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INDONESIA
Sevaka: Hasil Kegiatan Layanan Masyarakat
Published by Sekolah Tinggi Ilmu Kesehatan Columbia Asia Medan
ISSN : 30308844     EISSN : 30308836     DOI : 10.62027
Core Subject : Humanities, Health, Education, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Humanities Education Engineering Health Professions
Jurnal ini adalah Sevaka : Hasil Kegiatan Layanan Masyarakat yang bersifat peer-review dan terbuka. Bidang kajian dalam jurnal ini termasuk riset Hasil Kegiatan Layanan Masyarakat. Sevaka
Arjuna Subject : Umum - Umum
Articles 135 Documents
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Sosialisasi Penerapan Media Interaktif Untuk Klasifikasi Puisi Indonesia Berdasarkan Gaya Bahasa Atika Sadariah; Melati Rahma Suri; Indah Cahyani
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.v1i4.526

Abstract

The application of interactive media in classifying Indonesian poetry based on stylistic devices aims to enhance students' understanding in identifying and analyzing the stylistic elements used in poetry. Stylistic devices such as metaphors, personification, hyperbole, and similes play a significant role in enriching the meaning of poetry and deepening the reader’s interpretation. The use of interactive media offers a more dynamic and effective approach compared to traditional methods. Through digital platforms and interactive learning applications, students can become more engaged in an active and enjoyable learning process, making it easier for them to recognize and comprehend various stylistic devices in poetry. This media also supports diverse learning styles, such as visual, auditory, and kinesthetic, enabling students to learn according to their preferences. This study aims to explore the effectiveness of interactive media in teaching the classification of stylistic devices in poetry and how it can enrich the learning experience while improving students' analytical skills in literary works. It is hoped that the application of interactive media can create a more engaging learning environment, boost students' motivation, and deepen their understanding of Indonesian poetry.
Pelatihan Deteksi Risiko Hipertensi Dengan Analisis Data Riwayat Medis Berbasis Random Forest Untuk Tenaga Kesehatan Masyarakat Desi Irfan; Evri Ekadiansyah; Halimah Tusakdiyah Harahap; Novica Jolyarni Dornik; Yusril Iza Mahendra Hasibuan
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.v1i4.527

Abstract

Hypertension is one of the most prevalent non-communicable diseases and a major risk factor for heart disease, stroke, and kidney disorders. The high prevalence of hypertension cases in the community, particularly in the working area of Puskesmas Kota Rantau Prapat, highlights the urgent need for more effective early detection efforts to prevent severe complications in the future. However, the limited capacity of healthcare workers in utilizing data analysis technologies has resulted in hypertension risk detection being dominated by conventional methods, which are often less accurate and inefficient. To address this issue, this community service program was conducted through training on the application of the Random Forest algorithm to analyze patients’ medical history data in order to detect hypertension risks. The training method included an introduction to the fundamentals of machine learning, data pre-processing stages, implementation of the Random Forest algorithm, and interpretation of prediction results. The outcomes of the program demonstrated that healthcare workers were able to understand the use of data analysis technologies to support more accurate early detection of hypertension. Furthermore, the participants gained practical skills in utilizing medical datasets to produce predictions that can serve as a decision-support tool for preventive medical actions.Thus, this training contributed to enhancing the capacity of community healthcare workers in integrating machine learning-based technologies into preventive healthcare services. This program is expected to serve as an initial step toward developing more effective, efficient, and sustainable data-driven health systems.
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 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

Abstract

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 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 Penerapan K-Means Clustering Dalam Pengelompokan Data Keuangan Rumah Sakit Untuk Pengelolaan Anggaran Di RSUD Rantauprapat Intan Nur Fitriyani; Evri Ekadiansyah; Indah Cahyani
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.v2i2.536

Abstract

Financial management in hospitals is a crucial aspect to ensure the sustainability of quality health services. However, the complexity of financial data, which involves various budget components, often creates challenges for hospital management in conducting accurate analysis and budget planning. Therefore, a data-driven approach is required to present financial information in a structured and comprehensible manner. This study examines the application of the K-Means Clustering method to classify hospital financial data based on expenditure characteristics and patterns, with a case study at RSUD Rantau Prapat as part of a community service program. The financial data were analyzed through pre-processing stages, determination of the optimal number of clusters using the Elbow Method, and the implementation of the K-Means algorithm to generate more representative budget groups. The results indicate that clustering hospital financial data into three main categories—routine operational costs, medical service costs, and administrative/personnel costs—provides clearer insights into budget distribution. This supports hospital management in identifying budget allocation priorities, detecting potential inefficiencies, and improving the overall efficiency of financial governance. The limitation of this study lies in the data scope, which only involved a single hospital, thus restricting its generalizability. Future research is recommended to expand the scope to multiple hospitals and integrate alternative clustering methods to obtain more comprehensive results.
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.
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.
Pengembangan Platfrom Teknologi Inovatif Untuk Efisiensi Produksi UMKM (2024) Nana Erika; Nailatun Nadrah; Ramada Sandi
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.v2i4.557

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in supporting national economic growth and expanding employment opportunities in Indonesia. However, many MSMEs still face significant challenges in improving production efficiency due to limited human resources, low levels of technology adoption, and suboptimal operational management. To address these issues, this study developed an innovative digital technology platform designed to help MSMEs enhance productivity, reduce material waste, and accelerate the production process. The platform integrates Internet of Things (IoT), Artificial Intelligence (AI), and simplified Enterprise Resource Planning (ERP) technologies tailored for small business operations. Through IoT, production processes can be monitored in real time; AI is applied to analyze sales data and predict material requirements and production schedules; while the ERP system automates inventory, transaction, and financial reporting processes. Trials conducted across MSMEs in the food, handicraft, and textile sectors demonstrated a 30% improvement in production efficiency and a 20% reduction in operational costs. The results indicate that the implementation of innovative technological platforms can significantly enhance efficiency, accuracy, and competitiveness among MSMEs. Digital transformation not only increases production efficiency but also enables broader business integration into the global market through cloud-based systems. Government and institutional support are essential to expand the adoption of such technologies, ensuring that Indonesian MSMEs become more adaptive, productive, and sustainable in the era of Industry 4.0.
Edukasi Social Marketing Untuk Perubahan Prilaku Hidup Sehat Pada Masyarakat Desa (2024) Rina Anggraini; Novica Jolyarni; Indri Putri Nikanti
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.v2i4.558

Abstract

Behavioral change toward a healthy lifestyle in rural communities remains a major challenge requiring strategic and sustainable approaches. Social marketing has proven to be an effective tool in promoting behavior change through communication, education, and empowerment. This study aims to examine the application of social marketing education to improve health awareness and behaviors among rural communities, focusing on hygiene, balanced nutrition, and prevention of infectious diseases. The research uses a qualitative descriptive approach involving observation, interviews, and secondary data analysis from governmental and non-profit health promotion programs. The findings indicate that community-based social marketing, supported by local leaders and media, successfully increased participation in health and nutrition activities by up to 68%. The study concludes that social marketing education can effectively promote healthy behavioral changes when integrated with local cultural values and active community engagement.

Page 11 of 14 | Total Record : 135

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