Articles
<![CDATA[SEARCHING FOR A MORAL CHARACTER: THE GENESIS OF THE AUDITOR'S DUTY ]]>
<![CDATA[Budisusetyo, Sasongko]]>;
<![CDATA[Subroto, Bambang]]>;
<![CDATA[Rosidi, Rosidi]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ Journal of Economics, Business, and Accountancy Ventura Vol. 16 No. 3 (2013): December 2013 ]]>
Publisher : <![CDATA[Universitas Hayam Wuruk Perbanas ]]>
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DOI: 10.14414/jebav.v16i3.228
<![CDATA[Frequently, questions are asked to the accounting profession in the face of ethical dilemmas such as how auditors should behave. Many studies have shown moral character is important in ethical judgment, but there is very little explanation about the moral character of its own. This study aimed to test empirically the effect of individual personality factors, such as moral character variables comprising the dimensions of spirituality, idealism, moral courage, and perspective taking in the ethical judgment. Research data was obtained by distributing questionnaires to the auditor in Surabaya and Jakarta. Auditors' ethical decision-making is measured by making a story of ethical scenarios. Furthermore, the data were analyzed using software WarpPLS. This study shows importance of moral character in an auditor's ethical decision. This study shows that being an accountant is a choice being a noble human being and not a mere pursuit of economic benefits.]]>
<![CDATA[Development of Semiparametric Smoothing Spline Path Analysis on Cashless Society ]]>
<![CDATA[Nurdin, Muhammad Rafi Hasan]]>;
<![CDATA[Ullah, Muhammad Ohid]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Sumarminingsih, Eni]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i1.29846
<![CDATA[Path analysis requires assumptions to be met, particularly the linearity assumption, which can be tested using the Ramsey Regression Specification Error Test (RESET). Parametric path analysis is appropriate when all variable relationships are linear. For entirely non-linear relationships, a nonparametric model can be used, while a semiparametric model applies if there is a mix of linear and non-linear relationships. One nonparametric method is spline smoothing, which requires determining the spline polynomial order in estimating the nonparametric path function. Determining the spline polynomial order is challenging because there is no standard test for it. This study thus develops a modified Ramsey RESET to identify the optimal spline smoothing order. The development involves modifying the second regression equation with a nonparametric spline smoothing regression of orders 2 to 5. The modified Ramsey RESET algorithm is applied to cashless data, and the results are used to estimate a multi-group semiparametric smoothing spline function with a dummy variable approach. This estimation yields a goodness of fit of 94.14%, indicating that Product Quality and the Moderating Effect of Cashless Usage Frequency can explain Cashless User Satisfaction and Cashless User Loyalty by 94.14%, with the remaining 5.86% explained by variables outside the research model]]>
<![CDATA[Development of Semiparametric Truncated Spline Logistic Path Analysis ]]>
<![CDATA[Rejeki, Sasi Wilujeng Sri]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Nurjannah, Nurjannah]]>;
<![CDATA[Yulianto, Shalsa Amalia]]>;
<![CDATA[Ullah, Muhammad Ohid]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i1.29979
<![CDATA[Logistic path analysis extends logistic regression by incorporating intervening variables, addressing the limitations of linearity assumptions through nonparametric models like spline regression. However, this study develops a semiparametric truncated spline logistic path analysis to accommodate linear and nonlinear relationships, considering direct and indirect effects of intervening variables. The model is applied to analyze the impact of price volatility and human resource quality on farmer welfare, with farmer productivity as an intervening variable. It assumes a nonlinear relationship between price volatility and productivity/welfare, while other relationships are linear. This development was applied to secondary data collected through questionnaires from farmer group members in Bali Province, which were analyzed using a semiparametric truncated spline logistic path model. Optimal knots were determined using the lowest GCV value. The results show that the model effectively captures changes in data patterns, providing robust parameter estimates. Hypothesis testing highlights significant differences in the effectiveness of linear and nonlinear relationships. The use of truncated splines offers critical insights into variable interactions and enhances model reliability, making it a valuable tool for analyzing complex agricultural systems and informing policies to improve farmer welfare and productivity.]]>
<![CDATA[Integration of DBSCAN Cluster Analysis with Multigroup Moderation Path Analysis ]]>
<![CDATA[Al Jauhar, Hafizh Syihabuddin]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Fitriani, Rahma]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i1.29847
