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CAUCHY: Jurnal Matematika Murni dan Aplikasi
Published by Universitas Islam Negeri Maulana Malik Ibrahim Malang
ISSN : 20860382     EISSN : 24773344     DOI : 10.18860
Core Subject : Education,
Mathematics
Jurnal CAUCHY secara berkala terbit dua (2) kali dalam setahun. Redaksi menerima tulisan ilmiah hasil penelitian, kajian kepustakaan, analisis dan pemecahan permasalahan di bidang Matematika (Aljabar, Analisis, Statistika, Komputasi, dan Terapan). Naskah yang diterima akan dikilas (review) oleh Mitra Bestari (reviewer) untuk dinilai substansi kelayakan naskah. Redaksi berhak mengedit naskah sejauh tidak mengubah substansi inti, hal ini dimaksudkan untuk keseragaman format dan gaya penulisan.
Arjuna Subject : -
Articles 438 Documents
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Mathematical Model and Simulation for the Mechanism of Glucose Uptake in the Cells Kusumastuti, Ari; Irawan, Mohammad Isa; Fahim, Kistosil
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29292

Abstract

Understanding the mechanism of Glucose Transporter 4 (GLUT4) translocation to the cell membrane is essential for describing daily glucose uptake. A normal mechanism maintains glucose homeostasis and reduces the occurrence of Type 2 Diabetes Mellitus (T2DM) and its complications. Kinetic reactions are crucial for revealing the interactions involving proteins, enzymes, and complexes within the system. We propose a system of ordinary differential equations (ODEs) to elucidate the underlying mechanism under the assumption of non-conservative complexes . The insulin signaling pathway, which includes the GLUT4 mechanism, serves as the basis for reconstructing the necessary kinetic reactions. Investigating the behaviour of the model through numerical simulations and dynamics within parameters and initial conditions from relevant researchs .
Systematic Literature Review for Robust Mixed-Integer Linear Programming Models Using Benders Decomposition in Facility Location Problems Haq, Fadiah Hasna Nadiatul; Chaerani, Diah; Triska, Anita
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.31539

Abstract

The robust Mixed-Integer Linear Programming (MILP) model is an approach to address uncertainty in linear optimization involving integer and continuous variables, which can be solved using the Benders Decomposition method. One of its applications is facility location problems, which often face demand, costs, and capacity uncertainties. This article presents a systematic literature review (SLR) on solving robust MILP models using the Benders Decomposition method and its application to facility location problems. The objectives are to explore the state-of-the-art and research trends, identify issues modeled as robust MILP and solved using Benders Decomposition, and determine the most frequently used uncertainty sets. SLR was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method on the Scopus, Science Direct, and Dimensions databases for the last five years of publication, with bibliometric analysis using VOSviewer and RStudio. The results show that there are limited articles that discuss the solution of the robust MILP model on the problem of facility location with the ellipsoidal uncertainty set. In addition, the Benders Decomposition method is widely used to solve robust MILP problems in energy, logistics, supply chains, and scheduling, with interval uncertainty sets being the most common. This topic is an influential theme and has the potential to be explored further.
Spearman Rank Correlation PCA for Mixed Scale Indicator in Structural Equation Modeling Asaliontin, Lisa; Sumarminingsih, Eni; Solimun, Solimun; Ullah, Mohammad Ohid
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29976

Abstract

Structural Equation Modeling (SEM) is a statistical modeling technique that integrates measurement models and structural models simultaneously. In the SEM measurement model, not all latent variables are metric, they can be mixed scales, namely metric and non-metric which have not been widely studied. This study aims to apply the Spearman Rank Correlation Principal Component Analysis (PCA) to handle mixed-scale indicator data in a mixed measurement model (formative and reflective). This method is evaluated on a case study of fertilizer repurchase decisions, resulting in a total determination coefficient of 80%. This shows the flexibility of SEM in handling the complexity of mixed-scale data without sacrificing estimation accuracy. The results showed that the Spearman Rank Correlation PCA was able to store 78.62% of the diversity of data from mixed-scale indicator variables, namely Farmer Demographics (X2). In addition, the results showed that Customer Satisfaction (X1) significantly influenced Repurchase Decisions (Y2) but did not directly affect Customer Engagement (Y1). Farmer Demographics (X2) significantly influences Customer Engagement (Y1) and Repurchase Decisions (Y2), and Customer Engagement has a significant effect on Repurchase Decisions (Y2).
A Combined Truncated Spline and Kernel Semiparametric Path Model Development Rohma, Usriatur; Fernandes, Adji Achmad Rinaldo; Astutik, Suci; Solimun, Solimun
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29849

