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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Marisa Rifada
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PEMODELAN RISIKO KEJADIAN DIABETES MELLITUS DAN HIPERTENSI BERDASARKAN REGRESI LOGISTIK BIRESPON Marisa Rifada; Nur Chamidah; Sely Novika Norrachma
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 2 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.678 KB) | DOI: 10.26714/jsunimus.5.2.2017.%p

Abstract

Diabetes dan hipertensi merupakan penyakit yang berhubungan erat. Mereka seringterjadi bersama-sama sehingga dianggap sebagai “komorbiditas” (penyakit yangmungkin ada pada pasien yang sama). Penderita hipertensi dapat mempunyai risikoterkena diabetes. Demikian pula sebaliknya, risiko hipertensi juga dapat dialami oleh penderita diabetes. Untuk melihat seberapa besar pengaruh faktor-faktor yang secara signifikan mempengaruhi peluang kejadian sesorang terkena suatu penyakit akan lebih bermanfaat apabila dirumuskan dalam bentuk matematis. Salah satu analisis statistik yang dapat menggambarkan kejadian tersebut adalah analisis regresi logistik birespon yang merupakan pengembangan dari regresi logistik jika terdapat dua variabel respon biner dengan asumsi ada hubungan yang signifikan antar variabel respon. Berdasarkan analisis data secara deskriptif, diabetes dan hipertensi lebih banyak terjadi pada laki-laki dibandingkan perempuan, serta paling banyak terjadi pada usia 55-64 tahun. Responden yang memiliki Body Mass Index (BMI) > 30 Kg/ m2 cenderung terkena Diabetes.Sedangkan responden yang terkena Hipertensi memiliki BMI antara 25 – 30 Kg/ m. Dalam penelitian ini diperoleh nilai odds ratio () sebesar 1.1454 yang artinyaterdapat dependensi antara kejadian Diabetes dengan Hipertensi. Kata Kunci: Diabetes Mellitus, Hipertensi, Regresi Logistik Biner, Odds ratio.
LEPROSY CASE MODELING IN EAST JAVA USING SPATIAL REGRESSION WITH QUEEN CONTIGUITY WEIGHTING Saifudin, Toha; Rifada, Marisa; Makhbubah, Karina Rubita; Ramadhanty, Devira Thania
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2141-2154

Abstract

Leprosy, a highly contagious disease caused by the bacterium Mycobacterium leprae, can result in permanent disability if left untreated. It remains a significant public health issue in many regions, particularly tropical countries like Indonesia. Despite ongoing control efforts, incidence rates are still high in some areas. In 2023, East Java had the highest number of leprosy cases in Indonesia, with 2,124 out of 7,166. To understand the factors contributing to these cases, this study explores various influences and offers policy recommendations to reduce leprosy in East Java. The study uses spatial modeling with a weighting scheme based on queen contiguity, selected because leprosy spreads through human interactions and movement, creating spatial dependencies. It examines spatial, social, economic, educational, and environmental factors based on cross-sectional data from 38 regencies/cities in East Java for 2023. Among the regression models tested, the spatial error regression model proved most effective, showing an R-Square value of 67.14% and an AIC of 213.023. Key findings identified () average years of schooling and () healthcare worker ratios as significant factors influencing leprosy cases. These results aim to guide policymakers in developing stronger leprosy control strategies and offer a basis for further research in East Java.
Modelling Consumer Price Index Effect on 10-year US Treasury Bond Yields using Least Square Spline Approach Widiyanti, Julia; Salsabila, Safira; Harsanti, Dwika Maya; Amelia, Dita; Rifada, Marisa
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.33020

Abstract

Inflation measured by the Consumer Price Index (CPI) is a critical indicator in the government bond market that directly affects the yields of long-term securities such as the 10-year US Treasury Bond. This study is an explanatory quantitative study that aims to examine the complex dynamics of this relationship using the nonparametric least square spline method. The analysis uses monthly CPI data from FRED and 10-year US Treasury bond yield data from Investing.com for the period 2013-2025. This method divides the data into simple polynomial segments that are smoothly connected at transition points (knots), enabling the modelling of nonlinear patterns without assuming an initial curve shape. The analysis results indicate that a first-degree polynomial spline model (piecewise linear) with three knots successfully represents the bond yield response to inflation shocks with R^2 = 86.48%. Model segmentation identified four regimes: (1) Post-crisis recovery phase, with a negative relationship driven by Fed monetary stimulus suppresing yields despite initial inflation emergence; (2) Policy normalization phase, with a positive relationship aligned with monetary tightening in response to moderate inflation; (3) During the COVID-19 pandemic, a negative relationship due to a surge in demand for safe-haven bonds despite rising inflation; (4) Post-pandemic, the relationship turned positive again following the Fed’s aggressive monetary tightening in response to high global inflation. These findings highlight the urgency of regime-based monitoring for investors and policymakers, while contributing concretely to SDG 8 (decent work and economic growth) through the facilitation of appropriate interest rate policies for sustainable macroeconomic stability, and supporting SDG 9 (industry, innovation, and infrastructure) through the identification of inflation patterns that strengthen shock-resistant infrastructure investment planning and financial innovation during turbulent economic transitions.
Analysis of Unmet Need for Health Services Based on the Percentage of Public Health Complaints with a Kernel Estimator Approach Rifada, Marisa; Amelia, Dita; Setyaningrum, Jeny Praesti; Septiandini, Niswah; Kalista, Yovita Karin; Dwitya, Shabrina Nareswari
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.32555

Abstract

Healthcare services are a fundamental need that governments must guarantee to ensure optimal health outcomes for all citizens. However, many individuals still face significant barriers in accessing necessary healthcare services. This quantitative research employs a spatial analysis to examine the unmet need for health services based on public health complaints, utilizing a nonparametric regression approach with Kernel estimator. The Kernel estimator method was chosen for its flexibility in capturing unstructured data patterns, allowing the analysis to better reflect real-world conditions. The study uses health complaint data from the Central Bureau of Statistics, covering 38 provinces in Indonesia in 2024. However, data from 4 provinces were incomplete, so only 34 provinces were included in the analysis. The independent variable is the percentage of public health complaints, while the dependent variable is the percentage of unmet healthcare needs. A Gaussian kernel function was applied for nonparametric regression, identified as the optimal method based on the lowest Generalized Cross Validation (GCV) value of 1.052939 at a bandwidth of 0.33. The model demonstrates high predictive accuracy, with an R² of 82.44% and a Mean Squared Error (MSE) of 30.7%. These findings provide actionable insights for targeting healthcare disparities and improving service accessibility.
Co-Authors Dita Amelia, Dita Dwitya, Shabrina Nareswari Harsanti, Dwika Maya Kalista, Yovita Karin Makhbubah, Karina Rubita Nur Chamidah Ramadhanty, Devira Thania salsabila, safira Sely Novika Norrachma Septiandini, Niswah Setyaningrum, Jeny Praesti Toha Saifudin Widiyanti, Julia