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"Latar Belakang : Lima belas persen (15%) pasien yang terinfeksi COVID- 19 jatuh ke dalam kondisi penyakit yang berat dan memerlukan suplementasi oksigen (O2). Lima persen (5%) lainnya mengalami perburukan lebih lanjut dan jatuh ke dalam penyakit kritis dengan komplikasi. Pemberian terapi O2 dilakukan segera kepada pasien dengan atau tanpa tanda-tanda kegawatdaruratan dengan saturasi oksigen (SpO2) < 90%. Penelitian ini bertujuan untuk mengetahui hubungan antara Skor Acute Physiology and Chronic Health Evaluation (APACHE) II dengan kejadian desaturasi pada pasien Pneumonia COVID-19.Metode : Penelitian ini merupakan penelitian analitik observasionalkohort prospektif yang dilakukan di Instalasi Gawat Darurat (IGD) dan ruang rawat inap RSUP Persahabatan periode 31 Juli 2021 – 30 September 2021. Subjek penelitian didapatkan dari pasien yang datang ke IGD RSUP Persahabatan sejak 30 Juli 2021 – 30 September 2021 dan terdiagnosis COVID-19 dari hasil pemeriksaan PCR usap tenggorok positif. Dilakukan pengumpulan data klinis, tanda vital, pemeriksaan penunjang dan Skor APACHE II sejak subjek tiba di IGD hingga masuk ruang rawat dalam 24 jam pertama. Hasil : Pada penelitian ini didapatkan 100 subjek penelitian. Hasil penelitian ini menyatakan bahwa tidak terdapat hubungan yang bermakna antara Skor APACHE II dengan kejadian desaturasi pada pasien Pneumonia COVID-19 (p 0,257). Selain itu, tidak ditemukan perbedaan bermakna skor APACHE II pada kelompok pasien dengan derajat keparahan COVID-19 yang berbeda pada PaO2 sesuai hasil pemeriksaan AGD (p 0,073) namun didapatkan hubungan yang bermakna pada penggunaan PaO2 seusai kurva disosiasi O2 (p <0,001).Kesimpulan : Tidak terdapat hubungan yang bermakna antara SkorAPACHE II dengan kejadian desaturasi pada pasien Pneumonia COVID-19.......Background : Fiftheen percents (15%) patients infected with COVID-19falls to severe disease and require oxygen (O2) therapy and the other 5% sufferedprogressive worsening to critical disease with complications. Oxygen therapyconducted in patients with or without emergency condition with low O2 saturation(SpO2 < 90%). This study aim to determine the correlation between AcutePhysiology and Chronic Health Evaluation (APACHE) II score with desaturationin Pnuemonia COVID-19 patients.Methods : A prospective cohort observational analytic studyconducted at National Respiratory Ceter Persahabatan Hospital Emergency Unitfrom July 2021-September 2021. The study subjects were patients admitted toEmergency Unit and diagnosed with COVID-19 from positive result ofnasopharing PCR swab. Clinical data, vital signs, supportive examination andAPACHE II score are collected since Emergency Unit admission to inward unit infirst 24 hours.Results : There were 100 subjects participating in this study. Theresult stated there were no significant correlation between APACHE II Score withdesaturation in Pneumonia COVID-19 patients (p-value 0,257). There was also nosignificant correlation between APACHE II score with disease severity of COVID-19 based on O2 partial pressure collected from blood gas analysis examination (pvalue0,073) but there was a significant correlation between APACHE II score withdisease severity of COVID-19 based on O2 partial pressure referred to O2dissociation curve (p-value <0,001).Conclusion : There was no significant correlation between APACHE IIScore with desaturation in Pneumonia COVID-19 patients"

"ABSTRAKPendahuluan: Infeksi paru merupakan penyebab morbiditas dan mortalitas terbesar di Intensive Care Unit ICU. Pasien ICU umumnya dalam kondisi critically ill dan riwayat penggunaan antibiotic sebelumnya sehingga memiliki risiko resistensi terhadap antibiotik yang berpengaruh terhadap luaran pasien.Sistem skoring digunakan di ICU untuk menilai derajat keparahan penyakit dan luaran pasien. Penelitian ini menilai eta kuman pasien infeksi paru dan hubungannya dengan derajat keparahan penyakit yang dinilai dengan skor APACHE II. Metode: Penelitian ini menggunakan metode potong lintang yang dilaksanakan pada bulan Agustus-September 2017 di ICU RSUP Persahabatan. Total subjek terdiri atas 59 subjek dengan cara pengambilan sampel consecutive sampling. Semua pasien didiagnosis infeksi paru oleh dokter spesialis dan dikonfirmasi melalui foto toraks kemudian dinilai derajat keparahan penyakit dengan skor APACHE II dan dilakukan biakan dan resistensi mikroorganisme. Hasil: Kuman yang banyak ditemukan merupakan gram negative (37,2%) dengan risiko mortalitas tertinggi 75% jenis Acinetobacter pada skor (APACHE II 30-34). Rerata skor APACHE II 15,78+ 6,04 dengancut off point skor APACHE II 16,5 dan skor APACHE II >16 memiliki mortalitas terbesar (64%) (p=0,032). Diagnosis infeksi paru dengan mortalitas terbesar didapatkan pada CAP (56%). Kesimpulan: Acinetobacter baumanii merupakan kuman terbanyak yang menyebabkan kematian pada pasien infeksi paru dan skor APACHE II merupakan prediktor yang baik dalam menilai derajat keparahan penyakit dan luaran pasien.

