cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Juhari
Contact Email
juhari@uin-malang.ac.id
Phone
+6281336397956
Journal Mail Official
cauchy@uin-malang.ac.id
Editorial Address
Jalan Gajayana 50 Malang, Jawa Timur, Indonesia 65144 Faximile (+62) 341 558933
Location
Kota malang,
Jawa timur
INDONESIA
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
 40   41   42   43   44 
Modified Ramsey RESET in Combined Truncated Spline–Fourier Nonparametric Path Analysis on Waste Management Behavior Hidayatulloh, Moh Zhafran; Solimun, Solimun; Fernandes, Adji Achmad Rinaldo; Rizqia, Anggun Fadhila; Junianto, Fachira Haneinanda
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.v10i2.37239

Abstract

Nonparametric path analysis is a statistical approach that does not require the functional form of relationships between variables to be known a priori. Classical path analysis assumes linearity, which can be tested using the Ramsey Regression Specification Error Test (RESET). If the linearity test indicates that the relationships between variables are nonlinear, a nonparametric model can be applied. The purpose of this study is to develop a modified Ramsey RESET to identify nonparametric relationships modeled using truncated spline and Fourier series. The modified Ramsey RESET algorithm was successfully implemented to detect the optimal functional form of the nonparametric truncated spline and Fourier series and was subsequently applied to behavioral data on waste management practices. Furthermore, this study proposes an estimator for a hybrid nonparametric path model combining truncated spline and Fourier series approaches. The analysis results reveal that the best model integrates truncated spline with one and two knot points and a Fourier series with one oscillation. The model achieved an adjusted coefficient of determination of 0.956, indicating that it explains 95.6% of the variation in the Behavior of Transforming Waste into Economic Value, while the remaining 4.4% is explained by other unobserved factors outside the model.
Spatial-Temporal Modeling of Regional Sales Using Generalized Space Time Autoregressive (GSTAR): Spillover Effect Analysis Rahayu, Firstyan Deviena Citra; Farahdiansari, Ardana Putri
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.v10i2.36799

Abstract

This study aims to develop a model and forecast product sales in four central provinces on the island of Java using the Generalized Space-Time Autoregressive (GSTAR) method. The data used are monthly sales data from DKI Jakarta, West Java, Central Java, and East Java, covering 12 observation periods. The research stages include testing data stationarity using the Augmented Dickey-Fuller (ADF) test, determining the best model based on the Akaike Information Criterion (AIC) criteria, creating a spatial weight matrix using the inverse distance weighting approach, calculating model parameters using the Ordinary Least Squares (OLS) method, and evaluating model performance using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that the best GSTAR(1,1) model was obtained in the Central Java configuration, with an AIC value of 347.892. This model successfully demonstrated a strong spatial relationship with a real spillover effect, particularly the influence of DKI Jakarta on West Java, which reached 72%. The model accuracy level shows an overall MAPE of 48.84% and a total RMSE of 42.749128, with the best performance in Central Java (MAPE: 38.92%) and East Java (MAPE: 45.23%)/. This study shows that a forecasting model that considers both geographical and time factors simultaneously can be effective
Nonparametric Path Modeling with Double Resampling for Waste Economic Value Utilization: Simulation-Based Performance Comparison Hidayat, Kamelia; Fernandes, Adji Achmad Rinaldo; Iriany, Atiek; Solimun, Solimun; Hidayatulloh, Moh. Zhafran; Junianto, Fachira Haneinanda
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.v10i2.37218

Abstract

Waste generation exceeding landfill capacity highlights the urgency of realizing its economic value. This study analyzes the effect of Quality of Facilities and Infrastructure (X1) and Use of Waste Banks (X2) on Waste Management-Based 3R (Y1) and Waste Economic Value Utilization (Y2) using a truncated spline nonparametric path model. This study evaluates the performance of a nonparametric path analysis model based on truncated spline combined with a double resampling. Data were collected using a Likert scale questionnaire on community perceptions of waste’s economic benefits in Batu City. Simulation results show that the Jackknife-Bootstrap method achieves the lowest average bias (0.058), outperforming single resampling approaches such as Single-Bootstrap (0.178) and Single-Jackknife (0.176). Empirical findings indicate that improvements in the Quality of Facilities and Infrastructure  (X1) and Waste Bank Use (X2) significantly enhance Waste Management Based 3R (Y1) and Utilization of Waste Economic Value (Y2). The truncated spline model reveals a saturation effect, where the marginal benefits of X1 and X2 decrease beyond a threshold. Furthermore, Y1 positively affects Y2, emphasizing the importance of efficient waste management in enhancing economic value. The results support policies promoting balanced infrastructure development, community empowerment, and institutional innovation for sustainable circular economy implementation.
Development of Multigroup Structural Equation Modeling on Structural and Measurement Models For Waste Management Behavior Patterns Khairani, Aldianur; Solimun, Solimun; Fernandes, Adji Achmad Rinaldo; Junianto, Fachira Haneinanda; Khairina, Nadia
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.v10i2.34997

