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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Inferensi
Ni Wayan Surya Wardhani
Brawijaya University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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Ensemble Cluster Method For Clustering Cabbage Production In East Java Maulidya Maghfiro; Ni Wayan Surya Wardhani; Atiek Iriany
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20378

Abstract

Cluster analysis is a multivariate analysis method classified under interdependence methods, where explanatory variables are not differentiated from response variables. The methods used include hierarchical cluster analysis, such as agglomerative and divisive, and non-hierarchical methods such as Self Organizing Maps (SOM) based on Artificial Neural Networks (ANN). Various cluster analysis methods often yield diverse solutions, making it challenging to determine the optimal solution. Therefore, the ensemble cluster method is employed to combine various clustering solutions without considering the initial data characteristics with providing better results. One case study of clustering is the grouping of cabbage production. East Java Province has become the third-highest cabbage-producing province in Indonesia with a production of 210,454 tons. Clustering of cabbage-producing regencies/cities was conducted to optimize production and identify areas that have not yet reached their maximum potential. This study compares five clustering methods which are hierarchical analysis (complete linkage, single linkage, average linkage), Self-Organizing Map (SOM), and Ensemble Cluster. The quality of clustering was evaluated using the Silhouette Coefficient (SC), Dunn Index (DI), and Connectivity Index (CI). The results indicate that the Ensemble Cluster method showed the best performance, with an SC value of 0.9124, a DI value of 1.3734, and a CI value of 2.9290, indicating excellent cluster separation. Therefore, the ensemble cluster method is recommended as the best clustering method in this study.
Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost Nabila Ayunda Sovia; Ni Wayan Surya Wardhani; Eni Sumarminingsih; Elvo Ramadhan Shofa
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.
Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control Laila Nur Azizah; Adji Achmad Rinaldo Fernandes; Ni Wayan Surya Wardhani
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.v10i1.29773

Abstract

Rice pest control is a critical challenge in the agricultural sector that requires a deep understanding of rice pest management. Regression analysis is a statistical method capable of describing and predicting cause-and-effect relationships between individuals. In real-life applications, not all relationships exhibit a known curve pattern, and non-identifiable curve forms are often observed. Additionally, a single cause may affect more than one outcome, and the outcomes themselves can have interrelationships. Such relationships can be approached through a multi-response semiparametric regression using a truncated spline multi-group model. This study aims to develop a multi-response semiparametric multi-group regression model using the truncated spline approach to understand the variables influencing rice pest control under light and dark conditions. This model is applied to secondary and simulated data with various scenarios to determine the best model. The study results indicate that the optimal model for secondary data is a semiparametric regression model with a linear order and a single knot point, achieving a determination coefficient of 89.17%. Simulation results show that the scenario 1 model (linear with a single knot point) produces a high determination coefficient. This multi-response regression model proves more optimal when error variance and multicollinearity levels are kept low to moderate.
Co-Authors Adji Achmad Rinaldo Fernandes Atiek Iriany Elvo Ramadhan Shofa Eni Sumarminingsih Laila Nur Azizah Maulidya Maghfiro Nabila Ayunda Sovia