Hasil Pencarian  ::  Simpan CSV :: Kembali

"Indonesia merupakan negara kepulauan yang memiliki keberagaman budaya. Kebudayaan yang dimiliki dapat mempengaruhi pandangan sehat dan sakit seseorang, sehingga dibutuhkan adanya asuhan keperawatan peka budaya. Asuhan keperawatan peka budaya membutuhkan pengetahuan transkultural sehingga dapat menghasilkan self efficacy dalam mengaplikasikannya. Penelitian ini bertujuan untuk mengetahui hubungan antara pengetahuan keperawatan transkultural dengan self efficacy dalam melakukan asuhan keperawatan peka budaya. Penelitian ini melibatkan 119 orang mahasiswa profesi baik asal program ekstensi maupun reguler menggunakan desain cross sectional dengan teknik total sampling. Hasil penelitian menunjukkan bahwa terdapat hubungan yang bermakna antara pengetahuan keperawatan transkultural yang dimiliki dengan self efficacy dalam melakukan asuhan keperawatan peka budaya pada mahasiswa Profesi FIK UI angkatan 2015 (p value = 0,009). Penelitian ini merekomendasikan agar dilakukannya integrasi asuhan keperawatan peka budaya dalam kurikulum keperawatan lainnya. Penelitian pada unsur kompetensi budaya yang lain diperlukan pada penelitian selanjutnya.

---

Indonesia is an archipelago country which has multicultures. Culture can affect someone's view about health and illness, therefore cultural sensitive nursing care is needed. Cultural sensitive nursing care need transcultural knowledge to attain self efficacy in applying. The aim of this research was to discover the relationship between transcultural nursing knowledge with self efficacy in applying cultural sensitive nursing care. This research involved 119 nursing profession students from extension and regular program using cross sectional design with total sampling technique.

The result of this research showed that there was a significant relationship between transcultural nursing knowledge with self efficacy in applying cultural sensitive nursing care among nursing profession student, faculty of nursing universitas indonesia 2015 (p value = 0,009). This research is recomended the integration transcultural nursing care in the other curriculum nursing. Any further researches in other cultural competence is needed."

"In developing professionalism dental health services for patient, which it uses the transculture approach as a theory foundation in giving dental proffesional guidance. It is hope that the fifth of subsystems inside professional values, relationships between professional, management approach, decission making system and dental health care method will be success. Transcultural dental health services is implemented in accordance with the background of patient's culture thereby possibility allowing the acceptable treatment and care in accordance with is desired."

"Indonesia masuk kedalam 20 besar Negara terbanyak tekonfirmasi kasus COVID-19. Hingga 17 Mei 2021, sudah tercatat lebih dari 1 juta kasus COVID-19 terkonfirmasi dengan jumlah kematian mencapai lebih dari 48 ribu orang. tes Reverse transcriptionpolymerase chain reaction (RT-PCR) merupakan standarisasi alat pendeteksian dini COVID-19. Namun, tes RT-PCR membutuhkan waktu waktu yang lama dan biaya yang mahal dalam mengidentifikasi tahap awal kasus COVID-19. Hal ini mengakibatkan peningkatan jumlah pasien COVID-19 terjadi terus menerus secara eksponensial dan terjadi antrian panjang dalam menggunakan Intensive Care Unit (ICU) pada rumah sakit. Oleh karena itu, dibutuhkan alat pendeteksian dini yang cepat dan akurat salah satunya menggunakan citra chest X-ray. Deep learning merupakan salah satu metode klasifikasi citra yang populer dan terbukti baik dalam melakukan klasifikasi citra medis, salah satunya adalah Capsule Network (CapsNet) yang merupakan pengembangan dari Convolutional Neural Network (CNN). Pada penelitian ini, penulis menggunakan model CapsNet berbasis Inception V3 dengan optimasi AdaBelief (Adapting Stepsizes by the Belief in Observed Gradients) untuk mengklasifikasikan citra chest X-ray COVID-19. Data yang digunakan dalam penelitian ini diambil dari situs kaggle yang berisi citra chest X-ray COVID-19. Selanjutnya, dilakukan tahap pra-pengolahan pada citra seperti resize dan menerapkan normalisasi agar semua citra dapat diinput ke dalam model dengan ukuran yang sama serta meningkatkan kinerja model. Hasil penelitian ini menunjukkan model CapsNet berbasis Inception V3 dengan optimasi AdaBelief pada dataset citra chest X-ray COVID-19 dapat mengklasifikasikan data menjadi dua kelas, yaitu normal dan COVID-19, sehingga dapat membantu untuk mendiagnosis COVID-19 dengan baik. Selain itu, hasil terbaik juga diperoleh oleh model CapsNet berbasis Inception V3 dengan optimasi AdaBelief berdasarkan nilai loss sebesar 0,2703 dan akurasi 88,75% dibandingkan metode CapsNet tradisional dan berbagai optimasi lainnya untuk mengklasifikasikan dataset citra chest X-ray COVID-19.

---

Indonesia is in the top 20 countries with the most confirmed cases of COVID-19. As of May 17, 2021, there have been more than 1 million confirmed cases of COVID-19 with the death toll reaching more than 48 thousand people. Reverse transcription polymerase chain reaction (RT-PCR) test is a standardization tool for early detection of COVID-19. However, the RT-PCR test takes a long time and is expensive to identify early stages of COVID-19 cases. This resulted in an exponential increase in the number of COVID-19 patients and long queues for using the Intensive Care Unit (ICU) at the hospital. Therefore, a fast and accurate early detection tool is needed, one of which is using chest X-ray images. Deep learning is one of the popular image classification methods and has proven good in classifying medical images, one of which is the Capsule Network (CapsNet) which is the development of the Convolutional Neural Network (CNN). In this study, the authors used the Inception V3-based CapsNet model with AdaBelief optimization (Adapting Stepsizes by the Belief in Observed Gradients) to classify COVID-19 chest X-ray images. The data used in this study was taken from the kaggle site which contains COVID-19 chest Xray images. Furthermore, pre-processing is carried out on the image such as resizing and applying normalization so that all images can be inputted into the model with the same size and improve model performance. The results of this study show that the Inception V3-based CapsNet model with AdaBelief optimization on the COVID-19 chest X-ray image dataset can classify data into two classes, namely normal and COVID-19, so that it can help to diagnose COVID-19 properly. In addition, the best results were also obtained by the Inception V3-based CapsNet model with AdaBelief optimization based on a loss value of 0,2703 and 88,75% accuracy compared to traditional CapsNet methods and various other optimizations to classify COVID-19 chest X-ray image datasets."