Perbandingan Optimasi Metode Grid Search dan Random Search dalam Algoritma XGBoost untuk Klasifikasi Stunting

 (*)Nirvan Adam Pramudhyta Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Muhammad Syaifur Rohman (Universitas Dian Nuswantoro, Semarang, Indonesia)

(*) Corresponding Author

Submitted: November 3, 2023; Published: January 9, 2024

Abstract

Stunting is a condition of stunted physical growth in children due to chronic nutritional deficiencies with serious impacts on health and psychological aspects. The impacts include decreased self-esteem, learning difficulties, impaired concentration, critical thinking problems, and lower economic contributions as adults. This study aims to optimize the XGBoost classification model using the Grid Search and Random Search methods, thereby improving the accuracy of detecting stunting and obtaining an accurate and efficient diagnosis. Seeing the danger and alarming prevalence rate of stunting, signaling the urgency of handling this problem for the welfare of future generations, an automatic classification model is needed to avoid subjectivity and potential errors in the manual decision-making process. XGBoost needs optimization because it has parameters that require adjustment to maximize accuracy. Comparison of two optimization models is important to understand the advantages and disadvantages of each because they have different approaches in finding the best combination. The study used 10,000 data from Krobokan Health Center with attributes of gender, age, birth weight, birth height, weight at measurement, height at measurement, and category. The largest increase in accuracy was obtained by the Grid Search model with an increase in XGBoost accuracy of 5.81% from 83.28% to 89.09%. The Random Search model increased the accuracy by 5.43%, reaching an accuracy of 88.71%. The choice of both models depends on time and resource preferences. Random Search provides higher time efficiency than Grid Search. This research can contribute to identifying children at risk of stunting so that intervention actions can be carried out more efficiently.

Keywords


Stunting; Optimization; XGBoost; Grid Search; Random Search

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References

M. R. Nugroho, R. N. Sasongko, and M. Kristiawan, Faktor-faktor yang Mempengaruhi Kerjadian Stunting pada Anak Usia Dini di Indonesia, J. Obs. J. Pendidik. Anak Usia Dini, vol. 5, no. 2, pp. 22692276, 2021, doi: 10.31004/obsesi.v5i2.1169.

J. Riptek Widya et al., Kajian Stunting di Kota Semarang, J. Riptek, vol. 13, no. 2, pp. 101106, 2019, [Online]. Available: http://riptek.semarangkota.go.id.

K. Rahmadhita, Permasalahan Stunting dan Pencegahannya, J. Ilm. Kesehat. Sandi Husada, vol. 11, no. 1, pp. 225229, 2020, doi: 10.35816/jiskh.v11i1.253.

M. Rafika, Dampak Stunting Pada Kondisi Psikologis Anak, Bul. Jagaddhita, vol. 1, no. 1, pp. 14, 2019, [Online]. Available: http://dx.doi.org/10.4236/ojmp.2016.54007.

R. Pratiwi, R. S. Sari, and F. Ratnasari, Dampak Status Gizi Pendek (Stunting) Terhadap Prestasi Belajar, J. Ilm. Ilmu Keperawatan, vol. 12, no. 2, pp. 1023, 2021, [Online]. Available: https://stikes-nhm.e-journal.id/NU/article/view/317/284.

E. Sumartini, Dampak Stunting Terhadap Kemampuan Kognitif Anak, Pros. Semin. Nas. Kesehat. Peran Tenaga Kesehat. Dalam Menurunkan Kerjadian Stunting Tahun 2020 Impact, pp. 127134, 2020.

H. Dasman, Empat Dampak Stunting Bagi Anak dan Negara Indonesia, Conversat. (Disipln Ilmiah, gaya Jurnalistik), pp. 24, 2019, [Online]. Available: http://repo.unand.ac.id/21312/1/Empat dampak Stunting bagi anak dan negara Indonesia.pdf.