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ABSTRACT<>br>Introduction: Lung infection are the most common cause of high mortality and morbidity in Intensive Care Unit (ICU). Patients in ICU mostly critically ill with history of antibiotic use and risk of drug resistant that will influence the outcome of the patients. Scoring system used in ICU to measure severity of the disease and the outcome of the patients. This study asseses the microbiological pattern of patients with lung infection and severity of the disease using APACHE II Score. Methods: This study used cross sectional methods that heldbetween August 2017-September 2017in Persahabatan Hospital Intensive Care Unit. Total subjects consisted of 59 patients with lung infection base on consecutive sampling. All of the patients diagnosed with lung infection from specialist and confirmed with radiological findings, measured the APACHE II Score and performed sputum culture and resistance. Results: The most common isolation found in lung infection patients was gram negative (37,2%) with mortality risk of Acinetobacter baumanii75% (APACHE II Score 30-34). Mean APACHE II Score was 15,78+ 6,04 with cut off point APACHE II Score 16,5 and APACHE II Score > 16 has the highest mortality (64%) (p=0,032). Diagnose of lung infection with the highest mortality found in patients with CAP (56%). Conclusions: Acinetobacter baumanii are the most common cause of mortality in lung infection patients. The APACHE II Score has good predictor in measure severity of the diseases and the outcome of the patients."

"Latar Belakang:.Pembedahan kepala dan leher merupakan tindakan yang kompleks dan penuh tantangan karena berhubungan dengan pencernaan dan pernapasan. Dengan angka kejadian komplikasi yang cukup tinggi yaitu 17%, dibutuhkan tolok ukur yang dapat memprediksi komplikasi pascabedah terutama di bidang bedah mulut dan maksilofasial. Sistem skoring APACHE II pada penelitian terdahulu terbukti efektif dalam memprediksi kejadian komplikasi pascabedah reseksi dan rekonstruksi mandibula. Tujuan Penelitian: Mengetahui efektivitas sistem skoring APACHE II sebagai prediktor komplikasi pascabedah reseksi dan rekosntruksi tumor jinak mandibula. Metode Penelitian: Studi retrospektif tahun 2015 – 2020 pada subjek yang memenuhi kriteria inklusi. Data diambil dari rekam medis pasien baik tertulis maupun digital. Analisis variabel kategorik dengan Uji Chi Square. Uji Mann-Whitney U untuk perbandingan rerata skor dua kelompok. Efektivitas skor APACHE II dinilai berdasarkan kurva ROC dan luas area dibawah kurva. Hasil: Dari 62 subjek penelitian, sebanyak 6 responden (9.7%) mengalami komplikasi pascabedah. Tidak terdapat perbedaan bermakna antara semua variabel independen yang duji dengan peningkatan skor APACHE II (nilai p > 0.05). Rerata skor pada kelompok komplikasi lebih tinggi (4.83) namun pada Uji Mann-Whitney U nilai p > 0.05. Analisis ROC pada studi ini memiliki sensitivitas 50% dan spesifisitas 78.6% dengan nilai cut off point 5.5 dan luas area dibawah kurva ROC sebesar 0.558. Kesimpulan: Sistem skoring APACHE II terbukti efektif dalam memprediksi kejadian komplikasi pascabdedah reseksi dan rekonstruksi tumor jinak mandibula.......Background: Head and neck surgery is a complex and challenging procedure because it affect the digestion and respiration organ system. With a fairly high incidence of complications, namely 17%, an indicator is needed to predict postoperativecomplications, especially in the field of oral and maxillofacial surgery. The APACHE II scoring system in a previous study proved to be effective in predicting the incidence of postoperative complications after mandibular resection and reconstruction. Objective: To determine the effectiveness of the APACHE II scoring system as a predictor of postoperative complications of mandibular resection and reconstruction of benign tumors. Methods: Retrospective study on subjects who met the inclusion criteria in the period of 2015 – 2020. The data is collected from the patient's medical record, both written and digital. Categorical variable is being analyze with Chi Square Test. While Mann-Whitney U test analyzing the comparison of the mean scores of the two groups. The effectiveness of the APACHE II score was assessed based on the ROC curve and the area under the curve. Results: Of the 62 research subjects, 6 respondents (9.7%) experienced postoperative complications. There was no significant difference between all tested independent variables with an increase in the APACHE II score (p value > 0.05). The mean score in the complication group was higher (4.83) but in the Mann-Whitney U test the p value was > 0.05. The ROC analysis in this study has a sensitivity of 50% and a specificity of 78.6% with a cut off point value of 5.5 and an area under the ROC curve of 0.558. Conclusion: The APACHE II scoring system proved to be effective in predicting the incidence of postoperative complications after surgical resection and reconstruction of benign mandibular tumors."

