Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal Sari Pediatri Indonesian Journal for Health Sciences (IJHS) Mutiara: Multidiciplinary Scientifict Journal International Journal of Electrical Engineering and Intelligent Computing
Sri Yati
Bagian Ilmu Kesehatan Anak Universitas Khairun Ternate
Arts Computer Science & IT Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences

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

Found 5 Documents
Search

 1 
Hubungan Penggunaan Antibiotik dengan Kekambuhan Infeksi Saluran Pernapasan Akut pada Balita di Layanan Primer Kota Ternate Lathifah Azzahra; Wahyunita Do Toka; Abdul Hakim Husen; Sri Yati
Sari Pediatri Vol 24, No 6 (2023)
Publisher : Badan Penerbit Ikatan Dokter Anak Indonesia (BP-IDAI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14238/sp24.6.2023.377-81

Abstract

Latar belakang. Infeksi saluran pernapasan akut merupakan suatu permasalahan penyakit infeksi yang umum terjadi yang disebabkan oleh berbagai mikroorganisme, terbanyak akibat infeksi virus dan bakteri. Di Amerika Serikat, pasien yang datang berobat dengan gejala ISPA menerima resep antibiotik. Peresepan antibiotik pada anak-anak muncul dengan perkiraan berjumlah 421 resep per 1000 populasi. Frekuensi pemberian antibiotik yang terus meningkat dapat meningkatkan peluang insiden penggunaan antibiotik yang tidak rasional yang mengakibatkan timbulnya bakteri yang resisten.Tujuan. Untuk mengetahui hubungan penggunaan antibiotik dengan kekambuhan ISPA pada balita di layanan primer Kota Ternate tahun 2022.Metode. Jenis penelitian ini adalah analitik observasional dengan cross sectional, melibatkan 161 sampel yang diambil dengan teknik random sampling dari total populasi, dan dilakukan uji analisis chi square dan uji statistik SPSS.Hasil. Dari total 161 sampel yang dianalisis, diperoleh hasil penggunaan antibiotik pada balita yang terdiagnosis ISPA sebanyak 65,8% dan balita yang mengalami kekambuhan 49,7%. Hasil uji Chi square diperoleh nilai p value 0,040 (p=<0,05).Kesimpulan. Adanya hubungan bermakna antara penggunaan antibiotik dengan kekambuhan ISPA pada balita. 
Hubungan Pengetahuan Ibu tentang Makanan Pendamping-Air Susu Ibu dengan Status Gizi pada Anak Usia 6-24 Bulan Lary Anceli Br. Pasaribu; Sri Yati; Wahyunita Do Toka
Sari Pediatri Vol 25, No 2 (2023)
Publisher : Badan Penerbit Ikatan Dokter Anak Indonesia (BP-IDAI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14238/sp25.2.2023.112-6

Abstract

Latar belakang. Prevalensi kematian balita di dunia sekitar 45% dikarenakan masalah gizi. Salah satu upaya untuk mencegah terjadinya permasalahan gizi adalah pemberian Makanan Pendamping Air Susu Ibu pada anak dengan benar. Upaya ini dipengaruhi oleh beberapa faktor, salah satunya adalah tingkat pengetahuan ibu.Tujuan. Untuk mengetahui hubungan tingkat pengetahuan ibu tentang Makanan Pendamping Air Susu Ibu dengan status gizi pada anak di Puskesmas JambulaMetode. Penelitian observasional dengan pendekatan potong lintang. Sampel penelitian adalah ibu yang memiliki anak usia 6-24 bulan. Data yang di peroleh dianalisa dengan metode Fisher Freeman Halton Exact Test. Kuesioner yang digunakan berupa pernyataan terbuka tentang pengetahuan ibu sebanyak 30 nomor.Hasil. Berdasarkan 64 yang diteliti, terbanyak ditemukan ibu dengan pengetahuan baik, yaitu 59 (92,2%), status gizi anak terbanyak ditemukan pada kategori gizi baik, yaitu 42 (65,6%). Hasil analisis bivariat diperoleh nilai p=0,505. Hal ini menunjukan bahwa tidak terdapat korelasi antara tingkat pengetahuan ibu tentang Makanan Pendamping Air Susu Ibu dengan status gizi pada anak usia 6-24 bulan.Kesimpulan. Tidak terdapat hubungan antara tingkat pengetahuan ibu dengan status gizi anak usia 6-24 bulan di Puskesmas Jambula.
CHARACTERISTICS OF URINARY TRACT STONE PATIENTS AT Dr. H. CHASAN BOESOIRIE TERNATE REGIONAL GENERAL HOSPITAL 2021-2023 Hadiputra, Narendra Agung Rizky; Selomo, Prita Aulia M; Yati, Sri
Indonesian Journal for Health Sciences Vol 9, No 1 (2025): March
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/ijhs.v9i1.11300