Abstract

This research aims to develop multigroup Structural Equation Modeling (SEM) on structural and measurement models to analyze waste management behavior patterns in Batu City. Secondary data were used from 120 respondents who were grouped into two: Group 1 (away from tourism) and Group 2 (near tourism). Latent variables include environmental quality, waste bank utilization, awareness of the use of 3R, and economic benefits from waste. The analysis was carried out by validity, reliability, linearity (Ramsey RESET), and multigroup SEM. The validity and reliability results showed that all indicators met the criteria (Corrected Item Total Correlation 0.3; Cronbach's Alpha 0.6). The linearity test proves that the relationship between variables is linear. Measurement models using formative indicators showed significant contributions, such as environmental maintenance (Group 1 coefficient: 0.369; Group 2: 0.518) and reuse effectiveness (Group 1 coefficient: 0.555; Group 2: 0.590). In the structural model, environmental quality had a stronger direct effect on 3R awareness in Group 2 (near tourism; coefficient: 0.432), while the use of waste banks had a more effect on Group 1 (away from tourism; coefficient: 0.414). The indirect effects through 3R awareness were also significant, with a total determination coefficient of 0.732, suggesting the model was able to explain 73.2% of the data variance. This study highlights the importance of a location-based approach in waste management policies, particularly the optimization of waste banks in areas far from tourism (Group 1) and the increase of 3R awareness in areas near tourism (Group 2).
Bayesian Geographically Weighted Generalized Poisson Regression Modeling on Maternal Mortality in NTT in 2022 Wijaya, Dewi Ratnasari; Pramoedyo, Henny; Suryawardhani, Ni Wayan
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.v10i2.31626

Abstract

Maternal mortality is a crucial indicator of healthcare quality, particularly in East Nusa Tenggara (NTT) Province, which still records high mortality rates with significant spatial variation. This study aims to model maternal mortality in NTT in 2022 using the Bayesian Geographically Weighted Generalized Poisson Regression (BGWGPR) approach. This method integrates spatial weighting techniques with Bayesian parameter estimation through Gibbs Sampling to address spatial data characterized by overdispersion. Significant factors, including pregnant women's visits to healthcare facilities (K1), were found to influence the distribution of maternal deaths across districts in NTT. The model identifies that visits to healthcare facilities (K1) (X_1) are significant across all regions, while the variable for pregnant women receiving Tetanus Toxoid (X_3) is only significant in Alor and Timor Tengah Selatan. This model not only provides insights into determining factors but also helps identify priority areas for intervention. Therefore, this study contributes to evidence-based health policy-making aimed at reducing maternal mortality in NTT. The BGWGPR approach proves to be relevant for analyzing complex spatial data and can be applied to other epidemiological cases.
Optimization of Palm Oil Distribution Routes Using the Saving Matrix Approach and Genetic Algorithm on Capacitated Vehicle Routing Problem Yuliza, Evi; Andriani, Yuli; Indrawati, Indrawati; Octarina, Sisca; Ramadani, Diah Putri
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.v10i2.36371

Abstract

The transportation of goods and services is a strategic issue in logistics systems, particularly in the palm oil industry. One of the key distribution optimization challenges is the Capacitated Vehicle Routing Problem (CVRP), which involves determining optimal distribution routes while considering vehicle capacity constraints. This study aims to identify the shortest distribution routes for transporting fresh oil palm fruit bunches from collection points to the palm oil mill, with the goal of minimizing total vehicle travel distance. A heuristic approach using the Saving Matrix method and a metaheuristic approach using a Genetic Algorithm were applied separately to two regions: Block P and Block Q, each consisting of 14 collection points with daily distribution schedules. The performance of both algorithms was analyzed and compared in the context of region-based distribution.The results show that the Genetic Algorithm yields more optimal solutions than the Saving Matrix, reducing the total travel distance by 33.92% in Block P and 32.81% in Block Q. In comparison, the Saving Matrix achieved reductions of 38.72% in Block P and 35.25% in Block Q. These findings indicate that the Genetic Algorithm performs better in solving CVRP for the distribution of fresh oil palm fruit bunches and can serve as a foundation for developing more efficient distribution systems using heuristic and metaheuristic approaches
Interpolation of Fire Radiative Power Based on GSTAR Model Predictions with Queen Contiguity Weights Using Ordinary Kriging Fitriyana, Gita; Imro'ah, Nurfitri; Huda, Nur’ainul Miftahul
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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.v11i1.37462

Abstract

Forest fires are a persistent environmental issue in West Kalimantan, Indonesia, driven by both natural and human factors. Fire Radiative Power (FRP) serves as a vital indicator for assessing wildfire intensity and energy release. This study aims to model and predict the spatial temporal dynamics of FRP using the Generalized Space Time Autoregressive [GSTAR(1;1)] model combined with Ordinary Kriging interpolation. The dataset covers West Kalimantan from July 2024 to September 2025, comprising four attributes: observation date, longitude, latitude, and FRP value. Data filtering was applied from the national to provincial level, focusing on three regencies Sanggau, Sekadau, and Ketapang across 14 sub-districts represented by a 1.25×1.25 grid. The data consisted of 65 weekly observations, with 61 used for training and 4 for testing. The GSTAR(1;1) model with a spatial area-based framework achieved an optimal MAPE of 12.63% and satisfied the white noise assumption, indicating reliable performance. Predictions for October 2025 indicated relatively stable fire intensity, with a slight FRP decrease in Nanga Tayap and Sandai during the final week. Overall, the integrated GSTAR–Kriging framework effectively captured both temporal and spatial variations, supporting improved fire risk assessment and regional decision making for wildfire management in West Kalimantan.
Modified of Roots Finding Algorithm of High Degree Polynomials Sanjoyo, Bandung Arry; Yunus, Mahmud; Hidayat, Nurul
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.v10i2.37278