Y. Primasari and budi anna Keliat, Praktik Pengasuhan sebagai Upaya Pencegahan Dampak Stunting pada Perkembangan Psikososial Anak-Kanak, J. Ilmu Keperawatan, vol. 3, no. 3, pp. 263272, 2020.

Kemenkes, Hasil Survei Status Gizi Indonesia (SSGI) 2022. 2022.

W. H. Organisation, World Health Statistics 2022 (Monitoring Health of the SDGs). 2022.

B. Jange, Prediksi Harga Saham Bank BCA Menggunakan Prophet, J. Trends Econ. Account. , vol. 2, no. 1, pp. 15, 2021, doi: 10.47065/arbitrase.v3i2.495.

S. T. K. Theopilus Bayu Sasongko &, Optimasi K-Nearest Neighbor dengan Grid Search CV pada Prediksi Kanker ParuParu, STMIK Indones. Padang, vol. 8, no. 2, p. 121, 2019.

G. Abdurrahman, H. Oktavianto, and M. Sintawati, Optimasi Algoritma XGBoost Classifier Menggunakan Hyperparameter Gridesearch dan Random Search Pada Klasifikasi Penyakit Diabetes, INFORMAL Informatics J., vol. 7, no. 3, p. 193, 2022, doi: 10.19184/isj.v7i3.35441.

W. C. Wahyudin et al., Prediksi Stunting Pada Balita Di Rumah Sakit Kota Semarang Menggunakan Naive Bayes, vol. 2019, pp. 3236, 2023.

Juwariyem and Sriyanto, Prediksi Stunting Pada Balita Menggunakan Algoritma Random Forest, J. IndraTech, vol. 4, no. 1, pp. 2937, 2023.

D. K. D. Damayanti, Klasifikasi Status Stunting Balita Menggunakan Algoritma Fuzzy C-Means, J. Ilm. Mat., vol. 9, no. 2, pp. 437446, 2021, [Online]. Available: https://media.neliti.com/media/publications/249234-model-infeksi-hiv-dengan-pengaruh-percob-b7e3cd43.pdf.

C. Dewanti, V. Ratnasari, T. Rumiati, D. Statistika, F. Matematika, and S. Data, Pemodelan Faktor-faktor yang Memengaruhi Status Balita Stunting di Provinsi Jawa Timur Menggunakan Regresi Probit Biner, vol. 8, no. 2, 2019.

H. Pohan, M. Zarlis, E. Irawan, H. Okprana, and Y. Pranayama, Penerapan Algoritma K-Medoids dalam Pengelompokan Balita Stunting di Indonesia, JUKI J. Komput. dan Inform., vol. 3, no. 2, pp. 97104, 2021, doi: 10.53842/juki.v3i2.69.

S. E. Herni Yulianti, Oni Soesanto, and Yuana Sukmawaty, Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit, J. Math. Theory Appl., vol. 4, no. 1, pp. 2126, 2022, doi: 10.31605/jomta.v4i1.1792.

A. Toha, P. Purwono, and W. Gata, Model Prediksi Kualitas Udara dengan Support Vector Machines dengan Optimasi Hyperparameter GridSearch CV, Bul. Ilm. Sarj. Tek. Elektro, vol. 4, no. 1, pp. 1221, 2022, doi: 10.12928/biste.v4i1.6079.

U. Sunarya and T. Haryanti, Perbandingan Kinerja Algoritma Optimasi pada Metode Random Forest untuk Deteksi Kegagalan Jantung, J. Rekayasa Elektr., vol. 18, no. 4, pp. 241247, 2022, doi: 10.17529/jre.v18i4.26981.

R. G. Gunawan, Erik Suanda Handika, and Edi Ismanto, Pendekatan Machine Learning Dengan Menggunakan Algoritma Xgboost (Extreme Gradient Boosting) Untuk Peningkatan Kinerja Klasifikasi Serangan Syn, J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 3, pp. 453463, 2022, doi: 10.37859/coscitech.v3i3.4356.

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