Background Inadequate sleep may affect mental, emotional, and
Hiperbilirubinemia neonatus merupakan masalah klinis yang umum dihadapi selama periode neonatal. Persalinan seksio sesarea adalah salah satu faktor maternal untuk terbentuknya hiperbilirubinemia. Kejadian hiperbilirubinemia yang diprediksi sebelum melahirkan dapat mencegah komplikasi dan untuk mengenali dengan cepat ikterus pada bayi baru lahir. Studi ini bertujuan untuk mengetahui hubungan persalinan seksio sesarea dengan hiperbilirubinemia neonatus. Studi cohort retrospektif ini dilakukan pada 124 neonatus yang mengalami hiperbilirubinemia di RS Sumber Waras pada tahun 2019. Pengambilan sampel menggunakan teknik non-random consecutive sampling. Data dianalisis dengan uji Mann-Whitney. Didapatkan 50,8% diantaranya bayi berjenis kelamin laki-laki, 74,2% dilahirkan secara seksio sesarea. Rata-rata pemeriksaan bilirubin dilakukan pada usia lima atau enam hari. Tingkat rata-rata kadar bilirubin total yang didapatkan adalah 14,62;4 mg/dL, dengan rata-rata kadar bilirubin direk adalah 0,64;0,4 mg/dL dan rata-rata kadar bilirubin indirek adalah 13,97;4 mg/dL. Sebanyak 25% bayi memiliki kadar bilirubin total antara 5-12 mg/dL, 70,2% antara 12-20 mg/dL dan 4.8% bayi memiliki kadar bilirubin diatas 20 mg/dL. Tingkat rata-rata kadar bilirubin total pada kelompok seksio searea dan spontan masing-masing adalah 14,39 mg/dL dan 15,3 mg/dL (p value = 0,239). Tidak terdapat hubungan yang bermakna antara persalinan seksio sesarea dengan hiperbilirubinemia neonatus.
Based on a report submitted by Truecaller Insights Report 2020, Indonesia placed sixth position with the most spam messages, one of the spam applications is SMS. Spam SMS contains unwanted or unsolicited messages, including advertisements, scams and so on. The existence of this spam message causes inconvenience from the user's side when receiving spam SMS, and some even become victims of crime after responding to the SMS. To minimize inconvenience and crime caused by spam messages, the purpose of this study is to filter SMS spam or SMS filtering by classifying SMS spam using the Multinomial Naïve Bayes algorithm by looking for the best combination of parameters to improve the performance of the model that is formed. The results of model testing get the highest precision value in the MNB and SVM models by 93%, the highest recall value in the SVM model at 94%, the highest f1-score value in the SVM model at 94%, the highest accuracy value in the SVM model at 95%, and the fastest test time on the MNB model is 2.66 ms
The process of manually prescribing drugs by doctors can cause several problems, including doctors not knowing what drugs are available and it takes time to find out what drugs are available in the pharmacy. Speech recognition is now widely used in various ways, which can help facilitate work. The application of speech recognition can be done in the e-prescribing application with the neural network method using the Convolutional Neural Network (CNN) algorithm, which is the basic method of deep learning. This study aims to facilitate physicians in filling out drug data in e-prescribing applications using speech recognition. The data used in this study were obtained from the open source dataset provided by Google and collected independent datasets. From the results of experiments that have been carried out, the accuracy achieved with 40 epochs and 40 direct impressions with different words is 90%. Where words are successfully recognized 36 words out of 40 words
The need for fast and accurate information has become the need of every company, including hospitals. This is one of the factors that makes a company superior to other companies. In making the right and accurate decisions, leaders need information that is presented clearly, easily understood, on time, and in accordance with needs. To support the presentation of such information a database, data warehouse and other applications that are easy to understand are needed. Hospitals as a socio-economic institution are not only required to provide solutions to health problems but are also demanded to always improve the quality of their services. For this purpose to be achieved, its management must be efficient. Service indicators can be used as a measuring tool to assess the level of management efficiency. Business Intelligence (BI) application is one form of implementation that is able to facilitate management to monitor hospital performance. This research explores the use of information technology to build BI applications, reviewing the right approach in building BI applications, as well as several important aspects that must be considered for the system work for the hospital environment. There are two main stages in building this application, namely: building a data warehouse originating from an electronic medical record database and hospital operations, and building a BI application. With the formation of this BI application, it is very useful for hospital management in managing their institutions better
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