Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi ComTech: Computer, Mathematics and Engineering Applications
Eni Sumarminingsih
Brawijaya University
Computer Science & IT Engineering Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Fuzzy C-Means in Content-Based Document Clustering for Grouping General Websites Based on Their Main Page Contents Sri Probo Aditiyo; Eni Sumarminingsih; Rahma Fitriani
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 2 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i2.9732

Abstract

The research aimed to use Fuzzy C-Means clustering in content-based document clustering to classify general websites based on their content. The data used were a table ranking of the most visited websites for Indonesia, taken from https://dataforseo.com/top-1000-websites/ on September 24th, 2022. The research was conducted with two different cases using Fuzzy C-Means clustering, which had two different iteration parameter values, namely 100 and 200 in maximum iteration. The research results on Fuzzy C-Means clustering in content-based document clustering are based on the two cases. These different maximum iteration parameters result in a different amount of website name data in the cluster. They are formed in the first and second clusters only. However, in the other clusters, the numbers are all the same. The results of the cluster research are validated using the silhouette coefficient, with case no. 1 and no. 2 values being 0,977783879 and 0,977788457. The use of Fuzzy C-Means clustering in content-based document clustering has an excellent performance when this method is applied to group general websites based on their content. With that result, content-based clustering can be also applied in other cases. Hence, the results can be considered to be applied to other cases for content-based clustering in the future.
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, Maulana Malik Ibrahim State Islamic University of 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.
Co-Authors Elvo Ramadhan Shofa Nabila Ayunda Sovia Ni Wayan Surya Wardhani Rahma Fitriani Sri Probo Aditiyo