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All Journal JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research
Badriah Nursakinah
Universitas Pamulang, Tangerang Selatan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasifikasi Citra Medis Penyakit Pneumonia dengan Metode Convotional Neural Network Khairudin Khairudin; Bobi Agustian; Badriah Nursakinah
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.576

Abstract

Pneumonia is a pulmonary infection that remains one of the leading causes of death among children under five, especially in developing countries. Early detection and rapid diagnosis are critical in managing this disease, particularly in regions with limited access to medical professionals. This study aims to develop an automatic classification system for pediatric chest X-ray images using the Convolutional Neural Network (CNN) method to detect pneumonia. The dataset used consists of 5,863 pediatric chest X-ray images categorized into two classes: Pneumonia and Normal. The images underwent preprocessing stages including resizing, normalization, augmentation, and noise removal. The CNN architecture includes stacked convolutional layers, max pooling, dropout, and a fully connected layer with sigmoid activation. The model was trained using 80% of the data for training, 10% for validation, and 10% for testing. Performance was evaluated using accuracy, precision, recall, and F1-score metrics. Evaluation results showed that the model achieved over 93% accuracy, with 92.5% precision, 94.2% recall, and an F1-score of 93.3%. Transfer learning using pretrained models (VGG16 and ResNet50) further improved performance. These findings demonstrate that CNN is an effective tool for medical image classification and has strong potential to support fast and accurate pneumonia diagnosis, especially in resource-limited healthcare settings.
Predict Goods Demand Using the XGBoost Method Based on Sales Historical Data Badriah Nursakinah; Nurhalimah; Yuda Samudra
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9584

Abstract

Predicting the demand for goods is an important aspect of inventory management and operational planning because inaccurate predictions can lead to overstock or shortages of goods. This study aims to predict the demand for goods using the Extreme Gradient Boosting (XGBoost) algorithm based on historical sales data. The dataset used contains information on the transaction date, number of sales, stock, price, and time index, which is then processed through the preprocessing and feature engineering stages, including the formation of temporal features and sales lag features. Data sharing is carried out using a time series split approach to maintain the chronological order of the data. The XGBoost model is optimized using GridSearchCV with the TimeSeriesSplit validation scheme. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results showed that the model produced an MAE score of 54.13 and an RMSE of 77.60, while a SMAPE score of 43.13% showed an acceptable relative error rate in highly fluctuating sales data. Feature importance analysis shows that previous period (lag_1) sales and weekly patterns are the most dominant factors in demand predictions. These results prove that XGBoost is effectively used for historical data-driven demand prediction of goods and has the potential to support inventory management decision-making.
Co-Authors Bobi Agustian Khairudin Khairudin Nurhalimah Yuda Samudra