Abstract

Semiparametric path analysis is a combination of parametric and nonparametric path analysis performed when the linearity assumption in some relationships is not met. In this study, the development of semiparametric path function estimation was carried out by combining two truncated spline and kernel approaches. In addition, the purpose of this study is to determine the significance of function estimation using t-test statistics at the jackknife resampling stage. This research was conducted in 135 Junrejo sub-districts of Batu district.  The results showed that the development of a combined semiparametric path function estimation of truncated spline and kernel with weighted least square allows a more flexible and accurate estimation in modeling waste management behavior patterns. 2. The significance of the best truncated spline nonparametric path estimation in the model of the effect of Environmental Quality and the Use of Waste Banks on the Economic Benefits of Waste through the Use of the 3R Principles using t test statistics at the jackknife resampling stage shows that all exogenous variables have a significant effect on endogenous variables.
Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost Sovia, Nabila Ayunda; Wardhani, Ni Wayan Surya; Sumarminingsih, Eni; Shofa, Elvo Ramadhan
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.30866

Abstract

Classifying imbalanced datasets presents significant challenges, often leading to biased model performance, particularly in multiclass classification. This study addresses these issues by integrating Convolutional Neural Networks (CNN) and XGBoost, leveraging CNN’s exceptional feature extraction capabilities and XGBoost's robust handling of imbalanced data. The Hybrid CNN-XGBoost model was applied to classify cabbage plants affected by pests and diseases, which are categorized into five classes, with a significant imbalance between healthy and affected plants. The dataset, characterized by severe class imbalance, was effectively handled by the proposed model. A comparative analysis demonstrated that the CNN-XGBoost approach, with a Balanced Accuracy of 0.93 compared to 0.53 for the standalone CNN, significantly outperformed the standalone model, particularly for minority class predictions. This approach not only enhances the accuracy of plant disease and pest diagnosis but also provides a practical solution for farmers to efficiently identify and classify cabbage plants, contributing to more effective agricultural management.
Optimization Modeling of Investment Portfolios Using The Mean-VaR Method with Target Return and ARIMA-GARCH Yasmin, Arla Aglia; Riaman, Riaman; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.30042

Abstract

This research develops a portfolio optimization model using the Mean-Value at Risk (Mean-VaR) approach with a target return constraint, addressing the gap in models that specific return objectives. The ARIMA-GARCH model is utilized to predict stock returns and volatility, offering precise inputs for optimization. By applying the Lagrange method and Kuhn-Tucker conditions, the model determines optimal portfolio weights that balance risk and return. Using data from infrastructure stocks on the Indonesia Stock Exchange (January 2019-September 2024), the model’s effectiveness is validated through numerical simulations. The results illustrate efficient frontiers for target returns of 5x10^-6, 0.001, and 0.0019, revealing that higher return targets proportionally increase risk. ARIMA-GACRH’s advantage lies in its ability to capture both mean and variance dynamics, ensuring reliable volatility estimates for informed decision-making. This study contributes to portfolio optimization literature by emphasizing target return constraints and demonstrating the practical utility of volatility modeling. The findings provide a robust framework for investors to align portfolios with financial goals and risk tolerance. Future work could explore broader market contexts or integrated additional constraints for enhanced applicability.
Structural Equation Modeling Semiparametric Truncated Spline in Banking Credit Risk Behavior Models Amanda, Devi Veda; Iriany, Atiek; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29769

