Analisis Perbandingan Metode AdaBoost, Gradient Boosting, dan XGBoost Untuk Kalsifikasi Status Gizi Pada Balita
Abstract
Nutritional issues in toddlers are a crucial issue that significantly impacts the health and development of children in Indonesia. Malnutrition can lead to various long-term health problems. Therefore, detecting and classifying the nutritional status of toddlers is very important. This study aims to analyze and compare boosting techniques to classify the nutritional status of toddlers, focusing on three boosting techniques: AdaBoost, Gradient Boosting, and XGBoost. This is done because boosting techniques work by sequentially building models, where each new model attempts to correct the prediction errors of the previous model. The results show that the XGBoost model provides the best performance with a precision of 0.9849, recall of 0.9848, accuracy of 0.9848, F1 score of 0.9848, and ROC-AUC of 0.9994 at an 80:20 data split ratio. Conversely, the AdaBoost model shows the lowest results with a precision of 0.6294, recall of 0.6292, accuracy of 0.6292, F1 score of 0.6291, and ROC-AUC of 0.7581 at a 90:10 data split ratio, caused by its sensitivity to outliers and noise in the data. These findings indicate that XGBoost is the best boosting model for classifying the nutritional status of toddlers, followed by Gradient Boosting, with AdaBoost in the last position. The outstanding performance of XGBoost is due to the use of regularization techniques, effective handling of missing values, and efficient and fast boosting algorithms through parallel processing techniques.
Downloads
References
Z. Prihatini and S. W. Wibawa, “Angka Stunting Indonesia 24,4 Persen, 7 Provinsi Catat Kasus Tertinggi,” kompas.com, 2022. https://www.kompas.com/sains/read/2022/07/01/140000123/angka-stunting-indonesia-24-4-persen-7-provinsi-catat-kasus-tertinggi
S. Sukmawati, Faktor-faktor yang Berhubungan dengan Kejadian Stunting pada Balita. Pekalongan: Penerbit NEM, 2023.
A. Wahyu, L. Ginting, and N. D. Sinaga, Faktor Penyebab Terjadinya Stunting. Sukabumi: CV Jejak (Jejak Publisher), 2022.
B. Analysis and C. Analysis, “Upaya Penanganan Stunting di Indonesia: Analisis Bibliometrik dan Analisis Konten,” J. Ilmu Pemerintah. Suara Khatulistiwa, vol. VIII, no. 01, pp. 44–59, 2023.
M. L. Dambe, S. Y. Padang, and M. S. Adha, “Evaluasi K-Nearest Neighbour Untuk Klasifikasi Status Gizi Balita,” INFINITY, vol. 3, no. 1, pp. 33–40, 2023, doi: 10.34148/infinity.v9i1.xxx.
F. M. Sarimole, F. B. Pasaribu, Y. Akbar, and A. Z. Hidaya, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Status Gizi Balita Di Posyandu Nusa Indah 4,” J. Tek., vol. 18, no. 2, pp. 489–500, 1978.
D. N. A. Kurniawan and M. Maryam, “Implementasi Metode Decision Tree pada Sistem Prediksi Status Gizi Balita,” J. Sains Komput. Inform., vol. 7, no. 2, pp. 731–739, 2023.
Y. R. Nasution, A. Armansyah, M. Furqan, and T. R. Matondang, “Penerapan Algoritma C4.5 Pada Klasifikasi Status Gizi Balita,” J. FASILKOM, vol. 14, no. 1, pp. 216–225, 2024.
P. Handayani, A. C. Fauzan, and H. Harliana, “Machine Learning Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 6, pp. 3064–3072, 2024, doi: 10.30865/klik.v4i6.1909.
N. Nurhayati, Teknik Ensemble Learning Untuk Peningkatan Performa Akurasi Model Prediksi (Seleksi Mahasiswa Penerima Beasiswa). Tangerang: Pascal Books, 2022.
G. Abdurrahman, “Klasifikasi Penyakit Diabetes Melitus Menggunakan Adaboost Classifier,” JUSTINDO (Jurnal Sist. Teknol. Inf. Indones., vol. 7, no. 1, pp. 59–66, 2022.
