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All Journal International Journal of Health, Engineering and Technology
Juni Purwanto
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Dentistry Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Implementation of the Support Vector Machine (SVM) Algorithm to Improve the Accuracy of Computer Network Performance Predictions Desi Irfan; Fahruzi Sirait; Rahadatul, Aisy Riadi; Aldi Indrawan; Juni Purwanto
International Journal of Health Engineering and Technology Vol. 4 No. 1 (2025): IJHET May 2025
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v4i1.271

Abstract

Computer network performance is very important in supporting various digital activities, but systems often cannot accurately predict changes in performance, which can cause service disruptions and economic losses. This research aims to implement the Support Vector Machine (SVM) algorithm to increase the accuracy of network performance predictions based on parameters such as latency, packet loss, throughput and jitter. Data is collected through network simulation and real data monitoring, then processed with normalization and selection of relevant features. The SVM model is tested with various kernels, including linear, RBF, and polynomial, to find the best configuration. Performance evaluation uses accuracy, precision, recall, F1-score, and ROC-AUC metrics, with cross-validation to increase the reliability of the results. The results show that the RBF kernel provides a prediction accuracy of 92%, higher than baseline methods such as Decision Tree and Logistic Regression. This model shows its potential to be applied in computer network monitoring systems to predict network performance in real-time, with the possibility of wider implementation in artificial intelligence-based network applications. Therefore, this research not only contributes to machine learning theory in the field of computer networks, but also provides practical solutions that can improve the management and optimization of network performance in various environments that require fast and accurate data processing
Implementation of the Neural Network Algorithm in Monitoring Child Development to Screen for Developmental Disorders at an Early Age Santosa Pohan; Rani Darma Sakti Tanjung; Riyan Agus Faisal; Nur Indah Nasution; Nadya Fitriani; Juni Purwanto
International Journal of Health Engineering and Technology Vol. 4 No. 1 (2025): IJHET May 2025
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v4i1.272

Abstract

This research aims to implement a Neural Network (NN) in monitoring children's development, especially to detect developmental disorders from an early age. The data used includes variables such as Age, Height, and Weight, which have been normalized to have a uniform scale. The modeling process begins with the use of Convolutional Layers to extract important features from numerical data, which are then passed to the ReLU activation layer to introduce non-linearity to the model, enabling the detection of more complex patterns. After that, Max Pooling is carried out to reduce data dimensions and increase computing efficiency. This model was trained using 100 normalized data, and continued with the use of fully connected layers to process further information. In the output layer, a sigmoid activation function is used to generate probability predictions, allowing binary classification (whether a developmental disorder is present or not). Evaluation results show that this model has an accuracy of 85%, which indicates its effectiveness in detecting child developmental disorders based on available data. Although the results are promising, there is still room for improvement, especially in improving the model's accuracy and ability to handle more complex data. Overall, this research shows that Neural Networks can be a useful tool in the early detection of childhood developmental disorders, with potential for broad applications in the fields of children's health and education.
Classification of Heart Disease Risk Factors Using Decision Tree at Rantauprapat Regional Hospital Quratih Adawiyah; Riyan Agus Faisal; Nailatun Nadrah; Juni Purwanto; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology Vol. 3 No. 4 (2024): IJHESS NOVEMBER 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i4.273

Abstract

Heart disease is one of the leading causes of death in Indonesia, so it is important to identify risk factors that contribute to the increasing incidence of heart disease. This study aims to classify risk factors for heart disease using the Decision Tree method with the CART (Classification and Regression Tree) algorithm at Rantauprapat Regional Hospital. The data used includes factors such as Age, High Blood Pressure, High Cholesterol Levels, Body Mass Index (BMI), Family History, Smoking, Unhealthy Diet, and Low Physical Activity. The results of the analysis show that the factors Age, High Blood Pressure, and High Cholesterol Levels have a significant effect on the increased risk of heart disease, with a model accuracy of 80%. Although this model successfully classifies high risk well, there are some errors in identifying low risk, as reflected in the Recall value (0.67).
Co-Authors Aldi Indrawan Baginda Restu Al Ghazali Desi Irfan Fahruzi Sirait Nadya Fitriani Nailatun Nadrah Nur Indah Nasution Quratih Adawiyah Rahadatul, Aisy Riadi Rani Darma Sakti Tanjung Riyan Agus Faisal Santosa Pohan