Article Per Year (5 Year)
p-Index From 2021 - 2026
3.947
P-Index
This Author published in this journals
All Journal Jurnal Keperawatan Priority Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Gentle Birth JUKESHUM: Jurnal Pengabdian Masyarakat Jurkessutra/Jurnal Kesehatan Surya Nusantara Excellent Midwifery Journal Miracle Journal International Journal of Public Health Excellence (IJPHE) International Journal of Health, Engineering and Technology Journal of Innovative and Creativity Sevaka: Hasil Kegiatan Layanan Masyarakat Jurnal Pemberdayaan Kepada Masyarakat
D, Novica Jolyarni
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Education Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 28 Documents
Search

 1   2   3 
Factors Related to Premenstrual Syndrome in Young Women at MTsN Labuhanbatu in 2024 Jolyarni, Novica; Nadrah, Nailatun; Nasution, Fitriyani
International Journal of Public Health Excellence (IJPHE) Vol. 4 No. 1 (2024): June-December
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijphe.v4i1.1011

Abstract

Premenstrual syndrome (PMS) is a complex and poorly understood condition consisting of one or more of a number of physical and psychological symptoms that begin in the luteal phase of the menstrual cycle. World Health Organization (WHO) in 2020 PMS has a higher prevalence in Asian countries compared to western countries. The purpose of this study was to determine the factors associated with Premenstrual syndrome in adolescent girls at MTsN 1 Labuhanbatu in 2024. This research design uses an analytic survey, namely research trying to explore how and why the phenomenon occurs. Then analyze the dynamics of the correlation between phenomena, both between related factors (Indipendent) and effect factors (Dependent). The approach used in this research is cross sectional. The population in this study was 291 people. The sample in this study amounted to 74 people. Data analysis used univariate analysis and bivariate analysis using the chi-square test. The results of statistical tests with stress categories obtained using the chi-square test at a confidence level of 95% are known that psig 0.000 is smaller than 0.05, the results of chi-square tests with consumption patterns at a confidence level of 95% are known that psig 0.000 is smaller than 0.05, the results of statistical tests with premenstrual syndrome incidence obtained using the chi-square test at a confidence level of 95% are known that psig 0.000 is smaller than 0.05. In conclusion, it is known that there is a relationship between stress, consumption patterns and exercise with the incidence of premenstrual syndrome in adolescent girls at MTsN 1 Labuhanbatu in 2024. It is suggested that the results of this study can add insight, knowledge and experience about premenstrual syndrome that can occur at any time.
Penyuluhan Kesehatan Tentang Premenstrual Syndrome Novica Jolyarni; Nailatun Nadrah; Fitriyani Nasution; Aswin Syahputra
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.v2i4.309

Abstract

Health education about Premenstrual Syndrome (PMS) aims to improve the understanding and awareness of adolescent girls at MTSN 1 Rantau Selatan regarding the symptoms, causes, and management of PMS. This research was conducted in January 2025 using an educational and interactive method, which included presentations, discussions, simulations, and distribution of educational materials. The results of the outreach showed a significant improvement in the participants' knowledge about PMS, as well as a positive shift in their attitudes toward the condition. Before the intervention, most participants were unaware of PMS symptoms and appropriate management strategies. After the session, they became more open to discussing menstruation-related issues and better equipped to manage PMS symptoms healthily. However, some participants still require ongoing support to implement techniques such as acupressure and light exercises. This program is expected to reduce stigma surrounding PMS and improve the reproductive health of adolescent girls at MTSN 1 Rantau Selatan.
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.
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.
Utilization of RNN Chatbots for Midwifery Education for Pregnant Women at Rantauprapat City Community Health Centers Fadillah, Riszki; Tanjung, Rani Darma Sakti; Tusakdiyah, Halimah; Jolyarni D, Novica; Purwanto, Juni
International Journal of Public Health Excellence (IJPHE) Vol. 4 No. 2 (2025): January-May
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijphe.v4i2.1469