V. Atlantic, E. Sulistianingsih, and H. Perdana, “Gradient Boosting Machine Pada Klasifikasi Kelulusan Mahasiswa,” Bul. Ilm. Math. Stat. dan Ter., vol. 13, no. 2, pp. 165–174, 2024.
A. H. Permana, F. R. Umbara, and F. Kasyidi, “Klasifikasi Penyakit Jantung Tipe Kardiovaskular Menggunakan Adaptive Synthetic Sampling dan Algoritma Extreme Gradient Boosting,” Build. Informatics, Technol. Sci., vol. 6, no. 1, pp. 499–508, 2024, doi: 10.47065/bits.v6i1.5421.
Y. Fernando, R. Napianto, and R. I. Borman, “Implementasi Algoritma Dempster-Shafer Theory Pada Sistem Pakar Diagnosa Penyakit Psikologis Gangguan Kontrol Impuls,” Insearch Inf. Syst. Res. J., vol. 2, no. 2, pp. 46–54, 2022.
R. P. Pradana, “Stunting Toddler Detection,” Kaggle, 2024. https://www.kaggle.com/datasets/rendiputra/stunting-balita-detection-121k-rows/
R. I. Borman and M. Wati, “Penerapan Data Maining Dalam Klasifikasi Data Anggota Kopdit Sejahtera Bandarlampung Dengan Algoritma Naïve Bayes,” J. Ilm. Fak. Ilmu Komput., vol. 9, no. 1, pp. 25–34, 2020.
R. I. Borman, F. Rossi, D. Alamsyah, R. Nuraini, and Y. Jusman, “Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022.
T. H. Saragih, M. Muliadi, M. R. Faisal, and M. A. I. N. R. Said, “AdaBoost Classifier untuk Klasifikasi Tanaman Jarak Pagar,” J. Komputasi, vol. 9, no. 2, pp. 60–66, 2021.
R. T. Febianto, D. Suranti, and R. T. Alinse, “Penerapan Algoritma Adaboost Dalam Mengetahui Pola Pengguna KB di Puskesmas Tanjung Harapan,” J. Sci. Soc. Res., vol. VII, no. 1, pp. 145–155, 2024.
N. Novianti, M. Zarlis, and P. Sihombing, “Penerapan Algoritma Adaboost Untuk Peningkatan Kinerja Klasifikasi Data Mining Pada Imbalance Dataset Diabetes,” J. Media Inform. Budidarma, vol. 6, no. 2, pp. 1200–1206, 2022, doi: 10.30865/mib.v6i2.4017.
M. Ridwansyah and H. Zakaria, “Implementasi Algortima Gradient Boosting Pada Aplikasi Hutang Piutang Perorangan Secara Berbasis Web Untuk Meningkatan Akurasi Prediksi Pelunasan Hutang (Studi Kasus : PT Naila Kreasi Mandiri),” JURIHUM J. Inov. dan Hum., vol. 1, no. 4, pp. 440–451, 2023.
S. E. Suryana, B. Warsito, and S. Suparti, “Penerapan Gradient Boosting dengan Hyperopt Untuk Memprediksi Keberhasilan Telemarketing Bank,” J. Gaussian, vol. 10, no. 4, pp. 617–623, 2021.
J. Melvin and A. Soraya, “Analisis Perbandingan Algoritma XGBoost dan Algoritma Random Forest Ensemble Learning pada Klasifikasi Keputusan Kredit,” J. Ris. Rumpun Mat. dan Ilmu Pengetah. Alam, vol. 2, no. 2, pp. 87–103, 2023.
S. E. H. Yulianti, O. Soesanto, and Y. Sukmawaty, “Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit,” J. Math. Theory Appl., vol. 4, no. 1, pp. 21–26, 2022.
I. M. K. Karo, “Implementasi Metode XGBoost dan Feature Importance untuk Klasifikasi pada Kebakaran Hutan dan Lahan,” J. Softw. Eng. Inf. Commun. Technol., vol. 1, no. 1, pp. 11–18, 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Perbandingan Metode AdaBoost, Gradient Boosting, dan XGBoost Untuk Kalsifikasi Status Gizi Pada Balita
Pages: 1799-1807
Copyright (c) 2024 Moh. Erkamim, Adam M Tanniewa, Irfan AP, Nurhayati Nurhayati

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).