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All Journal Journal of Soft Computing Exploration International Journal of Informatics Engineering and Computing
Endah H, Marselina
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Computer Science & IT

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Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks Endah H, Marselina; Wijaya , R. Nurhadi; Khotibul Ahsan, Hilmi
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.226

Abstract

Cirrhosis, a prevalent and life-threatening liver condition, demands early detection for effective intervention. This study investigates the potential of machine learning algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in cirrhosis prediction using a dataset from Kaggle containing 418 observations and 20 attributes. Performance evaluation involves metrics like accuracy, precision, recall, and F1-score, revealing CNN's superior performance with an 84% accuracy rate. The study highlights the importance of algorithm selection and feature engineering in medical diagnosis. Moreover, a comparison with traditional machine learning techniques underscores CNN's prowess in this domain. Beyond cirrhosis, CNNs offer promise for automating feature extraction from medical imagery and recognizing complex patterns, potentially transforming diagnostic accuracy in healthcare.
Comparison of Hybrid CNN-LSTM, LSTM, and CNN Models for Stock Price Prediction (Case Study: PT. Indofood Sukses Makmur Tbk) Wijaya S, R. Nurhadi; Endah H, Marselina; Wanda, Putra; Rafitajudin, Rafitajudin
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/sdts5v08

Abstract

This study develops a hybrid deep learning model by combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to predict the stock price of INDF.JK using historical data from 2015 to 2025. Feature extraction is performed using Conv1D, followed by MaxPooling1D to reduce dimensions, and LSTM to capture time-dependent patterns. The model is evaluated using the R², RMSE, MAE, and MAPE metrics. The CNN-LSTM model demonstrates the best performance with an R² of 0.9759, RMSE of 87.77, MAE of 63.97, and MAPE of 1.02%. As a comparison, the single CNN model produced an R² of 0.9711, RMSE of 96.18, MAE of 71.16, and MAPE of 1.12%, while the single LSTM model obtained an R² of 0.9752, RMSE of 89.13, MAE of 66.99, and MAPE of 1.07%. These results confirm that the hybrid approach is superior in terms of stock price prediction accuracy compared to the use of a single model.
Co-Authors Khotibul Ahsan, Hilmi Putra Wanda, Putra Rafitajudin, Rafitajudin Wijaya , R. Nurhadi Wijaya S, R. Nurhadi