Memodelkan Faktor-Faktor Yang Mempengaruhi Gizi Buruk Balita Dengan Metode Multivariate Adaptive Regression Spline (Mars)

Kasse, Irwan

doi:10.24252/jmsa.v5n1p69

Cited by 2 publications

(2 citation statements)

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“…GWR disini digunakan untuk memodelkan kejadian stunting dengan memperhatikan efek spasial atau wilayah. Sementara itu pada penelitian yang lain untuk memodelkan kejadian balita stunting menggunakan metode Multivariate Adaptive Regression Splines (MARS) diantaranya yang dilakukan oleh (Azizah & Permatasari, 2020), (Kasse, 2017), dan (Ningrum, 2021). MARS digunakan karena tidak adanya pola yang jelas antara variabel respon dengan variabel prediktor yang digunakan dalam penelitian-penelitian tersebut.…”

Section: Pendahuluanunclassified

Pemodelan Kejadian Balita Stunting di Kabupaten Bojonegoro dengan Metode Geographically Weighted Regression dan Multivariate Adaptive Regression Splines

Kartini

Ummah²

2022

J Statistika

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Toddler stunting is a chronic nutritional problem caused by one of which is inadequate nutritional intake in infants. Bojonegoro is a district in East Java province where the incidence of stunting under five is still common. In this study, we will compare the incidence of stunting under five in Bojonegoro district using GWR and MARS. GWR is able to model cases for each region spatially, while MARS is able to model cases without considering the pattern of relationships between predictor variables and response variables. The best GWR model selected the smallest CV and MSE values and the largest R-Square values. The best model of the Adaptive Bi-Square kernel function was obtained with a bandwidth of 28, the value of CV=2.4635, MSE=0.8620, and R-Square=0.8734. The best MARS model selected the smallest GCV value and the largest R-Square. From the combination of MO, BF and MI values, the best model was obtained at BF=24, MI=1 and MO=1 with GCV=1,29144 and R-Square=0,841 values. Comparing the two models, the MARS model is better because the R-Square value of the GWR model is greater than the MARS although the numerical value is not much different, while the MSE value of the MARS model is smaller than the MSE value of the GWR model with a much different difference. The results of this study will provide knowledge in the form of a regression model for the Bojonegoro district health office in predicting predictor variables that affect the incidence of stunting under five in Bojonegoro district.

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Section: Pendahuluanunclassified

Pemodelan Kejadian Balita Stunting di Kabupaten Bojonegoro dengan Metode Geographically Weighted Regression dan Multivariate Adaptive Regression Splines

Kartini

Ummah²

2022

J Statistika

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“…Menurut (Arifin, 2015)minimum jarak antar knot atau minimum observasi antar knot yakni bernilai 10, 20, 50 dan 100. Sehungga persamaan (4) dapat diformulasikan dengan (Kasse, 2017)…”

Section: Hasil Dan Pembahasanunclassified

Pemodelan Penyakit Infeksi Saluran Pernafasan Akut Di Daerah Sekitar Semburan Lumpur Lapindo Sidoarjo Dengan Pendekatan Model Multivariate Additive Regression Spline

Mahfudhotin¹

2022

Jambura J. Probab. Stat.

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The phenomenon of hot mudflow in Sidoarjo is interesting to be investigated further. Regarding the cause, the disaster occurred due to drilling errors resulting in the Lapindo mudflow which resulted in gas emissions causing health problems, especially those related to the respiratory tract, namely respiratory tract infections (ARI). Risk factors that can affect the incidence of ARI in general are socio-demographic, biological, housing and density factors and pollution. Therefore, this study aims to obtain a model for classifying ARI patient data in the Jabon, Tanggulangin, and Porong sub-districts, Sidoarjo district with the variables that contribute to the classification. The nonparametric approach Multivariate Adaptive Regression Spline (MARS) was chosen because several previous studies stated that this method resulted in a higher classification accuracy than other classification methods. In addition, MARS is a classification method that is able to form a model with causal interactions to produce the best MARS model obtained from a combination of Maximum Interaction (MI), Basis Function (BF), and Minimum Observation (MO) values. The results of modeling with MARS there are three variables that contribute to the grouping, namely the percentage of the distance between the house and the source of the Lapindo mudflow, the number of activities outside the house, and the number of house ventilation. The overall model classification accuracy is 97,4 percent with a GCV value of 0,096 and an R2 of 82,9 percent

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Memodelkan Faktor-Faktor Yang Mempengaruhi Gizi Buruk Balita Dengan Metode Multivariate Adaptive Regression Spline (Mars)

Cited by 2 publications

References 0 publications

Pemodelan Kejadian Balita Stunting di Kabupaten Bojonegoro dengan Metode Geographically Weighted Regression dan Multivariate Adaptive Regression Splines

Pemodelan Kejadian Balita Stunting di Kabupaten Bojonegoro dengan Metode Geographically Weighted Regression dan Multivariate Adaptive Regression Splines

Pemodelan Penyakit Infeksi Saluran Pernafasan Akut Di Daerah Sekitar Semburan Lumpur Lapindo Sidoarjo Dengan Pendekatan Model Multivariate Additive Regression Spline

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