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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 6 No. 4 (2024): September 2024" : 5 Documents clear
Enhancing Obesity Risk Classification: Tackling Data Imbalance with SMOTE and Deep Learning Syofian, Muhammad; Maulana, Ilham
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3011.529 KB) | DOI: 10.34288/jri.v6i4.349

Abstract

Data imbalance is a significant challenge in classification models, often leading to suboptimal performance, especially for minority classes. This study explores the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification model performance by balancing data distribution. The evaluation was conducted using a confusion matrix to measure prediction accuracy for each class. The results indicate that SMOTE successfully enhances minority class representation and improves prediction balance, although some misclassifications remain. Therefore, in addition to oversampling, additional approaches such as class weighting or ensemble learning are required to further improve model accuracy. This study provides deeper insights into the role of SMOTE in addressing data imbalance and its impact on classification model performance.
EXAMINATION OF MANGO FRUIT DISEASES TO IMPROVE THE QUALITY OF MANGO FRUIT USING IMAGE PROCESSING Budi Aji, Indra
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.347

Abstract

Stroke occurs due to disrupted blood flow to the brain, either from a blood clot (ischemic) or a ruptured blood vessel (hemorrhagic), leading to brain tissue damage and neurological dysfunction. It remains a leading cause of death and disability worldwide, making early prediction crucial for timely intervention. This study evaluates the impact of data balancing techniques on stroke prediction performance across different machine learning models. Random Forest (RF) consistently achieves the highest accuracy (98%) but struggles with precision and recall variations depending on the balancing method. Decision Tree (DT) and K-Nearest Neighbors (KNN) benefit most from SMOTE and SMOTETomek, improving their F1-scores (11.21% and 9.18%), indicating better balance between precision and recall. Random Under Sampling enhances recall across all models but reduces precision, leading to lower overall predictive reliability. SMOTE and SMOTETomek emerge as the most effective balancing techniques, particularly for DT and KNN, while RF remains the most accurate but requires further optimization to improve precision and recall balance.
Machine Learning for Stroke Prediction: Evaluating the Effectiveness of Data Balancing Approaches Muhamad Indra; Siti Ernawati; Ilham Maulana
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.344

Abstract

Stroke occurs due to disrupted blood flow to the brain, either from a blood clot (ischemic) or a ruptured blood vessel (hemorrhagic), leading to brain tissue damage and neurological dysfunction. It remains a leading cause of death and disability worldwide, making early prediction crucial for timely intervention. This study evaluates the impact of data balancing techniques on stroke prediction performance across different machine learning models. Random Forest (RF) consistently achieves the highest accuracy (98%) but struggles with precision and recall variations depending on the balancing method. Decision Tree (DT) and K-Nearest Neighbors (KNN) benefit most from SMOTE and SMOTETomek, improving their F1-scores (11.21% and 9.18%), indicating better balance between precision and recall. Random Under Sampling enhances recall across all models but reduces precision, leading to lower overall predictive reliability. SMOTE and SMOTETomek emerge as the most effective balancing techniques, particularly for DT and KNN, while RF remains the most accurate but requires further optimization to improve precision and recall balance.
IMPROVING IMAGE CLASSIFICATION ACCURACY WITH OVERSAMPLING AND DATA AUGMENTATION USING DEEP LEARNING: A CASE STUDY ON THE SIMPSONS CHARACTERS DATASET Maulana, Ilham; Ernawati, Siti; Indra, Muhammad
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.348

Abstract

The issue of data imbalance in image classification often hinders deep learning models from making accurate predictions, especially for minority classes. This study introduces AugOS-CNN (Augmentation and Over Sampling with CNN), a novel approach that combines oversampling and data augmentation techniques to address data imbalance. The The Simpsons Characters dataset is used in this study, featuring five main character classes: Bart, Homer, Agnes, Carl, and Apu. The number of samples in each class is balanced to 2,067 using an augmentation method based on Augmentor. The proposed model integrates oversampling and augmentation steps with a Convolutional Neural Network (CNN) architecture to improve classification accuracy. Evaluation results show that the AugOS-CNN model achieves the highest accuracy of 96%, outperforming the baseline CNN approach without data balancing techniques, which only reaches 91%. These findings demonstrate that the AugOS-CNN model effectively enhances image classification performance on datasets with imbalanced class distributions, contributing to the development of more robust deep learning methods for addressing data imbalance issues.
Optimizing Deep Learning with Dimensionality Reduction for Analyzing the CuMiDa Brain Cancer Gene Expression Dataset Duwi Lufita Marfiana; Asmita Rani, Fatimah
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.350

Abstract

In the digital era, machine learning and deep learning have become indispensable tools for bioinformatics, particularly in analyzing high-dimensional gene expression data for cancer diagnosis and classification. This study leverages the CuMiDa brain cancer dataset, a curated microarray database with 54,676 genes and 130 samples, to evaluate the effectiveness of deep learning models integrated with dimensionality reduction techniques. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (TruncatedSVD) were employed to address the challenges of high-dimensional data, reducing noise and computational complexity. Three deep learning models—DNN, MLP, and TabNet—were implemented with various optimizers, including ADAM, RMSprop, and SGD. Results showed that TruncatedSVD outperformed PCA in minimizing loss, especially for MLP with LBFGS optimizers, achieving near-zero loss. TabNet demonstrated the highest classification accuracy (96%) with ADAM and RMSprop. Conversely, SGD exhibited suboptimal performance across models. These findings highlight the critical role of dimensionality reduction and optimizer selection in enhancing the efficiency and accuracy of deep learning models for cancer classification. This research provides a robust framework for improving diagnostic tools in computational oncology.

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