COMPARISON OF MARS AND BINARY LOGISTIC REGRESSION MODELS FOR IDENTIFYING STUNTING RISK FACTORS IN TODDLERS IN TELUK WARU, EAST SERAM REGENCY

DOI: https://doi.org/10.30598/barekengvol20iss2pp1541–1556
Keywords: Binary Logistic Regression, MARS, Prevalence, Stunting

Abstract

In 2022, the prevalence of chronic stunting in Indonesia reached 21.6%, surpassing the World Health Organization (WHO) threshold of 20%. East Seram Regency reported an even higher prevalence of 24.1%, with Teluk Waru District identified as one of the areas most affected due to low compliance with healthy lifestyle practices. This study aimed to compare the performance of Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression in analyzing risk factors for toddler stunting in Teluk Waru District, East Seram Regency. Data were collected through direct anthropometric measurements at the Integrated Health Post (Posyandu) of Teluk Waru Health Center with 60 respondents. The findings revealed that Binary Logistic Regression outperformed MARS, achieving R2 = 72.7% accuracy in predicting stunting. Significant determinants of toddler stunting included a history of illness, provision of supplementary food for pregnant women, and iron tablet consumption during pregnancy. The novelty of this study lies in the application of a comparative modeling approach—MARS versus Binary Logistic Regression—in identifying stunting risk factors at a district level with high prevalence. Practically, the results can assist local health authorities in prioritizing maternal nutrition and disease prevention programs to reduce stunting.

Downloads

Download data is not yet available.

References

Nur Latifah Ariyani, “HUBUNGAN POLA ASUH MAKAN DAN KEBIASAAN MAKAN KELUARGA TERHADAP STATUS GIZI BALITA DI DESA TUMIYANG KECAMATAN PEKUNCEN,” Universitas Muhammadiyah Purwokerto, Purwokerto, 2017.

Kementerian Kesehatan Republik Indonesia, “LAPORAN NASIONAL RISET KESEHATAN DASAR TAHUN 2018,” 2018.

Y. Karyati, “PENGARUH JUMLAH PENDUDUK MISKIN, LAJU PERTUMBUHAN EKONOMI, DAN TINGKAT PENDIDIKAN TERHADAP JUMLAH STUNTING DI 10 WILAYAH TERTINGGI INDONESIA TAHUN 2010-2019,” Jurnal Riset Ilmu Ekonomi Dan Bisnis, pp. 101–108, 2021, doi: https://doi.org/10.29313/jrieb.v1i2.401.

R. Kurniawan and B. Yoniarto, ANALISIS REGRESI DASAR DAN PENERAPANNYA DENGAN R. Jakarta: Kencana, 2016.

Achmad Faqih, “ANALISIS FAKTOR RESIKO STUNTING MENGGUNAKAN REGRESI LOGISTIK BINER,” Universitas Islam Negeri Sunan Ampel, Surabaya, 2020.

D. Ariesta, N. Gusriani, and K. Parmikanti, “ESTIMASI PARAMETER MODEL REGRESI NONPARAMETRIK B-SPLIN E PADA ANGKA KEMATIAN MATERNAL,” Jurnal Matematika UNAND, vol. 10, no. 3, pp. 342–354, 2021, doi: https://doi.org/10.25077/jmu.10.3.342-354.2021.

R. Binadari, Y. Wilandari, and S. Suparti, “PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA PEMINATAN JURUSAN SMA (STUDI KASUS SMA NEGERI 2 SEMARANG),” Jurnal Gaussian, vol. 4, no. 4, pp. 987–996, 2015, doi: https://doi.org/10.14710/j.gauss.4.4.987-996.

T. R. Elisa, “PENERAPAN METODE MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) UNTUK KLASIFIKASI BALITA STUNTING DI KECAMATAN PADANG TIMUR,” Universitas Andalas, Padang, 2022.

A. Utami, “ANALISIS FAKTOR-FAKTOR YANG DIDUGA BERPENGARUH TERHADAP KASUS BALITA STUNTING DI JAWA TIMUR MENGGUNAKAN REGRESI LOGISTIK BINER,” Institut Teknologi Sepuluh Nopember, Surabaya, 2024.

R. Lu, T. Duan, M. Wang, H. Liu, S. Feng, X. Gong, H. Wang, J. Wang, Z. Cui, Y. Liu, C. Li and Jun Ma., “THE APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES IN EXPLORING THE INFLUENCING FACTORS AND PREDICTING THE PREVALENCE OF HBA1C IMPROVEMENT,” Ann Palliat Med, vol. 10, no. 2, pp. 1291303–1296303, 2021, doi: https://doi.org/10.21037/apm-19-406.