<![CDATA[This study examines the application of integration between DBSCAN cluster analysis and multigroup moderation path analysis to analyse patterns of waste management behaviour in Batu City. DBSCAN was used to cluster the data based on density, resulting in two main clusters as well as some noise data. The first cluster consisted of 189 respondents, while the second cluster included 196 respondents, with the remaining 10 data identified as noise. The DBSCAN clustering results showed a silhouette index of 0.664, indicating good clustering quality in terms of compactness and separation between clusters. After the data was clustered, each cluster was analysed using multigroup moderation path analysis to assess the relationship between environmental quality, understanding of 3R-based waste management, and economic usefulness of waste with facilities and infrastructure variables as moderators. The results showed that clusters with good quality facilities had a stronger understanding of 3R-based waste management and its economic usefulness. This finding underscores the importance of facilities and infrastructure in influencing community waste management behaviour patterns.]]>
<![CDATA[Spearman Rank Correlation PCA for Mixed Scale Indicator in Structural Equation Modeling ]]>
<![CDATA[Asaliontin, Lisa]]>;
<![CDATA[Sumarminingsih, Eni]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Ullah, Mohammad Ohid]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i1.29976
<![CDATA[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).]]>
<![CDATA[A Combined Truncated Spline and Kernel Semiparametric Path Model Development ]]>
<![CDATA[Rohma, Usriatur]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Astutik, Suci]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i1.29849
<![CDATA[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.]]>
<![CDATA[Structural Equation Modeling Semiparametric Truncated Spline in Banking Credit Risk Behavior Models ]]>
<![CDATA[Amanda, Devi Veda]]>;
<![CDATA[Iriany, Atiek]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i1.29769
<![CDATA[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.]]>
<![CDATA[Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration ]]>
<![CDATA[Gultom, Fandi Rezian Pratama]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Nurjannah, Nurjannah]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v6i4.8617
<![CDATA[Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant growth. The purpose this study is to apply the bootstrap resampling technique in Gompertz growth model which overcomes residual distribution with small samples, the research data was taken from soybean plant growth in four varieties with four spacing treatments, five replications and twelve weeks (long planting period). Gompertz growth model uses nonlinear least squares method in estimating parameters with Levenberg–Marquardt iteration. The value of the Gompertz model after resampling bootstrap has no significant difference. The adjusted R2 value of 0.96 is close to 1. This means that the total diversity of plant heights can be explained by the Gompertz model of 96 percent. Judging from the graph of predictions of soybean plant growth before resampling and after resampling coincide with each other it can also be seen in the initial growth values before resampling 14, 05 and 14.18, the maximum growth values are 55.13 and 55.60. Bootsrap resampling technique can overcome residual normality in the Gompertz growth model, but does not change the information in the initial data.]]>
<![CDATA[Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion ]]>
<![CDATA[Balqis, Nabila Azarin]]>;
<![CDATA[Astutik, Suci]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v6i4.10223
<![CDATA[This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases. ]]>
<![CDATA[Penguatan Prestasi OSN Matematika SMP dan SMA melalui Kolaborasi Guru dan Siswa: Studi pada SMA Sumber Putih Kabupaten Malang ]]>
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Hapsari, Meilina Retno]]>;
<![CDATA[Mudjiono, Mudjiono]]>;
<![CDATA[Lusiana, Evellin Dewi]]>;
<![CDATA[Hidayat, Kamelia]]>;
<![CDATA[Sianipar, Celia]]>;
<![CDATA[Zahra, Septi Nafisa Ulluya]]>
<![CDATA[ Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2025 Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2025) ]]>
Publisher : <![CDATA[Universitas Harapan Bangsa ]]>
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DOI: 10.35960/snppkm.v4i1.1332
<![CDATA[This community service program was designed to enhance the achievement of the National Science Olympiad in Mathematics through collaboration between teachers and students in Malang Regency. The background of this program lies in the limited availability of structured coaching, insufficient intensive mentoring by teachers, and the lack of competition simulations that hinder students’ academic readiness and mental resilience. The objective was to strengthen teacher competence and prepare students more effectively for the demands of Olympiad-level competitions. The program was implemented through seminars for teachers, intensive classes for students, collaborative discussion forums, competition simulations, and continuous evaluation using statistical-based monitoring. The results showed that teachers developed stronger skills in designing innovative and systematic coaching strategies, while students demonstrated improved readiness in facing challenging problems and competition pressure. The program also fostered a collaborative and participatory learning ecosystem that supports sustainable academic development. In conclusion, this initiative contributed to improving the quality of mathematics education in Malang Regency and provided a replicable model for Olympiad coaching in other schools and regions.]]>
