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Nadya Fitriani

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Author-ID : 5581920

Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary Other

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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 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

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 Infertility Risk in Female Patients Based on Medical Record Data Using Naive Bayes Algorithm Fahruzi Sirait; Halimah Tusakdiyah Harahap; Nadya Fitriani; Rika Handayani; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology 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.v2i4.274

Infertility is a reproductive health problem that has a significant impact globally, especially in developing countries such as Indonesia. This study aims to classify the risk of infertility in female patients at Rantauprapat Regional Hospital by utilizing the Naive Bayes algorithm based on electronic medical record data. The data used consisted of 500 medical records of female patients of childbearing age during the period 2019–2022, which had been processed and divided into training data (70%) and testing data (30%). The analysis and modeling process was carried out using the RapidMiner application without requiring programming skills. The results showed that the Naive Bayes model was able to classify the risk of infertility with an accuracy level of 86.7%, precision of 91.0%, recall of 93.2%, and F1-score of 92.1%. The main factors that most influence the classification of infertility include a history of reproductive disease, patient age, hormonal examination results, body mass index, and history of sexually transmitted infections. These findings indicate that the integration of the Naive Bayes algorithm into medical record data can be an effective solution for early detection of infertility clinically and support data-based decision making. This study also recommends increasing data and attribute coverage, as well as comparison with other algorithms for more optimal results in the future

Classification of Infertility Risk in Female Patients Based on Medical Record Data Using Naive Bayes Algorithm fahruzisirait; Halimah Tusakdiyah Harahap; Nadya Fitriani; Rika Handayani4; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology Vol. 4 No. 3 (2025): IJHET SEPTEMBER 2025
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar

Infertility is a reproductive health problem that has a significant impact globally, especially in developing countries such as Indonesia. This study aims to classify the risk of infertility in female patients at Rantauprapat Regional Hospital by utilizing the Naive Bayes algorithm based on electronic medical record data. The data used consisted of 500 medical records of female patients of childbearing age during the period 2019–2022, which had been processed and divided into training data (70%) and testing data (30%). The analysis and modeling process was carried out using the RapidMiner application without requiring programming skills. The results showed that the Naive Bayes model was able to classify the risk of infertility with an accuracy level of 86.7%, precision of 91.0%, recall of 93.2%, and F1-score of 92.1

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Journal : international journal of health engineering and technology