Abstract

The application of information technology in the healthcare sector has been rapidly advancing with the development of artificial intelligence. One of its potential applications is the use of chatbots powered by the Recurrent Neural Network (RNN) algorithm to enhance maternal health education access for pregnant women. Although health information is increasingly accessible, pregnant women often face challenges in obtaining accurate education about pregnancy due to limitations in time, location, and access to medical professionals. Puskesmas, as a primary healthcare center, plays a crucial role but is limited by the number of healthcare workers and operational hours, reducing the effectiveness of maternal health education delivery. Therefore, AI-powered chatbots can provide instant, personalized information that can be accessed anytime and anywhere. In this study, the developed chatbot using the RNN algorithm is capable of processing conversations contextually, providing relevant answers according to the stage of pregnancy and the specific needs of the pregnant woman. The implementation of this chatbot at Puskesmas Kota Rantauprapat is expected to improve the accessibility of maternal health education, reduce anxiety among pregnant women, and minimize the need for physical visits for common questions. The results of this study demonstrate the potential of RNN-based chatbots as an efficient tool in supporting maternal health education through digital platforms.
PREDIKSI METODE PERSALINAN DENGAN BIG DATA DAN ALGORITMA GRADIENT BOOSTING CLASSIFIER Fitriyani, Intan Nur; Fadillah, Riszki; Adawiyah, Quratih; D, Novica Jolyarni
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1557

Abstract

This study aims to develop a prediction model to determine the method of delivery (normal or cesarean) using the Gradient Boosting algorithm based on maternal examination data. This model was evaluated using precision, recall, F1-score, and accuracy metrics. The results showed that the Gradient Boosting model had an accuracy of 48%, with better performance in predicting Normal delivery compared to Caesarean. Although this model is effective, there is an imbalance in precision and recall for the Caesarean class, indicating the need for improvement in identifying cases of cesarean delivery. Comparison with other algorithms such as Random Forest, Logistic Regression, and SVM showed that Random Forest gave the best performance with an accuracy of 55%. To improve performance, this study recommends hyperparameter optimization, application of class balancing techniques, and enrichment of medical features. The developed model has the potential to be used as a tool in medical decision-making related to delivery methods, which is expected to improve the safety of mothers and babies, and reduce dependence on subjective factors in medical decisions.
Pengetahuan Bidan Tentang Pencegahan Infeksi Selama Persalinan Di Puskesmas Lingga Tiga Handayani, Rika; dornic, Novica Jolyarni; Nadrah, Nailatun; Tussolihin Dalimunthe, Khodijah
Miracle Journal Vol. 4 No. 1 (2024): Edisi Januari 2024
Publisher : Universitas Haji Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51771/mj.v4i1.1025

Abstract

Risiko infeksi pada ibu, bayi dan pada ibu,bayi dan penolong persalinan akan meningkat persalinan akan meningkat apabila tenaga kesehatan tidak mematuhi pencegahan infeksi pada saat menangani passien terutama pada saat pertolong utama pada saat pertolongan persalinan. Infeksi dapat melalui darah, sekresi vagina melalui darah, sekresi vagina air mani, air mani, cairan amnion dan cairan tubuh lainnya. Saat survei awal dengan wawancara dengan ditemukan bahwa bidan tersebut sudah mengetahui tentang pencegahan infeksi selama persalinan, namun saat observasi, peneliti melihat masih ada beberapa bidan yang belum memakai alat pelindung diri yang lengkap, serta hand hyigiene yang belum sesuai dengan standar. Tujuan penelitian ini adalah untuk melihat gambaran Pengetahuan Bidan tentang Pencegahan Infeksi selama persalinan di Puskesmas Lingga Tiga. Jenis penelitian bersifat deskriftif dengan metode cross sectional, populasi adalah bidan yang bertugas di pusksesmas lingga tiga sebanyak 34 orang, pengambilan menggunakan total sampling. Data yang dikumpulkan adalah data primer yaitu data yang diperoleh secara langsung dari responden. Analisis data yang digunakan yaitu analisis univariat. Dari hasil penelitian diperoleh data sebanyak 45 responden (83,3%). Mayoritas berumur berumur 32-39 tahun sebanyak 24 responden (44,4%), dan memiliki masa kerja 46 (85,19%) > 10 tahun
Kejadian Kurang Energi Kronik Pada Ibu Hamil di Wilayah Kerja Puskesmas Simundol Nadrah, Nailatun; Handayani, Rika; Jolyarni Dornic, Novica
Miracle Journal Vol. 5 No. 1 (2025): Edisi Januari 2025
Publisher : Universitas Haji Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51771/mj.v5i1.1401

