study aims to offer profound understanding into factors influencing stunting in the region, thus enabling the development of more effective and targeted intervention strategies. The objective of this research is to categorize Big Data related to stunting in North Sumatra using SVR methods, taking into account factors such as wasting and malnutrition. The main focus of this research is to identify patterns related to stunting, predict the risk of stunting in children, and design more effective intervention strategies while addressing the issues of wasting and malnutrition. The research process encompasses several steps including data collection, pre-processing to handle missing values and outliers, normalization, and the application of Support Vector Regression (SVR). The final outcomes were achieved using a Voting Classifier that integrates Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), resulting in an accuracy rate of 91.78%. This method effectively pinpoints the main factors contributing to stunting, which supports clinical decision-making and intervention strategies. The study highlights the potential of big data and machine learning in the healthcare sector, offering a model for enhancing health services and tracking children’s health conditions.


Keywords


Classification; Malnutrition; Stunting; Support Vector Regression; Wasting

References


Jalil, N. Childhood Stunting: Analysis Affecting Childrens Stunting In Sulawesi. Systematic Reviews in Pharmacy. 2021 Jan 1;12(3):505–11.G. Veruggio, “The EURON Roboethics Roadmap,” in Proceeding of Humanoids ’06: 6th IEEE-RAS International Conference on Humanoid Robots, 2006, pp. 612–617, doi: 10.1109/ICHR.2006.321337

Tri Siswati, Bunga Astria Paramashanti, Pramestuti N, Waris L. A POOLED DATA ANALYSIS TO DETERMINE RISK FACTORS OF CHILDHOOD STUNTING IN INDONESIA. Journal of Nutrition College. 2023 Mar 23;12(1):42–52.

Sutarto Sutarto, None Naza Tsasbita Hayuning Adila, Sari, None Reni Indriyani. Analisa Komplikasi Penyakit Infeksi Dan Riwayat Berat-Panjang Badan Saat Lahir Pada Kejadian Stunting Balita Di Indonesia. Jurnal Niara. 2023 May 25;16(1):149–66.

Rizki Hardinata, Lisda Oktaviana, Farah Fadhilah Husain, Syofmarlianisyah Putri, Fitri Kartiasih. Analisis Faktor-Faktor yang Memengaruhi Stunting di Indonesia Tahun 2021. Prosiding Seminar Nasional Official Statistics. 2023 Oct 4;2023(1):817–26.

Indah Pratiwi Putri, Terttiaavini Terttiaavini, Nur Arminarahmah. Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak. MALCOM Indonesian Journal of Machine Learning and Computer Science. 2024 Jan 15;4(1):257–65.

Indah Pratiwi Putri, Terttiaavini Terttiaavini, Nur Arminarahmah. Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak. MALCOM Indonesian Journal of Machine Learning and Computer Science. 2024 Jan 15;4(1):257–65.

Indah Syafitri Nasution, Susilawati Susilawati. Analisis faktor penyebab kejadian stunting pada balita usia 0-59 bulan. Florona. 2022 Aug 25;1(2):82–7.

Syahrani Lonang, Dwi Normawati. Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination. Jurnal media informatika Budidarma. 2022 Jan 25;6(1):49–9 [8] Y. Akbar and T. Sugiharto, “Analisis Sentimen Pengguna Twitter di Indonesia Terhadap ChatGPT Menggunakan Algoritma C4.5 dan Naïve Bayes,” Jurnal Sains dan Teknologi, vol. 5, no. 1, pp. 115–122, 2023, doi: 10.55338/saintek.v4i3.1368.

Nani Purwati, Gunawan Budi Sulistyo. Stunting Early Warning Application Using KNN Machine Learning Method. Jurnal Riset Informatika. 2023 Jun 10;5(3):373–8.

Otong Saeful Bachri, Raden. Penentuan Status Stunting pada Anak dengan Menggunakan Algoritma KNN. Jurnal Ilmiah Intech. 2021 Nov 30;3(02):130–7.

Indah Pratiwi Putri, Terttiaavini Terttiaavini, Nur Arminarahmah. Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak. MALCOM Indonesian Journal of Machine Learning and Computer Science. 2024 Jan 15;4(1):257–65.

Syahrial Syahrial, Rosmin Ilham, Asikin ZF, Surya S. Stunting Classification in Children’s Measurement Data Using Machine Learning Models. Journal La Multiapp. 2022 Mar 31;3(2):52–60

lzzati Rahmi, Mega Susanti, Hazmira Yozza, Frilianda Wulandari. CLASSIFICATION OF STUNTING IN CHILDREN UNDER FIVE YEARS IN PADANG CITY USING SUPPORT VECTOR MACHINE. Barekeng. 2022 Sep 1;16(3):771–8

Gibran Nasrizal Masacgi, Muhammad Syaifur Rohman. Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita. Edumatic : jurnal pendidikan informatika. 2023 Dec 20;7(2):455–64

Caesar Jalu Ananta, Arna Fariza, Rengga Asmara. Stunting Program Classification in East Java, Indonesia From Internet News Using Location-Based and SVM. 2023 Aug 8;

Azhari M, Situmorang Z, Rosnelly R. Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Byes. JURNAL MEDIA INFORMATIKA BUDIDARMA. 2021 Apr 25;5(2):640.

Maradona Jonas Simanullang, Hartono N, Kom S, Kom M, Roslina M.I.T. Combination of SOM, SVR, and LMKNN for Stock Price Prediction. 2023 Nov 10;

Frans Mikael Sinaga, Jonas M, Felix, Halim A. Stock Trend Prediction using SV-kNNC and SOM. 2019 Oct 1; [19] M. P. Geetha and D. Karthika Renuka, “Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model,” International Journal of Intelligent Networks, vol. 2, pp. 64–69, 2021, doi: 10.1016/j.ijin.2021.06.005.

Indah Pratiwi Putri, Terttiaavini Terttiaavini, Nur Arminarahmah. Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak. MALCOM Indonesian Journal of Machine Learning and Computer Science. 2024 Jan 15;4(1):257–65.

Bhavsar, H., & Panchal, M. H. (2012). A review on support vector machine for data classification. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), 185-189.




DOI: http://dx.doi.org/10.24014/ijaidm.v8i1.32177

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