Article Per Year (5 Year)
p-Index From 2021 - 2026
1.756
P-Index
This Author published in this journals
All Journal Jurnal Media Informatika International Journal of Community Service (IJCS) Jurnal Kebidanan, Keperawatan dan Kesehatan (J-BIKES) International Journal of Health, Engineering and Technology Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Sevaka: Hasil Kegiatan Layanan Masyarakat
Tusakdiyah Harahap, Halimah
Fakultas Ilmu Kesehatan, Institut Teknologi Dan Kesehatan Ika Bina, Rantauprapat, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Identifikasi Faktor Risiko Utama Terhadap Gizi Buruk Pada Balita Usia 12-59 Bulan: Analisis Komprehensif Di Desa Tanjung Anom Nasution, Nur Indah; Damanik, Rina Anggraini; Harahap, Halimah Tusakdiyah
JURNAL KEBIDANAN, KEPERAWATAN DAN KESEHATAN (BIKES) Vol 4, No 2 (2024): J-BIKES NOVEMBER
Publisher : Mata Pena Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51849/j-bikes.v4i2.94

Abstract

Balita usia 12-59 bulan mengalami pertumbuhan dan perkembangan yang pesat, sehingga membutuhkan asupan gizi yang lebih banyak. Kekurangan gizi pada periode ini dapat menyebabkan masalah signifikan dalam perkembangan mental, sosial, kognitif, dan fisik. Penelitian ini bertujuan untuk mengidentifikasi faktor risiko yang terkait dengan malnutrisi pada balita di Desa Tanjung Anom, Kecamatan Pancur Batu, Kabupaten Deli Serdang. Menggunakan metode observasional analitik dengan desain kasus-kontrol, penelitian ini melibatkan 64 balita, yang dibagi menjadi kelompok kasus (32 balita malnutrisi) dan kelompok kontrol (32 balita tanpa malnutrisi). Sampel dipilih melalui purposive sampling. Analisis statistik dilakukan menggunakan uji chi-square dan rasio odds (OR). Hasil penelitian menunjukkan bahwa pengetahuan ibu (nilai p = 0,018, OR = 4,333), pendapatan keluarga (nilai p = 0,001, OR = 11,667), riwayat penyakit infeksi (nilai p = 0,002, OR = 6,943), dan asupan makanan (nilai p = 0,000, OR = 81,000) secara signifikan memengaruhi risiko malnutrisi. Penelitian ini menunjukkan bahwa pengetahuan ibu, pendapatan keluarga, riwayat penyakit infeksi, dan asupan makanan adalah faktor-faktor utama yang memengaruhi risiko malnutrisi pada balita di Desa Tanjung Anom. Untuk menurunkan angka kejadian malnutrisi, perlu dilakukan intervensi yang terfokus pada peningkatan edukasi gizi bagi ibu, dukungan ekonomi keluarga, pencegahan penyakit infeksi, serta perbaikan asupan makanan balita.
Penerapan Naive Bayes untuk Identifikasi Keterlambatan Perkembangan Anak Berdasarkan Data Kesehatan pada Program Studi Kebidanan Sirait, Fahruzi; Sakti Tanjung, Rani Darma; Tusakdiyah Harahap, Halimah; Fadillah, Riszki
Jurnal Media Informatika Vol. 6 No. 1 (2024): Jurnal Media Informatika Edisi September - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini berfokus pada pemantauan perkembangan anak, yang merupakan aspek penting dalam kesehatan anak, terutama pada masa emas (golden period) perkembangan. Keterlambatan perkembangan anak sering kali tidak terdeteksi secara dini, yang dapat berdampak negatif pada kualitas hidup mereka di masa depan. Penelitian ini bertujuan untuk mengeksplorasi penerapan metode Naive Bayes dalam mengidentifikasi keterlambatan perkembangan anak berdasarkan data kesehatan yang tersedia. Dengan menggunakan pendekatan kuantitatif dan eksperimen, penelitian ini menganalisis data dari rekam medis, hasil pemeriksaan kebidanan, serta informasi tambahan dari orang tua. Metode Naive Bayes dipilih karena kemampuannya dalam mengolah data besar dan memberikan klasifikasi yang akurat dengan cepat. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes dapat digunakan untuk mengklasifikasikan status perkembangan anak ke dalam kategori normal atau terlambat dengan tingkat akurasi yang tinggi. Dengan memanfaatkan sistem informasi kesehatan, tenaga medis dapat lebih mudah mengakses dan menganalisis data kesehatan anak, sehingga memungkinkan deteksi dini terhadap keterlambatan perkembangan. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan efektivitas pemantauan kesehatan anak dan mendukung intervensi yang tepat waktu. Selain itu, temuan ini juga membuka peluang untuk pengembangan lebih lanjut dalam penerapan teknologi informasi di bidang kebidanan dan kesehatan anak, dengan fokus pada peningkatan kualitas layanan kesehatan secara keseluruhan
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
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.
Stunting Prevention Education with Nutrition Counseling and Provision of Nutritional Food at Rantauprapat City Community Health Center, Labuhanbatu Regency Harahap, Halimah Tusakdiyah; Elliana, Agusta Dian
International Journal of Community Service (IJCS) Vol. 4 No. 1 (2025): January-June
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijcs.v4i1.1361

