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All Journal Media Farmasi : Jurnal Ilmu Farmasi (Journal Of Pharmaceutical Science) jurnal informasi teknologi dan sistem
Maria Carolina
Fakultas Farmasi, Universitas Sanata Dharma Yogyakarta
Chemistry Computer Science & IT Control & Systems Engineering Immunology & microbiology Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology

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EVALUASI PENGGUNAAN ANTIBIOTIKA DENGAN METODE DDD (DEFINED DAILY DOSE) PADA PASIEN ANAK RAWAT INAP DI SEBUAH RUMAH SAKIT PEMERINTAH DI YOGYAKARTA PERIODE JANUARI - JUNI 2013 Carolina, Maria; Widayati, Aris
Media Farmasi: Jurnal Ilmu Farmasi Vol. 11 No. 1: Maret 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v11i1.1400

Abstract

Antibotika banyak diresepkan pada pasien anak. Penggunaan antibiotika yang berlebihan berkontribusi pada resistensi. Penelitian ini bertujuan mengevaluasi penggunaan antibiotika pada pasien anak rawat inap di sebuah rumah sakit pemerintah di Yogyakarta menggunakan metode DDD (Defined Daily Dose).Jenis dan rancangan penelitian adalah deskriptif cross-sectional,dengan data retrospektif. Data penggunaan antibiotika diperoleh dari 249 rekam medik periode rawat Januari – Juni 2013 yang dipilih dengan metode simple random sampling. Data yang diambil meliputi profil pasien, diagnosis, dan peresepan antibiotika. Kuantitas penggunaan antibiotika dihitung dengan rumus DDD 100 patient-days. Data dianalisis secara deskriptif.Hasil penelitian menemukan 28 jenis antibiotika yang diresepkan, dengan total nilai DDD 100 patient-days sebesar 41,99. Nilai DDD tertinggi yaitu ampisilin (10,33) dan merupakan antibiotika yang paling sering diresepkan (13,9%).Dapat dikatakan bahwa pemilihan antibiotika di rumah sakit tersebut masih belum selektif.
PENERAPAN METODE SUPPORT VECTOR MACHINE PADA APLIKASI DETEKSI TINGKAT KESEGARAN IKAN Carolina, Maria; Tendean, Sandi; Krisyesika, Krisyesika
INTEKSIS Vol 11 No 2: Nopember 2024
Publisher : LPPM Universitas Widya Dharma Pontianak

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Abstract

Indonesia is the second largest producer of marine fish in the world based on data from the Food and Agriculture Organization 2022. Fish is a food that contains excellent protein, fat, vitamins, and minerals. According to data from the Ministry of Marine Affairs and Fisheries, the national fish consumption rate continues to increase, reaching 57.27 kg/capita in 2022. Therefore, it is important to know which fish are fresh and suitable for consumption. In this research, fish freshness detection will be carried out using the Support Vector Machine (SVM) method. The detection process will be carried out using three classes, namely fresh, not fresh, and rotten. SVM is a linear classifier, but it is developed to work on nonlinear data with the concept of kernels in a high-dimensional workspace. The results of testing the SVM model show that the accuracy of the model with the Radial Basic Function (RBF) kernel produces 99 percent accuracy with parameters C = 10 and gamma = 0.5. Application testing using two smartphones with four tests resulted in 92 percent accuracy. Keywords: Support Vector Machine, Fish Freshness Detection, Web, Machine Learning
Co-Authors Aris Widayati Krisyesika, Krisyesika Tendean, Sandi