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All Journal Jurnal Mahasiswa Statistik Inferensi
Wardhani, Ni Wayan Surya
brawijaya university
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Social Sciences

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PERBANDINGAN MODEL RICHARDS DAN CHAPMAN-RICHARDS PADA PERTUMBUHAN TANAMAN TEBU (Saccharum officinarum Linn) Wijayanto, Andik Dwi; Wardhani, Ni Wayan Surya
Jurnal Mahasiswa Statistik Vol 2, No 6 (2014)
Publisher : Jurnal Mahasiswa Statistik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.001 KB)

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Hybrid CNN-SVM with Borderline SMOTE for Imbalance Class Cabbage Plants Sovia, Nabila Ayunda; Wardhani, Ni Wayan Surya; Sumarminingsih, Eni
Inferensi Vol 7, No 3 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i3.20514

Abstract

Cabbage farming is highly vulnerable to diseases and pests, leading to substantial yield losses if not properly managed. Traditional diagnostic methods, reliant on manual assessment, are often time-consuming and inaccurate. This study introduces a hybrid approach combining Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to address these challenges, specifically focusing on improving classification accuracy in imbalanced cabbage image datasets. CNNs are leveraged for their powerful feature extraction, while SVM, optimized using a One-vs-All strategy, enhances multi-class classification. To handle data imbalance, Borderline SMOTE (Synthetic Minority Over-sampling Technique) is applied, generating synthetic samples to balance underrepresented classes. The SqueezeNet architecture is employed for feature extraction, with SVM hyperparameters fine-tuned via grid search. Results demonstrate that the integration of CNN, SVM, and Borderline SMOTE significantly improves classification performance, particularly for minority classes, achieving an accuracy of 99%. This approach offers a more reliable and efficient tool for early detection of cabbage diseases and pests, contributing to better agricultural management and reduced crop losses.
Ensemble Cluster Method For Clustering Cabbage Production In East Java Maghfiro, Maulidya; Wardhani, Ni Wayan Surya; Iriany, Atiek
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.
Co-Authors Atiek Iriany Maghfiro, Maulidya Sovia, Nabila Ayunda Sumarminingsih, Eni Wijayanto, Andik Dwi