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All Journal International Journal of Health, Engineering and Technology Sevaka: Hasil Kegiatan Layanan Masyarakat
Riswan Syahputra Damanik
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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

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.
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.
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

Abstract

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.
Program Sosialisasi Keamanan Email Akademik Mahasiswa Terhadap Ancaman Phishing Berbasis Social Engineering Sentosa Pohan; Hafizhah Mardivta2; Riswan Syahputra Damanik
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 1 No. 4 (2023): 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.v1i4.528

Abstract

The rapid development of digital technology has brought significant benefits to the field of education, particularly through the use of academic email as an official medium of communication. However, this also creates potential security risks, especially phishing attacks based on social engineering. The low level of digital security literacy among students makes academic email accounts vulnerable to cybercrime. This study aims to implement an awareness program on academic email security, focusing on improving students’ understanding of phishing threats at SMA Islam Terpadu Rantau Prapat. The method used was an interactive workshop approach, which included theoretical sessions, demonstrations of phishing cases, simulations on identifying fake emails, and group discussions. Evaluation was carried out through pre-tests and post-tests to measure the participants’ ability to detect phishing. The results showed a significant improvement in students’ knowledge and skills, with the percentage of participants able to identify phishing increasing from 20% before the program to 82% after the program. These findings demonstrate that practice-based education is effective in building students’ digital literacy. The limitation of this study lies in the relatively small sample size and short-term evaluation. Future research is expected to expand the number of participants and integrate interactive technologies to ensure more sustainable impacts.
Penyuluhan Penerapan Media Interaktif Untuk Analisis Leksikal Dalam Pengembangan Kosakata Bahasa Inggris Suerni; Melati Rahma Suri; Riswan Syahputra Damanik
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.v2i2.535

Abstract

Vocabulary mastery is a fundamental component in English language learning that determines comprehension, speaking, and writing skills. However, in the context of district-level communities, including the Regional Library of Labuhan Batu, vocabulary learning still faces various challenges such as the lack of contextual teaching materials, the dominance of rote learning methods, and the limited use of local resources. To address these issues, this Community Service Program (PkM) proposes the application of lexical analysis on 100 local library documents as the basis for developing interactive media used in vocabulary training. The method employed is a community-based participatory approach, with stages including preparation, lexical analysis, media development, training, and evaluation. Corpus analysis produced 27,800 word types with 200 high-frequency words and 1,350 distinctive local vocabulary items. The interactive media developed include multimedia modules, Wordwall quizzes, mobile-based gamification, and bilingual infographics. The training was attended by 40 participants, with evaluation results showing a significant improvement in the average post-test score (76.8) compared to the pre-test (42.5), and 82.5% of participants achieving scores ≥70. Furthermore, 90% of participants reported increased motivation to learn, and 85% considered the locally based materials more relevant. Thus, this PkM provides a tangible contribution to enhancing English literacy through local vocabulary and offers an interactive learning model that can be replicated in other regional libraries in Indonesia.
Penyuluhan Prediksi Risiko Rambut Rontok Menggunakan Algoritma Support Vector Machine (SVM) Bambang Irwansyah; Novica Jolyarni Dornik; Riswan Syahputra Damanik
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.v3i3.554

Abstract

Hair loss is one of the common health problems experienced by many people and often causes psychological impacts, particularly on self-confidence. The factors contributing to hair loss are diverse, ranging from genetics, diet, and stress to lifestyle. The lack of public knowledge about these risk factors, as well as the low level of digital literacy in the use of predictive technology, makes it difficult for people to take early preventive measures. This community service activity aims to provide education and simple training on predicting hair loss risk using the Support Vector Machine (SVM) algorithm for residents of Rantau Prapat Village. The implementation methods include a pre-test to measure initial understanding, interactive counseling on hair loss risk factors, practical simulation of risk prediction using SVM based on a simple dataset, and evaluation through a post-test. The results of the activity showed a significant increase in participants’ understanding, from an average of 45.2% in the pre-test to 81.6% in the post-test, with a participant satisfaction level reaching 92%. This counseling not only improved health literacy but also introduced the practical application of artificial intelligence in the health sector.
Co-Authors Baginda Restu Al Ghazali Desi Irfan Desi Irpan Fahruzi Sirait Hafizhah Mardivta2 Halimah Tusakdiyah Harahap Irwansyah, Bambang Melati Rahma Suri novica jolyarni Novica Jolyarni D Novica Jolyarni Dornik Riszki Fadillah Rizalina Rizalina Sentosa Pohan Suerni