Abstract

Urinary tract stones (UTC) are the occurrence of stones in the urinary tract organs. Environmental factors such as daily behavior, excess body weight, eating habits, and lack of fluid intake influence the occurrence of UTC. Hormonal, genetic, and anatomical factors can also play a role in its pathogenesis. The purpose of this study was to determine the characteristics of urinary tract stone patients at Dr. H. Chasan Boesoirie Ternate Regional Hospital. This type of research is descriptive research with a cross-sectional approach research design. The data used are secondary data in the form of medical records of UTC patients in 2021-2023. This study obtained a total sample of 171 people, the distribution of patients was mostly male (67.8%), adult age group (≥19-45 years) (39.2%), normal body mass index (BMI) (49.1%), high school education (58.5%), and civil servant/military/police/state-owned enterprises/regional-owned enterprises occupation (21.1%). Men are at higher risk of experiencing UTC, allegedly due to the role of hormones. Workload, sweat, and lack of fluids that are common in productive age are risk factors for BSK. Obesity triggers urinary tract stones through increased uric acid, decreased urine pH, inflammation, and oxidative stress. Education level can affect the risk of BSK. Work can be a risk for BSK due to sun exposure to poor lifestyle
Karakteristik Bayi Baru Lahir di RSUD dr. H. Chasan Boesoirie Ternate Antoni, Emyherawati; Yati, Sri; Pratiwi, Dian
Mutiara: Multidiciplinary Scientifict Journal Vol. 3 No. 6 (2025): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v3i6.381

Abstract

Newborns are babies who have just experienced the birth process, aged 0-28 days. According to data from the Indonesian Demographic and Health Survey (IDHS) in 2017, it shows a baby birth rate (AKB) of 24 per 1,000 live births. Every year newborn and neonatal deaths in the world account for 37% of all deaths in children under five. This study was an observational descriptive study. Based on the study, a total sample of 382 subjects was obtained. Male sex is 55.2%, birth weight in the range of 2500 - 4000 grams is 79.1%, birth length in the range of 48 - 52 cm is 69.4%, apgar score in the range of 7 - 10 is 92.6%, type of vaginal spontaneous delivery is 66.8%, gestational age is 37 - 42 weeks 86.6%. Based on the results of the study, it can be concluded that men are the most gender, the most birth weight is normal birth weight, the most birth length is normal body length, the highest apgar score is 7-10 (not asphyxia), the most types of labor are spontaneous vaginal, and the most gestational age is term pregnancy.
A Multimodal Deep Learning Framework for Early Detection of Congenital Heart Disease in Neonates Iis Hamsir Ayub wahab; Sri Yati
International Journal Of Electrical Engineering and Inteligent Computing Vol 2 No 2 June (2025): International Journal Of Electrical Engineering And Intelligent Computing
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/ijeeic.v2i2.10059

Abstract

Congenital heart disease (CHD) is the most common congenital defect and still adds significantly to the neonatal morbidity and mortality rates. Classic echocardiography and ECG unimodal data traditional methods are often unable to analyze complex, multifunctional, and multifactorial cardiac pathologies in neonates. This paper presents an explainable multimodal deep learning framework that acquires four diverse sources of clinical data. Multimodal data includes echocardiogram videos, ECG, and other physiological and structured electronics health record (HER) data. We propose a self-attention-based late fusion transformer architecture that also uses self-attention mechanisms. The model trains and validates on benchmark datasets, which are transparently and reproducibly available (EchoNet-Dynamic, MIMIC-IV, PhysioNet Capnobase, and MIT-BIH). The results achieved using the proposed model mark an improvement over existing benchmarks with 93% accuracy, 95% sensitivity, and 0.96 area under the ROC curve. Using interpretability modules, features that were value added towards determining the diagnostic indicators that were incorporated in the neonatal infant care were shown to be critically relevant. Moreover, the model shows high performance consistency across several data sources and shifts. The research illustrates the use of explainable deep learning architectures for automation of early-stage heart defect detection in newborns. Some of the future work includes validation through clinical studies and multilingual electronic health record integration.
Co-Authors Abd Hakim Husen Antoni, Emyherawati Dian Pratiwi Hadiputra, Narendra Agung Rizky Lary Anceli Br. Pasaribu Lathifah Azzahra Selomo, Prita Aulia M Wahab, Iis Hamsir Ayub