"Dengan bertambahnya jumlah pengguna internet seiring waktu, bertambah pula jumlah data yang dihasilkan dari aktivitas yang berasal dari media sosial. Data tersebut dapat digunakan untuk berbagai keperluan, salah satunya untuk keperluan moderasi supaya aktivitas pengguna internet tetap tunduk pada hukum yang berlaku. Namun, diperlukan cara yang optimal untuk melakukan proses tersebut mengingat data yang jumlahnya sangat besar. Penelitian ini mengajukan sebuah platform yang dapat menjadi salah satu pilihan untuk memproses data media sosial yang berjumlah besar tersebut. Rancangan platform pada penelitian ini ditujukan untuk dapat memiliki throughput yang besar. Selain itu, platform dirancang untuk dapat dimodifikasi demi memenuhi berbagai kebutuhan. Karenanya, aspek extensibility juga menjadi perhatian utama dalam proses pengembangan platform. Kedua tujuan utama dalam pengembangan platform ini dapat diwujudkan dengan bantuan sebuah klaster Apache Kafka yang membuat platform memiliki sifat loosely-coupled dan juga extensible. Dengan berpusat pada klaster Apache Kafka, proses pengolahan data yang ada dapat dilakukan secara paralel, dan terbukti dapat meningkatkan throughput dari platform secara keseluruhan. Sebagai pembanding, penelitian ini diuji coba dengan suatu skenario bersama dengan platform Tweetream yang dikembangkan oleh Susanto (2022). Hasil dari uji coba tersebut membuktikan bahwa platform pada penelitian ini dapat mengungguli Tweetream.......As the number of internet users increases over time, so does the amount of data generated from activities originating from social media. This data can be used for various purposes, one of which is for moderation purposes so that the activities of internet users remain subject to applicable laws. However, an optimal way to do the process is needed considering the huge amount of data. This research proposes a platform that can be one of the options for processing large amounts of social media data. The design of the platform in this research is intended to have a large throughput. In addition, the platform is designed to be modifiable to meet various needs. Therefore, extensibility is also a major concern in the platform development process. These two main objectives in the development of the platform can be realized with the help of an Apache Kafka cluster that makes the platform loosely-coupled and extensible. By centering on the Apache Kafka cluster, the data processing can be done in parallel, which has been proven to increase the throughput of the platform as a whole. For comparison, this study was tested in a scenario with the Tweetream platform developed by Susanto (2022). The results of the test proved that the platform in this study can outperform Tweetream."

"Cloud Computing menyediakan akses mudah ke berbagai sumber daya melalui jaringan internet, dengan jangkauan jaringan yang luas, kemampuan elastisitas yang cepat, dan layanan yang dapat diukur. Dalam penelitian ini, digunakan Cloud Management Platform Apache CloudStack untuk membangun sistem Cloud sendiri. Sistem ini menggunakan sistem operasi Ubuntu dan hypervisor KVM (Kernel-based Virtual Machine) untuk menguji performa dan optimalisasi konfigurasi hypervisor dalam menjalankan tugas-tugas komputasi tertentu. Penelitian ini bertujuan untuk menganalisis apakah konfigurasi default dari hypervisor KVM sudah optimal dan bagaimana konfigurasi hypervisor KVM dapat meningkatkan performa dalam menjalankan tugas komputasi seperti kompresi video, enkripsi, dekripsi, dan validasi integritas data. Metode yang digunakan melibatkan pengujian performa hypervisor KVM dengan melakukan berbagai tugas komputasi dan mengukur waktu yang diperlukan untuk menyelesaikan tugas-tugas tersebut. Hasil pengujian menunjukkan bahwa konfigurasi hypervisor dengan menambahkan flag tertentu seperti SSSE3, SSE4.1, SSE4.2, SSE4a, dan AES dapat meningkatkan performa komputasi secara signifikan. Kesimpulan dari penelitian ini adalah bahwa konfigurasi default hypervisor KVM bersifat minimal dan perlu dilakukan konfigurasi untuk mencapai performa optimal. Konfigurasi hypervisor yang tepat dapat mempercepat kompresi video hingga 2.4 kali, validasi integritas data hingga 7.56 kali, enkripsi AES hingga 2.1 kali, dan dekripsi AES hingga 2.68 kali dibandingkan dengan konfigurasi default. Penelitian ini diharapkan memberikan kontribusi akademis dalam bidang optimalisasi infrastruktur cloud.......Cloud Computing provides easy access to various resources over the internet, with a wide network reach, fast elasticity capabilities, and measurable services. In this research, the Apache CloudStack Cloud Management Platform is utilized to build a Cloud system. This system uses the Ubuntu operating system and KVM (Kernel-based Virtual Machine) hypervisor to test performance and optimize the hypervisor configuration for specific computing tasks. The aim of this research is to analyze whether the default configuration of the KVM hypervisor is optimal and how configuration the KVM hypervisor can enhance performance in executing computing tasks such as video compression, encryption, decryption, and data integrity validation. The methodology involves performance testing of the KVM hypervisor by executing various computing tasks and measuring the time taken to complete these tasks. The test results indicate that configuring the hypervisor by adding specific flags such as SSSE3, SSE4.1, SSE4.2, SSE4a, and AES can significantly improve computational performance. The conclusion drawn from this research is that the default configuration of the KVM hypervisor is minimal and requires configuration to achieve optimal performance. Proper hypervisor configuration can accelerate video compression by up to 2.4 times, data integrity validation by up to 7.56 times, AES encryption by up to 2.1 times, and AES decryption by up to 2.68 times compared to the default configuration. This research is expected to make an academic contribution in the field of cloud infrastructure optimization."