M. Gackowski, K. Szewczyk-Golec, R. Pluskota, M. Koba, K. Mądra-Gackowska, and A. Woźniak, “APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARSPLINES) FOR PREDICTING ANTITUMOR ACTIVITY OF ANTHRAPYRAZOLE DERIVATIVES,” Int J Mol Sci, vol. 23, no. 9, p. 5132, 2022, doi: https://doi.org/10.3390/ijms23095132.

C.-F. Chang, T.-W. Chu, C.-H. Liu, S.-T. Wu, and C.-C. Yang, “EQUATION BUILT BY MULTIPLE ADAPTIVE REGRESSION SPLINE TO ESTIMATE BIOLOGICAL AGE IN HEALTHY POSTMENOPAUSAL WOMEN IN TAIWAN,” Diagnostics, vol. 15, no. 9, p. 1147, 2025, doi: https://doi.org/10.3390/diagnostics15091147.

Sugiyono, Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: Alphabet, 2019.

E. Y. Sari, “ANALISIS SURVIVAL DENGAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) UNTUK DATA RESAMPLING,” Universitas Lampung, Lampung, 2016.

A. Adityaningrum, “ESTIMASI PROSPENSITY SCORE MATCHING BERDASARKAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES,” Institut Teknologi Sepuluh Nopember, Surabaya, 2017.

S. Anam, S. Sugiman, and S. Sunarmi, “KETEPATAN KLASIFIKASI DENGAN MENGGUNAKAN METODE MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA DATA KELOMPOK RUMAH TANGGA KABUPATEN CILACAP,” Unnes Journal of Mathematics, vol. 6, no. 1, pp. 92–101, 2017, doi: https://doi.org/10.15294/ujm.v6i1.13638.

M. B. Adiguzel and M. A. Cengiz, “MODEL SELECTION IN MULTIVARIATE ADAPTIVE REGRESSIONS SPLINES (MARS) USING ALTERNATIVE INFORMATION CRITERIA,” Heliyon, vol. 9, no. 9, 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e19964.

C. Conoscenti, M. Ciaccio, N. A. Caraballo-Arias, Á. Gómez-Gutiérrez, E. Rotigliano, and V. Agnesi, “ASSESSMENT OF SUSCEPTIBILITY TO EARTH-FLOW LANDSLIDE USING LOGISTIC REGRESSION AND MULTIVARIATE ADAPTIVE REGRESSION SPLINES: A CASE OF THE BELICE RIVER BASIN (WESTERN SICILY, ITALY),” Geomorphology, vol. 242, pp. 49–64, 2015, doi: https://doi.org/10.1016/j.geomorph.2014.09.020.

D. W. Hosmer, S. Lemeshow, and S. May, “APPLIED SURVIVAL ANALYSIS,” Wiley Series in Probability and Statistics, p. 60, 2008.

A. Strzelecka, A. Kurdyś-Kujawska, and D. Zawadzka, “APPLICATION OF LOGISTIC REGRESSION MODELS TO ASSESS HOUSEHOLD FINANCIAL DECISIONS REGARDING DEBT,” Procedia Comput Sci, vol. 176, pp. 3418–3427, 2020, doi: https://doi.org/10.1016/j.procs.2020.09.055.

S. Risambessy, S. N. Aulele, and F. K. Lembang, “MISCLASSIFICATION ANALYSIS OF ELEMENTARY SCHOOL ACCREDITATION DATA IN AMBON CITY USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE,” Jurnal Matematika, Statistika Dan Komputasi, vol. 18, no. 3, pp. 394–406, 2022, doi: https://doi.org/10.20956/j.v18i3.19451.

S. Park, S.-Y. Hamm, H.-T. Jeon, and J. Kim, “EVALUATION OF LOGISTIC REGRESSION AND MULTIVARIATE ADAPTIVE REGRESSION SPLINE MODELS FOR GROUNDWATER POTENTIAL MAPPING USING R AND GIS,” Sustainability, vol. 9, no. 7, p. 1157, 2017, doi: https://doi.org/10.3390/su9071157.

Published
2026-01-26
How to Cite
[1]
J. B. Bension, F. Kondo Lembang, N. F. Idris, and N. Lewaherilla, “COMPARISON OF MARS AND BINARY LOGISTIC REGRESSION MODELS FOR IDENTIFYING STUNTING RISK FACTORS IN TODDLERS IN TELUK WARU, EAST SERAM REGENCY”, BAREKENG: J. Math. & App., vol. 20, no. 2, pp. 1541–1556, Jan. 2026.
Issue
Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2026 Johan Bruiyf Bension, Ferry Kondo Lembang, Nurul Fadillah Idris, Norisca Lewaherilla

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.