Abstract

Although the Durand-Kerner method is widely used across various fields of computer science, especially in numerical computing, it continues to encounter challenges in locating roots of high-degree polynomials, such as issues with accuracies of roots of the polynomial zeros. Our initial tests and observations on several methods for finding polynomial roots revealed that the roots' accuracy starts to degrade noticeably for polynomials where the degree exceeds 10. Based on considerations of algebraic concepts involving polynomial vector spaces, we introduce an improvement of the Durand-Kerner algorithm aimed at improving root precision. This approach includes targeted refinements in coefficient evaluation, identification of root types, and iterative polishing techniques. We also conducted a comparative evaluation to assess its effectiveness against the original Durand Kerner method and MATLAB's roots() function. Overall, the enhanced algorithm delivers superior accuracy for complex roots—particularly in cases involving multiple zero or integer roots—outperforming both benchmarks, but its execution time increases substantially with polynomial degree.
On the Approximation Capabilities of Deep Neural Networks for Multivariate Time Series Modeling Jamhuri, Mohammad; Irawan, Mohammad Isa; Kusumastuti, Ari; Mondal, Kartick Chandra; Juhari, Juhari
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.v10i2.32760

Abstract

Multivariate time series forecasting plays a crucial role in various domains, including finance, where accurate stock price prediction supports strategic decision-making. Traditional methods such as Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and Vector Autoregression (VAR) often fall short when dealing with complex, non-linear data—particularly those exhibiting long-term temporal dependencies. This study evaluates deep learning approaches, namely Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), using daily AAPL stock price data from January 2020 to November 2024. The results show that the MLP model with a 10-day time window achieves the best accuracy, yielding lower values in Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) compared to CNN, LSTM, and VAR. The findings suggest that MLP is particularly effective in capturing complex patterns in multivariate time series forecasting.
Empirical Evaluation of Wavelet Filter and Wavelet Decomposition Level on Time Series Forecasting Andriyani, Mira; S., Dewi Retno Sari
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.v10i2.36440

Abstract

Time series forecasting is essential for anticipating future outcomes and supporting decision-making, yet achieving high predictive accuracy remains challenging. Wavelet-based approaches, particularly the Maximal Overlap Discrete Wavelet Transform (MODWT), offer potential improvements, although limited studies have systematically compared wavelet filter types and decomposition levels. This study evaluates several wavelet filters and decomposition levels combined with ARIMA models across six datasets exhibiting varying temporal characteristics. Forecasting accuracy was measured using the Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). For the datasets analyzed, the Haar filter yielded the lowest MAE and SMAPE values, a result supported by the Kruskal–Wallis test and Dunn's test, which indicated significant differences in accuracy across filters. In contrast, differences in decomposition levels were not statistically significant, suggesting that decomposition level played a limited role in forecasting performance within this dataset context. These findings provide empirical, dataset-specific evidence regarding filter selection in MODWT–ARIMA modeling and highlight the comparatively minor influence of decomposition level on forecasting accuracy.

Page 42 of 44 | Total Record : 438

 40   41   42   43   44 

Filter by Year

2009 2026


Reset
Filter By Issues
All Issue Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 9, No 2 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 8, No 2 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi (May 2022) (Issue in Progress) Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 1 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 6, No 4 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 6, No 3 (2020): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 6, No 2 (2020): CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 6, No 1 (2019): CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 5, No 4 (2019): CAUCHY Vol 5, No 4 (2019): CAUCHY Vol 5, No 3 (2018): CAUCHY Vol 5, No 3 (2018): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 5, No 2 (2018): CAUCHY Vol 5, No 1 (2017): CAUCHY Vol 4, No 4 (2017): CAUCHY Vol 4, No 3 (2016): CAUCHY Vol 4, No 2 (2016): CAUCHY Vol 4, No 1 (2015): CAUCHY Vol 3, No 4 (2015): CAUCHY Vol 3, No 3 (2014): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 3, No 3 (2014): CAUCHY Vol 3, No 2 (2014): CAUCHY Vol 3, No 2 (2014): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI Vol 3, No 1 (2013): CAUCHY Vol 3, No 1 (2013): Cauchy Vol 2, No 4 (2013): CAUCHY Vol 2, No 3 (2012): CAUCHY Vol 2, No 2 (2012): CAUCHY Vol 2, No 1 (2011): CAUCHY Vol 1, No 4 (2011): CAUCHY Vol 1, No 2 (2010): CAUCHY Vol 1, No 1 (2009): CAUCHY More Issue