Abstract

Housing is one of the primary needs for every individual. Along with the increasing population growth in Indonesia, the need for housing has also experienced a significant surge. This study aims to analyze the effect of customer attitudes on compliance behavior, fear of paying late, and timeliness of payment on Home Ownership Credit (KPR) customers at X Bank. Using a semiparametric Structural Equation Modeling (SEM) approach, this study examines the relationship between these variables to provide a deeper understanding of the factors that influence customer payment behavior. The data used in this study are primary data obtained through questionnaires distributed to 100 Bank X mortgage customers. The results of the analysis show that there is a significant influence between customer attitudes (X1) on obedient payment behavior (Y1) and fear of paying late (Y2), as well as timeliness of payment (Y3). The estimated coefficients obtained show a positive relationship between compliance behavior and timeliness of payment, and a negative relationship between fear of paying late and timeliness of payment, with a p-value 0.001 indicating statistical significance. This finding indicates that good customer attitudes can improve payment timeliness, while poor attitudes can lead to fear of paying late, which in turn can affect payment timeliness.
Simulation Study of Bayesian Zero Inflated Poisson Regression Weni Utomo, Candra Rezzing; Efendi, Achmad; Wardhani, Ni Wayan S.
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.30207

Abstract

Bayesian merupakan salah satu metode estimasi parameter yang dapat diaplikasikan pada ukuran sampel yang kecil. Zero Inflated Poisson merupakan salah satu metode untuk menganalisis data Poisson yang mengalami overdispersion. Tujuan dari penelitian ini adalah untuk mengevaluasi kinerja analisis Zero Inflated Poisson Regression menggunakan Bayesian. Data yang digunakan adalah jumlah kasus campak di Jawa Timur. Campak merupakan penyakit menular yang berpotensi menjadi wabah di berbagai daerah, termasuk Jawa Timur. Terdapat empat variabel prediktor yang digunakan yaitu Jumlah Penduduk (X1), Persentase Vaksinasi (X2), Persentase Penduduk Miskin (X3), dan Persentase Sanitasi Layak (X4), serta satu variabel respon yaitu Jumlah Kasus Campak. Hasil penelitian ini menunjukkan bahwa estimasi model Zero Inflated Poisson (ZIP) menggunakan Bayesian lebih baik dibandingkan estimasi model Zero Inflated Poisson (ZIP) menggunakan MLE. Hal ini dikarenakan data yang digunakan dalam penelitian memiliki sampel yang kecil sehingga estimasi MLE cenderung kurang baik digunakan dalam estimasi parameter. Pemilihan model terbaik dilakukan dengan menggunakan metode Deviance Information Criteria (DIC). Model terbaik ditunjukkan dengan nilai DIC terkecil pada ukuran sampel 100 dan proporsi nol 0,8.
Actuarial Modelling For Diabetes Mellitus Insurance Amrullah, Fauzan Rafi; Kurniawaty, Mila; Fitri, Sa’adatul
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29880

Abstract

Diabetes mellitus is a hereditary disease with an increasing number of cases globally and has become one of the leading causes of death from critical illness in Indonesia. Several researchers have highlighted that genetic and social factors play a significant role in the development of diabetes mellitus. Consequently, financial planning and health insurance are essential. Diabetes health insurance is particularly beneficial for individuals with a family history of diabetes mellitus and individuals with unhealthy lifestyles who may become the first carriers to develop the disease. Some researchers have introduced actuarial models for calculating health insurance premiums and reserves, based on compartmental models used in the study of epidemics and pandemics. In this article, we aim to expand on previous research by applying actuarial models to diabetes mellitus  model, while incorporating genetic and social factors. We hope that this article can serve as a reference for the public in financial planning, such as participating in insurance programs, to maintain financial stability.
Modelling Geographically Weighted Truncated Spline Regression Using Maximum Likelihood Estimation for Human Development Disparities Saris, Laode Muhammad; Pramoedyo, Henny; Fernandes, Adji Achmad Rinaldo
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.31381

Abstract

A development of nonparametric truncated spline regression, Geographically Weighted Regression Spline Truncated (GWSTR) incorporates spatial effects in the modelling of nonlinear relationships between the response and predictor variables. This research utilizes the Maximum Likelihood Estimation (MLE) technique to estimate the parameters of the model. The first-order truncated spline with a single knot yielded a minimal Generalized cross-validation (GCV) value of 1. 729781, suggesting a high level of accuracy in the model.  Four weighting functions were evaluated: Gaussian Kernel, Exponential Kernel, Bi-Square Kernel, and Tri-Cube Kernel. Among these, the Bi-Square weighting function performed the best, achieving a coefficient of determination of 99.999%, demonstrating the model’s capability to explain nearly all data variability effectively. GWSTR proves to be a robust method for capturing complex nonlinear relationships while accounting for spatial variations, making it a valuable tool for spatial data analysis across various disciplines.

Page 32 of 44 | Total Record : 438

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