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All Journal ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Fundamental Sciences
Putri, Siti Choirotun Aisyah
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics

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Pemodelan Faktor-Faktor yang Mempengaruhi Kasus Stunting di Sulawesi Selatan Menggunakan Geographically Weighted Regression Putri, Siti Choirotun Aisyah; Salsabila, Afifah; Suardi, Shafira; Mutmainnah, Mutmainnah; Aswi, Aswi
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.30142

Abstract

One of the prevalent nutritional issues affecting toddlers worldwide is stunting. Several studies on stunting cases have been conducted in Indonesia. However, modeling using the Geographically Weighted Regression (GWR) method in South Sulawesi has not been carried out. This study aims to identify the variables that affect the incidence of stunting in each district in South Sulawesi based on spatial modeling using the GWR method. Data on the number of stunting cases, the pproportion of low-birth-weight infants, the percentage of under-five who are malnourished, the percentage of proper drinking water, and the percentage of poor people in South Sulawesi in 2020 were used. The results show that the GWR model has an  value of 86.64%, which is higher than that of the global regression model. The factors that influence the percentage of stunting based on the GWR modeling results are the percentage of under-five who are malnourished and the percentage of proper drinking water. The findings of this study are anticipated to help the government address the issue of stunting in South Sulawesi. Early prevention may then be implemented.
Evaluasi Performa Model Regresi Poisson Tweedie dan Conway Maxwell Poisson dalam Menangani Masalah Dispersi: Studi Angka Kematian Ibu di Provinsi Sulawesi Selatan Aswi, Aswi; Sanusi, Wahidah; Tiro, Muhammad Arif; Sukarna, Sukarna; Haekal, Muh. Fahri; Palarungi, Andi Gagah; Putri, Siti Choirotun Aisyah; Oktaviana, Oktaviana
Indonesian Journal of Fundamental Sciences Vol 11, No 2 (2025)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/ijfs.v11i2.77506

Abstract

Model regresi Poisson digunakan untuk menganalisis hubungan antara satu atau lebih variabel independen dengan variabel dependen berupa data cacahan. Salah satu asumsi utamanya adalah kesamaan antara nilai mean dan variansi (equidispersi). Namun, dalam praktiknya, asumsi tersebut sering tidak terpenuhi. Kondisi ini menyebabkan model regresi Poisson kurang sesuai digunakan, karena dapat menghasilkan estimasi standar error yang terlalu kecil (underestimate). Model alternatif yang dapat digunakan untuk mengatasi masalah overdispersi adalah Regresi Poisson Tweedie dan Conway Maxwell Poisson (CMP). Penelitian ini bertujuan untuk mengevaluasi kinerja model regresi Poisson Tweedie dan regresi CMP dalam menangani masalah dispersi pada data Angka Kematian Ibu (AKI) di Provinsi Sulawesi Selatan, Indonesia. Estimasi parameter dilakukan dengan metode Estimasi Kemungkinan Maksimum (MLE), sedangkan kinerja model dinilai berdasarkan Akaike Information Criterion (AIC), Mean Square Error (MSE), dan signifikansi parameter. Hasil penelitian menunjukkan bahwa model regresi Poisson standar kurang sesuai karena adanya pelanggaran asumsi ekuidispersi. Sebaliknya, model CMP dan Poisson Tweedie memberikan alternatif yang lebih tepat, dimana Model CMP menunjukkan akurasi prediktif yang lebih tinggi dengan nilai MSE terendah. Faktor perdarahan, hipertensi, gangguan kardiovaskular, dan komplikasi pasca-aborsi ditemukan memiliki pengaruh yang signifikan terhadap kematian ibu, sementara infeksi tidak signifikan secara statistik. 
Co-Authors Aswi, Aswi Aswi, Aswi Haekal, Muh. Fahri Muhammad Arif Tiro, Muhammad Arif Oktaviana Oktaviana Palarungi, Andi Gagah Salsabila, Afifah Suardi, Shafira Sukarna Sukarna Syamsiar, Syamsiar Wahidah Sanusi