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Science in Information Technology Letters
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27224139     DOI : https://doi.org/10.31763/SiTech
Core Subject : Science,
Computer Science & IT
Science in Information Technology Letters (SITech) aims to keep abreast of the current development and innovation in the area of Science in Information Technology as well as providing an engaging platform for scientists and engineers throughout the world to share research results in related disciplines. SITech is a peer reviewed open-access journal which covers four (4) majors areas of research that includes 1) Artificial Intelligence, 2) Communication and Information System, 3) Software Engineering, and 4) Business intelligence Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers. Finally, accepted and published papers will be freely accessed in this website.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol 6, No 1 (2025): May 2025" : 4 Documents clear
Machine learning-based residential load demand forecasting: Evaluating ELM, XGBoost, RF, and SVM for enhanced energy system and sustainability Abdalla, Modawy Adam Ali; Ishaga, Ahmed Mohamed; Osman, Hassan Ahmed; Elhindi, Mohamed; Ibrahim, Nasreldin; Snani, Aissa; Hamid, Gomaa Haroun Ali; Hammad, Abdallah
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.1866

Abstract

Accurate forecasting of electrical power load is essential for properly planning, operating, and integrating energy systems to accommodate renewables and achieve environmental sustainability. Therefore, this study introduces different machine learning (ML) methods, including support vector machines (SVM), random forests (RF), extreme learning machines (ELM), and extreme gradient boosting (XGBoost) to predict hourly electricity demand using electricity consumption and temperature data for train and test ML models. The data is processed by autocorrelation function (ACF) and cross-correlation function (CCF) to determine the appropriate lag time for the inputs. Furthermore, ML model accuracy is assessed using coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Results show that the ELM model achieved the highest R² in both summer (0.971) and winter (0.868), outperforming the other models in accuracy R² and error reduction (MAE and RMSE). ELM also more effectively captured load fluctuations. The result of this research has applications for load demand forecasting in the proper planning and operation of the residential grid. The results help estimate load demand and provide useful guidance for residential grid planning and management by determining the best techniques for precisely estimating load demand and identifying domestic energy consumption patterns
Optimizing breast cancer classification using SMOTE, Boruta, and XGBoost Hardiyanti P, Cicin
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.2109

Abstract

Breast cancer remains one of the leading causes of death among women worldwide. This study aims to develop a clinical data-based breast cancer classification framework by integrating the Synthetic Minority Oversampling Technique (SMOTE), the Boruta feature selection algorithm, and the XGBoost classifier. The proposed approach is tested using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, consisting of 569 samples and 30 numerical features. SMOTE addresses class imbalance, Boruta selects the most relevant diagnostic features, and XGBoost is the main classification algorithm due to its tabular and imbalanced data robustness. Model validation is conducted through Repeated Stratified K-Fold Cross Validation with 30 repetitions to ensure statistical stability. The resulting model achieves excellent classification performance, with an average accuracy of 0.9608 ± 0.0274, precision of 0.9465 ± 0.0481, Recall of 0.9512 ± 0.0524, and F1-score of 0.9475 ± 0.0374. The ROC-AUC value reaches 0.9926 ± 0.0094, the PR-AUC is 0.9906 ± 0.0113, and the Matthews Correlation Coefficient (MCC) is 0.9179 ± 0.0575, indicating a well-balanced model. Clinically, this model can aid early diagnosis by effectively reducing irrelevant diagnostic attributes, retaining only 10 key features without compromising accuracy, thereby offering a lightweight yet reliable diagnostic tool. However, limitations include the relatively small dataset and the absence of hyperparameter tuning. Future research should explore larger datasets, advanced ensemble methods, and interpretability techniques such as SHAP or LIME to improve clinical transparency and adoption.
Classification of coronary heart disease using the multi-layer perceptron neural networks Ikhwandoko, Fatih; Ismi, Dewi Pramudi
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.2186

Abstract

Coronary heart disease (CHD) is one of the leading causes of death worldwide. The complexity of risk factors such as blood pressure, cholesterol, smoking history, and unhealthy lifestyles often makes the diagnosis process less effective. With the increasing need for fast and accurate heart disease prediction systems, the use of artificial intelligence-based methods such as Neural Networks is a promising solution. This study aims to evaluate the ability of the Multi-Layer Perceptron (MLP) algorithm to classify CHD risk using the Framingham Heart Study dataset, while comparing it with other commonly used classification methods. This research used the collection of Framingham heart disease data containing 15 medical features. The data was then processed through cleaning, normalization, and class balancing using the SMOTE method. An MLP model was designed with two hidden layers using 200 and 128 neuron architectures, and tested in three training and testing data split scenarios (70:30, 75:25, and 80:20). The model was trained for 100 epochs and evaluated using accuracy, precision, and recall metrics to assess its classification performance. The experiment results show that MLP is able to produce high performance with 86.20% accuracy. 84.40% precision, and 88.56% recall. Compared to other methods such as Decision Tree and SVM, the experiment results show that MLP demonstrated superior classification accuracy. Thus, MLP has the potential to be an effective tool for supporting early diagnosis of coronary heart disease more intelligently and efficiently
A comparative study on SMOTE, CTGAN, and hybrid SMOTE-CTGAN for medical data augmentation Khoirunnisa, Ninda; Rosyda, Miftahurrahma
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.2203

Abstract

The imbalance of clinical datasets remains a challenge in medical data mining, often resulting in models biased toward majority outcomes and reduced sensitivity to rare but clinically critical cases. This study presents a comparative evaluation of three augmentation strategies—Synthetic Minority Oversampling Technique (SMOTE), Conditional Tabular GAN (CTGAN), and a hybrid SMOTE+CTGAN—on the Framingham Heart Study dataset for cardiovascular disease prediction. Augmented datasets were evaluated using Decision Tree, Random Forest, and XGBoost classifiers across multiple metrics, including accuracy, precision, recall, and F1-score. Results demonstrate that classifiers trained on imbalanced data achieved high accuracy but poor minority recall (0.40), confirming model’s bias toward majority class. SMOTE yielded the strongest improvements in minority recall (up to 0.88 with XGBoost) and balanced F1 across classes, though at the cost of reduced majority recall. CTGAN and SMOTE+CTGAN delivered more moderate improvements in minority recall (0.66–0.77) while preserving higher majority recall (0.86), providing a gentler trade-off. These findings indicate that while SMOTE remains a robust baseline for addressing imbalance, hybrid and GAN-based approaches offer practical alternatives for preserving majority performance. The results highlight that augmentation choice should be informed by clinical context.

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