Abstract

This Study to ensure that every mother and toddler gets the right access and necessary health information, thereby reducing the risk of stunting and other health problems that can affect their growth and development. Using a qualitative approach, the study examines stunting. Community Service Activities on stunting prevention education by providing nutritional counseling and providing food and examining the nutritional status of toddlers were carried out on March 22, 2025 at the Rantauprapat City Health Center . This activity has increased the knowledge of the target audience. Increasing the knowledge of mothers of toddlers about balanced nutrition in toddlers is expected to improve the attitudes and actions of mothers in providing balanced nutrition to toddlers so that the nutritional status of toddlers is in the normal category. This activity can make a positive contribution in reducing and preventing nutritional problems in Indonesia, especially in the Rantauprapat City Health Center area, Labuhanbatu Regency.
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.
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

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
Pendampingan Masyarakat Dalam Pemahaman Alur Administrasi BPJS Di Fasilitas Kesehatan Nana Erika; Halimah Tusakdiyah Harahap; Suci Ardiah
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 3 No. 4 (2025): 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.v3i4.583

Abstract

The community assistance program in understanding BPJS administrative procedures at healthcare facilities aims to improve health literacy and service accessibility for participants of the National Health Insurance (JKN). Many citizens, particularly in rural areas, still face challenges in understanding BPJS registration, referral, and service claim procedures. This activity was carried out through a community-based participatory approach using socialization, service flow simulations, and direct mentoring at healthcare facilities. The results show a significant increase in public understanding of BPJS administrative stages, improved ability to access services independently, and higher satisfaction with healthcare services. The program also strengthened collaboration between communities, health cadres, and BPJS officers in facilitating administrative processes. Therefore, this initiative contributes to improving service efficiency and promoting equitable access to healthcare for all community groups.
Penyuluhan Literasi Kesehatan tentang Pencegahan BBLR melalui Pendekatan Data Science di Lingkungan Kampus Quratih Adawiyah; Riyan Agus Faisal Hasibuan; Halimah Tusakdiyah Harahap; Noprida Wati Ritonga
Multidisiplin Pengabdian Kepada Masyarakat Vol. 5 No. 02 (2026): Multidisiplin Pengabdian Kepada Masyarakat, April-July 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Berat Badan Lahir Rendah (BBLR) masih menjadi salah satu permasalahan kesehatan yang berkontribusi terhadap meningkatnya risiko kesakitan dan kematian bayi. Salah satu upaya pencegahan yang dapat dilakukan adalah meningkatkan literasi kesehatan melalui kegiatan edukasi yang inovatif dan mudah dipahami. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan meningkatkan literasi kesehatan mahasiswa mengenai pencegahan BBLR melalui pendekatan data science di lingkungan kampus. Sasaran kegiatan adalah 25 mahasiswa yang berasal dari lima program studi, yaitu Sistem Informasi, Teknologi Informasi, Administrasi Kesehatan, Kebidanan, dan Keperawatan. Metode yang digunakan meliputi tahap persiapan, pelaksanaan penyuluhan, diskusi interaktif, serta evaluasi menggunakan pre-test dan post-test. Materi penyuluhan disampaikan melalui visualisasi data berupa grafik dan infografis yang menampilkan informasi mengenai faktor risiko, dampak, serta upaya pencegahan BBLR. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta setelah mengikuti penyuluhan, yang ditunjukkan oleh meningkatnya rata-rata nilai post-test dibandingkan pre-test. Selain itu, peserta memberikan respons positif terhadap penggunaan visualisasi data karena dinilai lebih menarik, mudah dipahami, dan membantu menginterpretasikan informasi kesehatan berdasarkan data. Pendekatan ini juga meningkatkan partisipasi peserta selama sesi diskusi. Dengan demikian, penyuluhan literasi kesehatan melalui pendekatan data science terbukti menjadi alternatif edukasi yang efektif dalam meningkatkan pemahaman mahasiswa mengenai pencegahan BBLR. Kegiatan serupa diharapkan dapat diterapkan secara berkelanjutan dengan memanfaatkan teknologi digital yang lebih inovatif untuk mendukung program promosi kesehatan di lingkungan perguruan tinggi maupun masyarakat. Kata Kunci: Berat Badan Lahir Rendah, Literasi Kesehatan, Data Science, Penyuluhan Kesehatan, Mahasiswa.
Co-Authors Baginda Restu Al Ghazali D, Novica Jolyarni Damanik, Rina Anggraini Desi Irfan Elliana, Agusta Dian Evri Ekadiansyah Fahruzi Sirait fahruzisirait Nadya Fitriani Nana Erika Nasution, Nur Indah Noprida Wati Ritonga Quratih Adawiyah Rika Handayani Rika Handayani4 Riswan Syahputra Damanik Riszki Fadillah Riyan Agus Faisal Hasibuan Sirait, Fahruzi Suci Ardiah Tanjung, Rani Darma Sakti Yusril Iza Mahendra Hasibuan