Jantung merupakan organ tubuh manusia yang mempunyai peran penting dalam kehidupan manusia dan pastinya sangat berbahaya jika jantung kita mempunyai masalah mengingat bahwa banyak kematian disebabkan oleh penyakit jantung. Tapi dengan pengetahuan dan informasi yang minim, mustahil untuk dapat menjaga kesehatan jantung. Oleh karena itu dibutuhkan seorang pakar yang ahli tentang jantung dan macam-macam penyakitnya. Berdasarkan fakta diatas, maka penelitian ini dapat membantu kita untuk mendiagnosa kesehatan jantung dan mengantisipasi jika mempunyai resiko penyakit jantung dengan merancang dan mengimplementasikan. Aplikasi ini dibuat berbasis web dengan menggunakan PHP dan database MySQL.  Pada sistem pakar ini akan diajukan beberapa pertanyaan. Setelah semua pertanyaan terjawab, maka akan tampak hasil diagnosa beserta saran yang dapat membantu mengantisipasi penyakit jantung tersebut.

Research and community service activities are the obligations of a lecturer that must be carried out from part of the Tri Dharma of Higher Education in addition to teaching, where research activities should have a level of innovation in the form of development or discovery of something new, but with a large number of lecturers, this results in research activities. and community service has many similarities with previous activities. At the Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Pamulang University, experiencing several problems in the management of research and community service activities, namely the absence of a system used to manage research and community service activities and data related to the track record of research and community service activities that have been carried out by 2,613 lecturers who impact on the difficulty in finding data, efficiency of storage space and more importantly is the number of similar proposals in the research itself. The research carried out aims to develop an information system that can process research and community service activities and detect similarities in content by applying the Cosine Similarity algorithm, so that it can overcome existing problems. The system development method uses a waterfall. From the results of making the system that has been carried out, it shows that the system is capable of processing activities in the field of research and community service carried out by lecturers, supporting storage, and facilitating screening of proposals for research and community service activities that will be approved.

Objective: this article was to describe nurses’ practice regarding postoperative pain management in Kebumen, IndonesiaMethod: a descriptive survey design was used in this study. The sample consisted of 65 bachelor nurses who were working in the postoperative wards of five hospitals and selected by convenience sampling. However, only 63 nurses returned the questionnaires. The instrument in this study developed by the researcher and had passed reliability process by involved the five experts. The data collected from October to November 2015. Spearman’s correlation used to determine the correlations between demographic data with nurses’ practice.Result: this study found that most of the subjects (66.7%) had a moderate level of practice (regarding postoperative pain management). There was no significantly between demographic data and practice. The others findings indicated that there was a significantly positive correlation between age and duration of working experience. On the other hand, there was a significantly negative correlation between duration of working experience and types of pain experienced by the nurse.Conclusion: the nurses showed that had a moderate level of postoperative pain management.

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Cardiac cine magnetic resonance imaging (MRI) is a widely used technique for the noninvasive assessment of cardiac functions. Deep neural networks have achieved considerable progress in overcoming various challenges in cine MRI analysis. However, deep learning models cannot be used for classification because limited cine MRI data are available. To overcome this problem, features from cine image settings are derived by handcrafting and addition of other clinical features to the classical machine learning approach for ensuring the model fits the MRI device settings and image parameters required in the analysis. In this study, a novel method was proposed for classifying heart disease (cardiomyopathy patient groups) using only segmented output maps. In the encoder–decoder network, the fully convolutional EfficientNetB5-UNet was modified to perform the semantic segmentation of the MRI image slice. A two-dimensional thickness algorithm was used to combine the segmentation outputs for the 2D representation of images of the end-diastole (ED) and end-systole (ES) cardiac volumes. The thickness images were subsequently used for classification by using a few-shot model with an adaptive subspace classifier. Model performance was verified by applying the model to the 2017 MICCAI Medical Image Computing and Computer-Assisted Intervention dataset. High segmentation performance was achieved as follows: the average Dice coefficients of segmentation were 96.24% (ED) and 89.92% (ES) for the left ventricle (LV); the values for the right ventricle (RV) were 92.90% (ED) and 86.92% (ES). The values for myocardium were 88.90% (ED) and 90.48% (ES). An accuracy score of 92% was achieved in the classification of various cardiomyopathy groups without clinical features. A novel rapid analysis approach was proposed for heart disease diagnosis, especially for cardiomyopathy conditions using cine MRI based on segmented output maps.