Article Per Year (5 Year)
p-Index From 2020 - 2025
0.983
P-Index
This Author published in this journals
All Journal International Journal of Health, Engineering and Technology Sevaka: Hasil Kegiatan Layanan Masyarakat
Fahruzi Sirait
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Dentistry Education Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
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
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

Abstract

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
Sentiment Analysis on Twitter Social Media towards Najwa Shihab Using Naïve Bayes Algorithm and Support Vector Machine (SVM) Fahruzi Sirait; Desi Irpan; Riszki Fadillah; Rizalina Rizalina; Riswan Syahputra Damanik
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.v3i1.280

Abstract

With the rapid growth of digital technology, social media has become a key platform for sharing information and opinions. Twitter, one of the most popular platforms in Indonesia, enables users to interact directly with public figures such as Najwa Shihab. This study aims to analyze public sentiment toward Najwa Shihab on Twitter using sentiment analysis, specifically employing the Naïve Bayes and Support Vector Machine (SVM) algorithms. Sentiment analysis is essential to understanding public opinion, as it classifies text into categories like positive, negative, or neutral, providing valuable insights into societal perspectives on public figures. In this study, 10,000 tweets related to Najwa Shihab were collected from January 1, 2023, to January 31, 2023. Data preprocessing steps such as data cleaning, tokenization, stopwords removal, and filtering were conducted to ensure high-quality data for analysis. The Naïve Bayes and SVM algorithms were applied using RapidMiner to classify the sentiment of the tweets. The performance of both algorithms was evaluated based on accuracy, precision, recall, and F1-score.The results revealed that SVM outperformed Naïve Bayes in all metrics, demonstrating its superior ability to classify sentiments correctly. The sentiment distribution indicated a majority of positive opinions toward Najwa Shihab, with fluctuations in negative sentiment during specific events. This study provides insights into public sentiment analysis and contributes to understanding social media opinions on public figures.
Penyuluhan Penerapan Naive Bayes Untuk Identifikasi Keterlambatan Perkembangan Anak Berdasarkan Data Kesehatan Pada Program Studi Kebidanan Fahruzi Sirait; Eka Ramadhani Putra; Nailatun Nadrah; Rika Handayani; Yusril Iza Mahendra Hasibuan
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.v2i4.525

Abstract

Child developmental delay is a public health issue that needs to be identified early to prevent long-term impacts on children’s quality of life. In Rantau Prapat Sub-district, cases are still found among toddlers with undernutrition, incomplete immunizations, and suboptimal developmental stimulation, which may pose risks of growth and developmental delays. This study aims to apply the Naive Bayes method in identifying child developmental delays based on health data collected through medical records and questionnaires. The research method includes data collection, pre-processing (cleaning, transformation, and normalization), classification using the Naive Bayes algorithm, and model validation with the k-fold cross-validation technique. The results showed that out of 150 toddler data samples, 30.7% experienced developmental delays, with the dominant influencing factors being nutritional status and immunization completeness. The Naive Bayes algorithm achieved an accuracy rate of 87.3% with a precision of 84.1%, recall of 85.7%, and F1-score of 84.9%. These findings demonstrate that Naive Bayes can be used as a decision support system in the early identification process of child developmental delays. Therefore, the results of this study are expected to assist healthcare workers, particularly midwives, in improving the quality of early detection and delivering more targeted interventions for children in the Rantau Prapat area.
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

Abstract

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.
Co-Authors Aldi Indrawan Baginda Restu Al Ghazali Desi Irfan Desi Irpan Eka Ramadhani Putra Hafizhah Mardivta Halimah Tusakdiyah Harahap Juni Purwanto Nadya Fitriani Nadya Fitriyani Nailatun Nadrah Rahadatul, Aisy Riadi Rika Handayani Riswan Syahputra Damanik Riszki Fadillah Rizalina Rizalina Yusril Iza Mahendra Hasibuan