Abstract

Salah satu tantangan gizi umum yang dihadapi oleh wanita hamil adalah kekurangan energi kronis (KEK), yang bermanifestasi sebagai konsekuensi dari kekurangan gizi yang berkepanjangan dan ditandai dengan berkurangnya lingkar lengan tengah atas kurang dari 23,5 cm. Dampak malnutrisi pada ibu hamil juga berdampak pada kesehatan ibu dan janin, mencakup risiko tinggi anemia, perdarahan, dan penambahan berat badan yang tidak memadai selama kehamilan, serta persalinan yang lama dan sulit, kelahiran prematur, perdarahan pascapersalinan, dan gangguan perkembangan janin. Data WHO 2021 ibu hamil KEK sebanyak 629 ibu (73,2 %) dari AKI. di Indonesia sebanyak 17,3%, di Sumatera Utara 1.383 ibu hamil KEK, Data dari tempat penelitian ditemukan ibu hamil KEK sebanyak 26 orang ibu KEK. Tujuan penelitian ini adalah untuk mengetahui kejadian KEK pada ibu hamil di wilayah kerja puskesmas simundol. Jenis penelitian ini adalah deskriptif dengan desain cross sectional, populasi pada penelitian ini adalah seluruh ibu hamil KEK yang berjumlah 26 orang. Teknik sampel menggunakan total sampling. Dari penelitian diperoleh hasil bahwa mayoritas responden berumur 20-35 tahun (69,22%), responden dengan p86engetahuan kurang sebanyak 61,5%. Responden memiliki pendidikan menengah sebanyak 50,0%. Pendapatan responden 57,7% memiliki pendapatan yang tinggi. Sebanyak 53,8% responden merupakan primipara. Pengkajian faktor yang berkontribusi dengan kejadian KEK bergantung pada karakteristik spesifik komunitas, konsumsi yang berlaku pola, dinamika sosial-ekonomi, dan budaya dalam komunitas masing-masing.
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.
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 Adawiyah, Quratih Anggi deva mayfrani Aswin Syahputra Aswin Syahputra Baginda Restu Al Ghazali Delfi Rosefitri Desi Irfan Devi Nur Fitriyana Evri Ekadiansyah Febri Aulia Fitriyani Nasution Halimah Tusya’diah Harahap Handayani, Rika Indah permata sari Indri Putri Nikanti Indri Safrina Intan Nur Fitriyani Irwansyah, Bambang Nailatun Nailatun Nadrah Nasution, Fitriyani Purwanto, Juni Quratih Adawiyah Rika Handayani Rika Handayani Rika Handayani Rina Anggaraini Damanik RINA ANGGRAINI Rini Haryati Riswan Syahputra Damanik Riszki Fadillah Riyan Agus Faisal Rosdiana - Shelly Monica Sinta Friti Br.Simatupang Siti Mardiah Hasibuan Tanjung, Rani Darma Sakti Tusakdiyah Harahap, Halimah Tusakdiyah, Halimah Tussolihin Dalimunthe, Khodijah Yusril Iza Mahendra Hasibuan