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All Journal Jurnal Manajemen Pelayanan Kesehatan (The Indonesian Journal of Health Service Management) Usadha Journal of Pharmacy
Ayu Nissa Ainni
Universitas Muhammadiyah Gombong
Biochemistry, Genetics & Molecular Biology Chemistry Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Public Health

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EVALUASI PENGGUNAAN ANTIVIRUS PASIEN COVID-19 DI INSTALASI RAWAT INAP RUMAH SAKIT DI DAERAH PURWOREJO TAHUN 2022 Vinca Elyana Purwantari; Ayu Nissa Ainni; Chondrosuro Miyarso; Wahidin Hidayat
Usadha Journal of Pharmacy Vol. 3 No. 1 (2024): Februari
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ujp.v3i1.304

Abstract

COVID-19 merupakan penyakit menular yang disebabkan oleh virus SARS-CoV-2. Kasus COVID-19 hingga saat ini masih adaan terapi COVID-19 masih beragam. Salah satu terapi yang digunakan adalah antivirus. Penelitian ini bertujuan untuk mengetahui pola penggunaan dan mengevaluasi ketepatan penggunaan antivirus pada pasien dewasa COVID-19 di instalasi rawat inap rumah sakit di daerah Purworejo Periode 2022. Metode penelitian ini menggunakan penelitian kualitatif dengan cara observasional menggunakan rancangan deskriptif non eksperimental dan bersifat rektrospektif. Data obat yang diperoleh dibandingkan dengan Pedoman Tatalaksana COVID-19 edisi IV Tahun 2022. Hasil Penelitian diperoleh sampel sebanyak 100 pasien COVID-19 di rawat inap Rumah Sakit (X) di Kabupaten Purworejo Periode 2022. Antivirus yang paling banyak diresepkan adalah Favipiravir sebanyak 97 pasien (97%) dan Remdesivir sebanyak 3 pasien (3%).  Evaluasi penggunaan obat Favipiravir dan Remdesivir berdasarkan Pedoman Tatalaksana COVID-19 edisi IV yaitu 100% tepat indikasi, tepat dosis, tepat obat, tepat cara pemberian, dan 92% tepat lama pemberian. Berdasarkan hasil evaluasi ketepatan penggunaan antivirus Favipiravir dan Remdesivir pada pasien dewasa COVID-19 yaitu tepat indikasi (100%), tepat obat (100%), tepat dosis (100%), tepat cara pemberian (100%), dan tepat lama pemberian (92%).
MODEL PREDIKSI FAKTOR-FAKTOR RISIKO OBESITAS MENGGUNAKAN MACHINE LEARNING Husnul Khuluq; Lazuardi Fatahillah Hamdi; Ayu Nissa Ainni; Tri Cahyani Widiastuti
Journal of Health Service Management Vol 29 No 00 (2026): Vol 29/Edisi Khusus/Februari/2026
Publisher : Departemen of Health Policy and Management, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta Jl. Farmako Sekip Utara Yogyakarta 55281 Telp 0274-547490

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jmpk.v29i00.25716

Abstract

Background: Obesity is a major global health concern and a key risk factor for various non-communicable diseases, including diabetes, hypertension, and cardiovascular disorders. Despite extensive studies, accurately identifying the key contributing factors remains a challenge. Objective: This study aims to predict the likelihood of obesity using a machine learning algorithm, based on questionnaire-derived clinical and behavioral data. Several supervised machine learning algorithms—logistic regression, naïve Bayes, support vector machine (SVM), and random forest—will be employed to build predictive models. Model performance will be evaluated using accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Methods: We used an open-access dataset from Kaggle comprising 2,111 samples with anthropometric, demographic, and lifestyle data. Of these, 972 individuals were categorized as obese and 1,139 as non-obese. The target variable was categorized into binary labels: "Obesity" and "Non-Obesity." Preprocessing included one-hot encoding, label encoding, and train-test splitting. All four ML models were trained and evaluated using accuracy, area under the curve (AUC), precision, sensitivity, and specificity metrics. Results: The model achieved an accuracy of 98.58%, AUC of 99.96%, sensitivity of 98.99%, specificity of 98.21%, and precision of 98.01%. The most influential predictors were weight, frequent consumption of high-caloric food, family history of being overweight, physical activity frequency, and daily water intake. Conclusion: The model demonstrated high performance and identified key lifestyle-related features. These findings support machine learning's potential for obesity screening and public health strategy development.
Co-Authors Chondrosuro Miyarso Husnul Khuluq Lazuardi Fatahillah Hamdi Tri Cahyani Widiastuti Vinca Elyana Purwantari Wahidin Hidayat