"ABSTRACTPurposesVascular surgery (VS) has a higher perioperative mortality than other types of surgery. We compared different scores for predicting mortality in patients admitted to the intensive care unit (ICU) after open VS.MethodsPatients admitted to the ICU after open VS from 2006 to 2013 were included. We calculated the Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Preoperative Score to Predict Postoperative Mortality (POSPOM). We performed multivariate logistic regression to assess independent factors with the calculation of odds ratios (ORs) and 95% confidence intervals (CIs). We tested the predictive ability of the scores using the area under the receiver operating characteristics curve (AUROC).ResultsA total of 833 consecutive patients were included. Hospital mortality was 5,1% (1,3% after intermediate-risk and 8,4% after high-risk surgery). In the multivariate analysis, the age (OR 1,04, 95% CI 1,01-1,08, p = 0,013), smoking status (OR 2,46, 95% CI 1,16-5,21, p = 0,019), surgery risk (OR 2,92, 95% CI 1,058,08, p = 0,040), serum sodium level (OR 1,17, 95% CI 1,10-1,26, p < 0,001), urea (OR 1,01, 95% CI 1,01-1,02, p = 0,001) and leukocyte count (OR 1,05, 95% CI 1,01-1,10, p = 0,009) at admission were considered independent predictors. Hematocrit (0,86, 95% CI 0,80-0,93, p < 0.001) was considered an independent protective factor. The AUROC of our model was 0,860, compared to SAPS (0,752), APACHE (0,774), POSPOM (0,798) and POSSUM (0,829).ConclusionThe observed mortality was within the predicted range (1-5% after intermediate-risk and > 5% after high-risk surgery). POSSUM and POSPOM had slightly better predictive capacity than SAPS or APACHE."

"The dynamic development of digital technologies, especially those dedicated to devices generating large data streams, such as all kinds of measurement equipment temperature and humidity sensors, cameras, radio telescopes and satellites Internet of Things enables more in depth analysis of the surrounding reality, including better understanding of various natural phenomenon, starting from atomic level reactions, through macroscopic processes e.g. meteorology to observation of the Earth and the outer space. On the other hand such a large quantitative improvement requires a great number of processing and storage resources, resulting in the recent rapid development of Big Data technologies. Since 2015, the European Space Agency ESA has been providing a great amount of data gathered by exploratory equipment a collection of Sentinel satellites which perform Earth observation using various measurement techniques. For example Sentinel 2 provides a stream of digital photos, including images of the Baltic Sea and the whole territory of Poland. This data is used in an experimental installation of a Big Data processing system based on the open source software at the Academic Computer Center in Gdansk. The center has one of the most powerful supercomputers in Poland the Tryton computing cluster, consisting of 1600 nodes interconnected by a fast Infiniband network 56 Gbps and over 6 PB of storage. Some of these nodes are used as a computational cloud supervised by an OpenStack platform, where the Sentinel 2 data is processed. A subsystem of the automatic, perpetual data download to object storage based on Swift is deployed, the required software libraries for the image processing are configured and the Apache Spark cluster has been set up. The above system enables gathering and analysis of the recorded satellite images and the associated metadata, benefiting from the parallel computation mechanisms. This paper describes the above solution including its technical aspects."