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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

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).

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

Analysis of risk factors for failure of hypertension therapy based on medical history and drug consumption using Random Forest Desi Irfan; Novica Jolyarni; Halimah Tusakdiyah Harahap; Baginda Restu Al Ghazali; Riswan Syahputra Damanik
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.v4i1.276

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 Password Validation using a Combination of Letters, Numbers and Symbols in the New Student Registration Application Sentosa Pohan; Putri Ramadani; Riszki Fadillah; Yusril Iza Mahendra Hasibuan; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.282

This research aims to evaluate the implementation of password validation using a combination of letters, numbers and symbols in new student registration applications in increasing the level of application security. This research method involves implementing a password validation system with strict criteria, as well as testing password strength using brute force attacks. The test results show that passwords that meet the criteria take time 150 seconds to be broken using brute force, while passwords that only use letters only take time 10 seconds. Surveys of users show that 70% feel comfortable with this validation system, though 40% find it difficult to create a valid password. As much 85% users consider this system to improve application security. This research suggests that new student registration applications adopt a strict password validation system to increase the protection of users' personal data, while providing solutions for users to create more secure passwords.complex but easy to remember. The implementation of this system is expected to strengthen application security and increase user confidence in the protection of their personal data.

Analysis of risk factors for failure of hypertension therapy based on medical history and drug consumption using Random Forest Desi Irfan; Novica Jolyarni D; Halimah Tusakdiyah Harahap; Baginda Restu Al Ghazali; Riswan Syahputra Damanik
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.v2i4.284

Cardiovascular disease is a major cause of global morbidity and mortality, with many patients experiencing therapy failure despite treatment. This study analyzes risk factors for failure of antihypertensive therapy based on medical history and drug consumption patterns using the Random Forest algorithm. Retrospective analytical research design using medical record data and structured interviews in hypertensive patients who have undergone treatment for at least one year. The dependent variable was therapy failure, defined as BP ≥140/90 mmHg despite treatment. Independent variables include medical history, drug consumption patterns, and demographic factors. Data is processed by handling missing data, normalization, and feature encoding. The Random Forest model was optimized using GridSearchCV and evaluated based on accuracy, precision, recall and AUC-ROC. Feature importance analysis identifies main risk factors, such as medication adherence, diabetes, and duration of hypertension. The model achieved 86% accuracy (AUC: 0.89), better than logistic regression (accuracy: 78%). These results confirm the importance of patient compliance and comorbidities in hypertension management. This study demonstrates the effectiveness of Random Forest in identifying high-risk patients, with recommendations for prioritization of interventions on medication adherence.

Sosialisasi Penerapan Media Pembelajaran Interaktif Untuk Meningkatkan Pemahaman Bahasa Indonesia Atika Sadariah; Baginda Restu Al Ghazali
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 2 No. 4 (2024): November: Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i4.523

Indonesian language learning at the junior high school level often faces challenges such as low student motivation, participation, and comprehension in reading, writing, and listening materials. The predominance of lecture-based methods and textbooks leads to suboptimal student engagement, especially in online or hybrid learning. This community service activity at SMP S Methodist 1 Rantau Prapat aims to enhance students’ understanding as well as teachers’ capacity through the application of interactive learning media. The media used include interactive PowerPoint, Wordwall, and H5P, which allow adaptive quizzes, writing exercises, and game-based activities. The implementation methods involve initial observation, development of interactive curriculum-based materials, socialization, teacher training, classroom implementation, and evaluation using pre-tests and post-tests. Data were collected through comprehension tests, classroom participation observations, and short interviews. The results showed a significant improvement: the students’ average post-test scores increased compared to the pre-test, with more active classroom participation in interactive quizzes and discussions. Teachers also felt more supported in delivering lessons and gained new skills in using digital media. In conclusion, the application of interactive media proved effective in improving the quality of Indonesian language learning and can serve as a sustainable model for secondary schools.

Penyuluhan Klasifikasi Gejala Keterlambatan Bicara (Speech Delay) Pada Anak Menggunakan Algoritma Naive Bayes, C4.5, Dan K-Nerest Neighbor (K-NN) Putri Ramadani; Ika Ima Nissa; Nur Indah Nasution; Baginda Restu Al Ghazali
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 2 No. 2 (2024): Mei : Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i2.534

Speech delay in children is a developmental issue commonly encountered in society, which can affect various aspects of a child's life, including communication, social interaction, and academic development. Early detection of speech delay is crucial for providing appropriate interventions to minimize its long-term impact on the child. This study aims to introduce the use of machine learning algorithms in detecting speech delay symptoms in children. Three machine learning algorithms applied in this study are Naïve Bayes, C4.5, and K-Nearest Neighbor (K-NN). These algorithms are used to classify speech delay symptoms based on health data, medical history, and environmental factors such as speaking habits and eating patterns. The outreach was conducted at Puskesmas Kota Rantauprapat with the involvement of parents and healthcare providers as participants. The experimental results showed that all three algorithms performed well in terms of accuracy, though with varying error rates. Naïve Bayes achieved relatively high accuracy but had a higher false positive rate compared to C4.5 and K-NN. C4.5 provided more stable results and was easier to interpret due to its decision tree structure. Meanwhile, K-NN performed better with data that had irregular distribution. This outreach is expected to assist both the community and healthcare providers in early detection of speech delay in children, providing a more efficient and affordable means for early intervention, which ultimately leads to better outcomes for children with speech delay.

Penyuluhan Klasifikasi Risiko Infertilitas Pada Pasien Wanita Berdasarkan Data Rekam Medis Menggunakan Algoritma Naive Bayes Fahruzi Sirait; Hafizhah Mardivta; Nailatun Nadrah; Nadya Fitriyani; Baginda Restu Al Ghazali
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 3 No. 3 (2025): Agustus : Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v3i3.555

Infertility in women is a reproductive health issue that requires early intervention to prevent long-term effects. With the advancement of technology, electronic medical records data can be utilized to assist in the diagnosis and classification of infertility risks. This study aims to classify the risk of infertility in female patients using the Naive Bayes algorithm based on medical record data, which includes factors such as age, health history, and medical test results. The data used in this study were obtained from hospitals and health clinics focused on managing infertility patients. The methods applied include data preprocessing, applying the Naive Bayes algorithm for classification, and evaluating the model using accuracy, precision, recall, and F1-score metrics. The results of the study show that the Naive Bayes algorithm provides fairly accurate classification in predicting infertility risks. The analysis-generated graph shows the distribution of infertility risks, with 60% of patients having a positive risk (1) and 40% having a negative risk (0). This study also suggests implementing the classification results in the form of counseling for patients to increase awareness and encourage early preventive actions. Thus, the Naive Bayes algorithm can be an effective tool in assisting healthcare providers in data-driven decision-making to address infertility risks in female patients.

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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