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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CAUCHY: Jurnal Matematika Murni dan Aplikasi Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan
Sa’adah, Umu
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Exploring DenseNet architectures with particle swarm optimization: efficient tomato leaf disease detection Lestari, Cynthia Ayu Dwi; Anam, Syaiful; Sa’adah, Umu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1377-1385

Abstract

The critical challenge of tomato leaf disease demands effective solutions surpassing manual detection limitations, ensuring rapid intervention, optimal crop health, and maximizing yield for farmers. DenseNet, a convolutional neural network (CNN) architecture, is lauded for its adept handling of gradient flow issues by extensive interlayer connectivity. Its application holds significant promise in tackling the intricate task of identifying tomato leaf diseases. This research introduces an innovative methodology employing particle swarm optimization (PSO) to fine-tune the DenseNet architecture and hyperparameter. The proposed approach efficiently converges on optimal configurations encompassing parameters, such as the number of layers in dense blocks, growth rates, dropout rates, activation functions, and optimizers tailored for DenseNet. The DenseNet-PSO model achieves remarkable accuracy and precision in classifying various tomato leaf diseases, outperforming alternative architectures in total parameters, computational efficiency, and overall performance compared with six other architecture models. These outcomes elucidate DenseNet-PSO's efficacy in tomato leaf disease detection and demonstrate.
EOQ MODEL FOR DETERIORATING AND AMELIORATING ITEMS UNDER CUBIC DEMAND AND PARTIAL BACKLOGGING Laily, Hafizha Nur; Abusini, Sobri; Sa’adah, Umu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.572 KB) | DOI: 10.30598/barekengvol17iss1pp0021-0028

Abstract

The inventory model aims to determine policies in inventory control. Therefore, the availability needs to be managed as well as possible to obtain optimal performance. This study aimed to produce EOQ models for deteriorating and ameliorating products with shortage and partial backlogging policies. The traditional Economic Order Quantity (EOQ) inventory model was used to develop the model. The search algorithm of the model solution was made to get a solution from the model. In the end, a case study of the model implementation at Minimarket SATUMART, Sidoarjo, is given
Obesity Prediction Using Synthetic Minority Oversampling Technique for Numeric and Continous and XGBoost Approaches Putri, Tiara Azahra Wika; Sa’adah, Umu; Habibah, Ummu
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.v10i1.30818

Abstract

This study investigates the effect of using SMOTE-NC on the XGBoost algorithm in predicting obesity. The main objective of this research is to determine the effect of implementing SMOTE-NC and also the features that are most influential in the prediction process. By using the SMOTE-NC approach with XGBoost it is hoped that it can improve obesity prediction performance, data is collected from UCI Machine Learning for Obesity analysis. The prediction results reveal that the application of SMOTE-NC can improve the accuracy of obesity prediction using XGBoost. The results show that the best accuracy in this study was able to reach 98.30%. Further analysis, this research reviews several influential features in the prediction process, namely Weight, Height and Age. Based on these results, it is hoped that they can contribute to further research. Overall, this research underlines the importance of maintaining health to avoid obesity by keeping body weight within normal limits.
Knowledge Discovery from Confusion Matrix of Pruned CART in Imbalanced Microarray Data Ovarian Cancer Classification Sapitri, Ni Kadek Emik; Sa’adah, Umu; Shofianah, Nur
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.50077

Abstract

Purpose: The results of microarray data analysis is important in cancer diagnosis, especially in early stages asymptomatic cancers like ovarian cancer. One of the challenges in analyzing microarray data is the problem of imbalanced data. Unfortunately, research that carries out cancer classification from microarray data often ignores this challenge, so that it doesn’t use appropriate evaluation metrics. It makes the results biased towards the majority class. This study uses a popular evaluation metric “accuracy” and an evaluation metric that is suitable for imbalanced data “balanced accuracy (BA)” to gain information from the confusion matrix regarding accuracy and BA values in case of ovarian cancer classification.Methods: This study use Classification and Regression Tree (CART) as the classifier. CART optimized by pruning. CART optimal is determined from the results of CART complexity analysis and confusion matrix.Results: The confusion matrix and CART interpretations in this research show that CART with low complexity is still able to predict majority class respondents well. However, when none of the data in the minority class was classified correctly, the accuracy value was still quite high, namely 86.97% and 88.03% respectively at the training and testing stages, while the BA value at both stages was only 50%.Novelty: It is very important to ensure that the evaluation metrics used match the characteristics of the data being processed. This research illustrate the difference between accuracy and BA. It concluded that that classification of an imbalanced dataset without doing resampling can use BA as evaluation metric, because based on the results, BA is more fairly to both classes.
Co-Authors Laily, Hafizha Nur Lestari, Cynthia Ayu Dwi Putri, Tiara Azahra Wika Sapitri, Ni Kadek Emik Shofianah, Nur Sobri Abusini Syaiful Anam Ummu Habibah, Ummu