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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Penelitian Pendidikan IPA (JPPIPA) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Economics Development Analysis Journal
Eni Sumarminingsih
brawijaya university
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Journal : CAUCHY: Jurnal Matematika Murni dan Aplikasi

 1 
Development of Semiparametric Smoothing Spline Path Analysis on Cashless Society Nurdin, Muhammad Rafi Hasan; Ullah, Muhammad Ohid; Fernandes, Adji Achmad Rinaldo; Sumarminingsih, Eni; 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.v10i1.29846

Abstract

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
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).
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.
Hyperparameter Optimization Approach in GRU Model: A Case Study of Rainfall Prediction in DKI Jakarta Mashfia, Fidia Raaihatul; Astutik, Suci; Sumarminingsih, Eni
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.v10i1.32277

Abstract

Rainfall is a crucial factor in water resource management and disaster mitigation. This study develops a rainfall prediction model for DKI Jakarta using a Gated Recurrent Unit (GRU) with hyperparameter optimization to enhance prediction accuracy. Daily rainfall data is processed using a sliding window technique, where 30 days of historical data serve as input to predict rainfall on the 31st day. The model is trained with various configurations of batch sizes and the number of neurons in hidden layers to determine the optimal performance. The results of hyperparameter tuning show that the batch size configuration of 64, hidden layer 1 with 32 neurons, and hidden layer 2 with 128 neurons produces an MAE of 6.66 and an RMSE of 13.94. The model is quite good at capturing daily rainfall patterns but still has difficulty in predicting extreme rainfall spikes
Co-Authors Achmad Efendi Ani Budi Astuti Asaliontin, Lisa Atiek Iriany Elfrida Ratnawati Fernandes, Adji Fernandes, Adji Achmad Rinaldo Hamdan, Rosita Henny Pramoedyo Iriany , Atiek Kalangi, Olyvia Maria Lusia, Dwi Ayu Mashfia, Fidia Raaihatul Melanwati, Rinda Lolita Menufandu, Dahlia Gladiola Rurina Muhammad Nurdin Ni Wayan Surya Wardhani Nurdin, Muhammad Rafi Hasan Rahma Fitriani Rotchildi, Gusti Ayu Putu Rawi Sa'diyah, Nur Kamilah Semathea, Karen Sepriadi, Hanifa Shofa, Elvo Ramadhan Solimun Solimun, Solimun Sovia, Nabila Ayunda Suci Astutik Ullah, Mohammad Ohid Ullah, Muhammad Ullah, Muhammad Ohid Waego Hadi Nugroho Wahyuni, Djihan