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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) EXPLORER Bulletin of Data Science Bulletin of Informatics and Data Science
Alkhairi, Putrama
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset Poningsih, Poningsih; Windarto, Agus Perdana; Alkhairi, Putrama
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29509

Abstract

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.
Optimizing brain tumor MRI classification using advanced preprocessing techniques and ensemble learning methods Pardede, Akim Manaor Hara; Zamsuri, Ahmad; Nuroini, Indi; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5106-5119

Abstract

Brain tumor classification is a critical task in medical imaging that directly impacts the accuracy of diagnosis and treatment planning. However, the complexity and variability of magnetic resonance imaging (MRI) images pose significant challenges, often resulting in reduced model reliability and generalization. This study addresses these limitations by proposing a novel ResNet+Bagging model, leveraging the strengths of residual networks and ensemble learning to enhance classification performance. Using publicly available brain tumor MRI datasets, including images labeled as benign, malignant, and normal, the study employs advanced preprocessing techniques such as normalization, data augmentation, and noise reduction to ensure high-quality inputs. The proposed model demonstrated significant improvements, achieving the highest testing accuracy of 72%, outperforming other tested models such as LeNet, standard ResNet, GoogleNet, and VGGNet. Precision (0.6010), recall (0.6000), and F1-score (0.5990) metrics further highlight its superior balance in detecting positive and negative classes. The novelty of this research lies in the application of Bagging to ResNet, which effectively mitigates overfitting and enhances predictive stability in complex medical datasets. These findings underscore the proposed model's potential as a robust solution for brain tumor classification, contributing to more accurate and reliable diagnostics.
Classification Model Optimization using Grid Search and Random Search in Machine Learning Algorithms Parinduri, Syawaluddin Kadafi; Alkhairi, Putrama; Irawan, Irawan; Qurniawan, Hendry
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.136

Abstract

The performance of a machine learning model is highly dependent on the selection and tuning of appropriate hyperparameters. The main problem in this study is how to improve the accuracy and stability of a classification model without sacrificing computational time efficiency, especially in the case of kidney disease classification that requires accurate and fast prediction results. This study aims to optimize the classification model by applying two hyperparameter search methods, namely Grid Search and Random Search, to the Random Forest algorithm. The kidney disease dataset is used as a case study with preprocessing processes including data cleaning, missing value imputation, categorical variable encoding, and normalization. Each model is tested using accuracy, precision, recall, and F1-Score metrics. The results show that the Grid Search_RF model produces the highest performance with perfect accuracy, precision, recall, and F1-Score values (1.0000), while Random Search_RF provides results close to (accuracy 0.9875 and F1-Score 0.9900) with more efficient training time. Meanwhile, the standard Random Forest without tuning still shows competitive performance (accuracy 0.9917 and F1-Score 0.9930). Based on these results, it can be concluded that hyperparameter optimization, using both Grid Search and Random Search, can significantly improve the performance of the classification model, with Random Search being the most efficient method for practical implementation in machine learning-based disease detection systems.
Co-Authors Agus Perdana Windarto Ahmad Zamsuri, Ahmad Ariani, Dhini Asyifah, Hazha Hikmah Batubara, Ela Roza Efendi, Muhamad Masjun Heru Satria Tambunan, Heru Satria Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Indra Riyana Rahadjeng Indra Riyana Rahadjeng Irawan Irawan Muhammad Noor Hasan Siregar Muliadi Nasution, Alisa Putri Amanda Nuroini, Indi Pardede, Akim Manaor Hara Parinduri, Syawaluddin Kadafi Poningsih Poningsih, Poningsih Prihandoko, P Qurniawan, Hendry Raharjo, Mokhamad Ramdhani Rika Rosnelly, Rika Saragih, Farah Yusni Solikhun Solikhun, Solikhun Tambunan, Fazli Nugraha Wanayaumini, W Zakarias Situmorang