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All Journal ESTIMASI: Journal of Statistics and Its Application MATHunesa: Jurnal Ilmiah Matematika
Ginting, Keristina Br.
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Mathematics

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PERBANDINGAN METODE SARIMA DAN BAYESIAN STRUCTURAL TIME SERIES PADA PERAMALAN INFLASI PROVINSI NUSA TENGGARA TIMUR Messakh, Louisa Feolin; Atti, Astri; Haning, Farly Oktriany; Ginting, Keristina Br.
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p421-429

Abstract

Inflation is one of the main macroeconomic indicators in Indonesia. Inflation occurs when demand exceeds supply, and if not properly controlled, it may affect the economic stability of a region. Inflation forecasting is therefore essential as a basis for governments in formulating and evaluating economic policies. This study aims to compare the performance of the Seasonal Autoregressive Moving Average (SARIMA) method and the Bayesian Structural Time Series (BSTS) method in forecasting inflation in East Nusa Tenggara Province. SARIMA is a classical forecasting method designed to handle seasonal patterns, while BSTS is a state-space model that allows separate decomposition of trend, seasonal, and regression components. The results of this study indicate that the BSTS method outperforms SARIMA, as reflected by smaller forecast error values. The BSTS model with a Semilocal Linear Trend component produces an RMSE of 0.5893397, an MAE of 0.4759239, and a MASE of 0.6509315.
MODEL REGRESI LOGISTIK BINER PADA FAKTOR-FAKTOR YANG MEMPENGARUHI PENYAKIT JANTUNG KORONER PADA PASIEN PENDERITA JANTUNG DI RSUD PROF DR. W. Z. JOHANNES KUPANG Ndolu, Wanda Susanti; Atti, Astri; Ginting, Keristina Br.
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p471-476

Abstract

Penyakit jantung koroner merupakan penyakit yang menyerang jantung dan termasuk salah satu penyakit yang mematikan didunia. Di Indonesia sendiri penyakit jantung koroner tercatat sebanyak 1,5% penyebab kematian setelah stroke. Penelitian ini dilakukan untuk mengetahui model analisis regresi logistik biner dan mengetahui faktor-faktor yang mempengaruhi terjadinya penyakit jantung koroner pada pasien di RSUD Prof. Dr. W. Z. Johannes Kupang. Sumber data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh dari rekam medis pasien di Poli Jantung RSUD Prof. Dr. W. Z. Johannes Kupang pada tahun 2023 Sebanyak 120 pasien yang dijadikan sampel dan dianalisis menggunakan regresi logistik biner. Variabel dependen yaitu status penderita penyakit jantung koroner dan variabel independen yaitu jenis kelamin, usia, riwayat penyakit keluarga, hipertensi, indeks massa tubuh, diabetes mellitus, dan merokok. Hasil analisis regresi logistik biner menunjukkan bahwa model regresi logistik biner pada pasien penderita penyakit jantung koroner di RSUD Prof. Dr. W. Z. Johannes Kupang adalah dan variabel independen yang mempengaruhi terjadinya penyakit jantung koroner yaitu hipertensi dan diabetes mellitus. Kata Kunci: Penyakit jantung koroner, regresi logistik biner, hipertensi, diabetes mellitus, faktor risiko
Model Generalized Poisson Regression (GPR) pada Faktor-Faktor yang Mempengaruhi Jumlah Kasus Stunting di Kabupaten Kupang, Provinsi Nusa Tenggara Timur (NTT) Jeharu, Bernadinus; Guntur, Robertus Dole; Ginting, Keristina Br.; Pahnael, Jusrry Rosalina
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44106

Abstract

Stunting is a condition of failure to thrive due to chronic malnutrition, recurrent infections, and poor sanitation. East Nusa Tenggara (NTT) Province is the highest contributor of stunting cases in Indonesia and Kupang Regency is also the third highest contributor of stunting cases in NTT Province. This study aims to identify factors that influence the number of stunting cases using the Generalized Poisson Regression (GPR) model which is able to overcome overdispersion in count data. Secondary data for 2023 was obtained from the Kupang District Health Office and BPS. Independent variables included LBW, complete basic immunization (IDL), exclusive breastfeeding, nutritional status of children under five, access to sanitation and safe drinking water, vitamin A administration, number of health centers, and health workers. The results of the analysis show that the percentage of IDL toddlers, the percentage of neighborhoods with access to safe drinking water, the number of infants receiving Vitamin A, exclusive breastfeeding, the number of health centers, and the number of community health workers have a significant effect on the number of stunting cases in Kupang district. These findings can inform the formulation of more effective health intervention policies in the region.
Co-Authors Farly Oktriany Haning Jeharu, Bernadinus Jusrry Rosalina Pahnael Messakh, Louisa Feolin Ndolu, Wanda Susanti Robertus Dole Guntur