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Santosa Pohan
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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 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 (IJHET) 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.
Application of SVM to Speed Up and Accurate Nursing Decisions for Mentally Disordered Patients Santosa Pohan; Riyan Agus Faisal; Fitriyani Nasution; Putri Ramadani; Ade Irma Yanti Hasibuan
International Journal of Health Engineering and Technology (IJHET) 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.v4i1.277

Abstract

This study aims to evaluate the application of the Support Vector Machine (SVM) algorithm in increasing the speed and accuracy of nursing decision making in patients with mental health disorders. Fast and accurate decision making is very important in the nursing context, especially in treating patients with complex mental disorders. In this research, patient medical record data is used to train an SVM model, which is then used to predict the severity of the patient's mental disorder, such as Mild, Moderate, or Severe. The model is trained using features such as the patient's age, gender, diagnosis, psychological test scores, and physical condition. The evaluation results show that the SVM model has 100% accuracy, which means the model succeeded in classifying the severity of the patient's mental disorder very accurately. In addition, implementing this model also reduces the time required for decision making, allowing nurses to provide faster and more precise decisions. These results indicate that SVM can be a very useful tool in supporting nursing decision making, increasing the efficiency and quality of care, and reducing diagnostic errors. This research provides important insights into the potential use of artificial intelligence algorithms in clinical decision support systems in the mental health field.
Simulation and Detection of Phishing Attacks on Student Academic Emails Using Social Engineering Techniques Santosa Pohan; Desi Irfan; Intan Nur Fitriyani; Yusril Iza Mahendra Hasibuan; Indah Chayani
International Journal of Health Engineering and Technology (IJHET) Vol. 2 No. 4 (2023): IJHET NOVEMBER 2023
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v2i4.283

Abstract

Phishing attacks on student academic emails are a serious threat to information security. Social engineering techniques are often used in these attacks to manipulate victims into divulging sensitive information, such as passwords and other personal data. This research aims to analyze and detect phishing attacks that use social engineering techniques on student academic emails. In this research, a phishing attack simulation was carried out with the scenario of falsifying the identity of an academic institution and creating fake emails that appear legitimate. Students as simulated subjects were tested to see how they reacted to deceptive phishing emails, such as clicking on malicious links or downloading infectious attachments. The detection methods used include heuristic analysis and machine learning techniques, where the system is trained to recognize suspicious patterns in emails, including elements such as unusual subjects, links and attachments. The research results show that phishing attacks that utilize social engineering are effective in manipulating victims. On the other hand, detection using machine learning and heuristic analysis can achieve a high level of accuracy in identifying phishing attacks. This research also underscores the importance of increasing awareness about cyber security among students as well as the need to develop more effective phishing detection tools.
Co-Authors Ade Irma Yanti Hasibuan Desi Irfan Fitriyani Nasution Indah Chayani Intan nur Fitriyani Juni Purwanto Nadya Fitriani Nur Indah Nasution Putri Ramadani Rani Darma Sakti Tanjung Riyan Agus Faisal Yusril Iza Mahendra Hasibuan