Analisis Perbandingan Metode AdaBoost, Gradient Boosting, dan XGBoost Untuk Kalsifikasi Status Gizi Pada Balita


  • Moh. Erkamim * Mail Universitas Tunas Pembangunan, Surakarta, Indonesia
  • Adam M Tanniewa Universitas Sulawesi Barat, Majene, Indonesia
  • Irfan AP Universitas Sulawesi Barat, Majene, Indonesia
  • Nurhayati Nurhayati Universitas Muhammadiyah Tangerang, Tangerang, Indonesia
  • (*) Corresponding Author
Keywords: Metode Boosting; AdaBoost; Gradient Boosting; XGBoost; Nutritional Status Classification

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.

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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.


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Article History
Submitted: 2024-07-31
Published: 2024-12-25
Abstract View: 73 times
PDF Download: 50 times
How to Cite
Erkamim, M., Tanniewa, A., AP, I., & Nurhayati, N. (2024). Analisis Perbandingan Metode AdaBoost, Gradient Boosting, dan XGBoost Untuk Kalsifikasi Status Gizi Pada Balita. Building of Informatics, Technology and Science (BITS), 6(3), 1799-1807. https://doi.org/10.47065/bits.v6i3.5717
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