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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan
Fauziah, Nathania
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MODELING THE INFLUENCE OF CRUDE OIL PRODUCTION AGAINST INDONESIAN SOLAR WHOLESALE PRICE INDEX WITH LEAST SQUARE SPLINE ESTIMATOR APPROACH Pratiwi, Rosidun Nindyo; Fauziah, Nathania; Syahputra, Bimo Okta; Firmanda, Ahmad Wahyu; Amelia, Dita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp805-818

Abstract

Solar plays a crucial role in supporting energy sector activities in Indonesia. The fluctuating price of solar is influenced by crude oil production, as crude oil is the main raw material in solar production. The Russia-Ukraine war, which reached its peak in March 2020, also impacted global oil production, given that Russia is one of the largest oil producers and exporters in the world. This study aims to model the effect of crude oil production on the Solar Wholesale Price Index (SWPI) in Indonesia after the Russia-Ukraine war using the Least Squares Spline estimator approach. This approach was chosen because the relationship between the variables is complex and nonlinear, making linear models unsuitable. The results show that the best model is a nonparametric model with three knot points at a polynomial degree of one, which explains 90.26% of the variability in crude oil production relative to the SWPI. The optimal knot points were selected using the Generalized Cross Validation (GCV) method, resulting in a minimum GCV value of 320.9889. Crude oil production was found to have a significant effect on the SWPI and meets the classical assumption tests. However, this study has limitations, as it only considers the effect of crude oil production without including other external factors, such as energy policies or geopolitical influences. Additionally, the model still has limitations in capturing more complex relationship patterns. This study offers an original contribution through the application of the Least Squares Spline estimator approach, which has not been widely used before in analyzing the relationship between crude oil production and SWPI in Indonesia. For future research, it is recommended that the model be expanded by considering more knot points and higher polynomial degrees to capture more complex relationship patterns between these variables.
Modeling Risk Factors of Acute Respiratory Infections using Logistic Regression and Multivariate Adaptive Regression Splines Kurniawan, Ardi; Fauziah, Nathania; Mahadesyawardani, Arinda; Gunawan, Syifa’ Azizah Putri; Anggakusuma, Aurellia Calista
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33833

Abstract

Acute Respiratory Infections (ARI) remain a leading cause of morbidity among toddlers, partic ularly in regions with limited healthcare access. This study aimed to model the risk factors of ARI in toddlers using Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS). Using secondary data from Southeast Aceh, seven predictor variables were analyzed, including ma ternal characteristics, breastfeeding status, and household conditions. Both models were statisti cally significant in identifying key predictors. Logistic regression showed superior performance with 86.96% accuracy, 85.00% precision, 91.89% recall, 81.25% specificity, and 88.30% F1-score. In contrast, MARS achieved a higher recall (97.30%) but lower specificity (62.50%), indicating higher sensitivity but a greater likelihood of false positives. Exclusive breastfeeding, home ventilation, and housing density were significant predictors in both models. Overall, logistic regression was found to be the more reliable and interpretable method, offering better balance in classification metrics. These f indings support the use of logistic regression for identifying ARI risk factors in similar contexts and contribute to improved data-driven public health strategies aimed at reducing ARI incidence among vulnerable populations.
Co-Authors Anggakusuma, Aurellia Calista Ardi Kurniawan Dita Amelia, Dita Firmanda, Ahmad Wahyu Gunawan, Syifa’ Azizah Putri Mahadesyawardani, Arinda Pratiwi, Rosidun Nindyo Syahputra, Bimo Okta