Optimasi Parameter Model Support Vector Regression Untuk Pemodelan Beban Listrik Di Empat Belas Wilayah Di Jawa Timur Dengan Menggunakan Genetic Algorithm Dan Particle Swarm Optimization

Beban listrik merupakan salah satu kebutuhan yang dibutuhkan masyarakat. Hal tersebut diketahui dengan meningkatnya permintaan beban listrik dari tahun ke tahun. Dengan adanya peningkatan beban listrik, PLN perlu melakukan tindakan antisipasi akan adanya peningkatakan tersebut karena kemampuan yang terbatas dalam penyediaan beban listrik. Langkah antisipasi yang dapat dilakukan adalah dengan melakukan peramalan beban jangka pendek. ARIMA merupakan metode peramalan dan memiliki kelemahan terhadap pola nonlinier dan pada penelitian sebelumnya, beban listrik memiliki pola nonlinier. Metode SVR merupakan metode yang memiliki fungsi kernel RBF (Gaussian) yang bisa menangani pola nonlinier. Peramalan akan dilakukan dengan menggunakan lag yang signifikan sebagai input pada SVR. SVR memiliki masalah pada penentuan nilai parameternya sehingga perlu dioptimasi menggunakan GA dan PSO. GA dan PSO merupakan sebuah metode optimasi yang menghasilkan nilai akurasi yang baik dan menghasilkan nilai yang global optimum. Kriteria yang digunakan untuk membandingkan antara kedua optimasi tersebut adalah nilai RMSE dan SMAPE. Dalam penelitian ini diperoleh kesimpulan bahwa metode optimasi GA merupakan metode terbaik karena menghasilkan ramalan dengan nilai RMSE dan sMAPE lebih rendah. ==================================================================== Electricity load is one of the needs that society needs. This is known by the increasing demand of electrical load from year to year. With the increase in electricity load, PLN needs to take action to anticipate the increase because of the limited ability to supply electrical load. Anticipation steps that can be done is to do forecasting short-term expenses. ARIMA is a method of forecasting and has a weakness to nonlinear pattern and in previous research, electric load has nonlinear pattern. The SVR method is a method that has the function of the RBF (Gaussian) kernel that can handle nonlinear patterns. Forecasting will be done by using a significant lag as input on the SVR. SVR has problems with determining parameter values so it needs to be optimized using GA and PSO. GA and PSO are optimization method that produces good accuracy value and produces global optimum value. The criteria used to compare between the two optimizations are RMSE and SMAPE values. In this research, it can be concluded that GA optimization method is the best method because it yields forecast with lower RMSE and sMAPE valu

Pembayaran Ganti Kerugian Terhadap Korban Kecelakaan Lalu Lintas Dalam Konstelasi Peradilan Pidana (Studi Kasus Pengadilan Negeri Sukoharjo)

Apakah pembayaran ganti kerugian merupakan kewajiban dari pelaku kepada korban, atau kepada keluarga korban ? atau hanya sebatas kerelaan semata ? Itu beberapa pertayaan yang sering timbul ditengah masyarakat. Rujukan mengenai ganti rugi telah diatur dalam UU No. 22 Tahun 2009 tentang Lalu Lintas Angkutan Jalan (LLAJ) dimana sipembuat (pelaku) dapat dikenai tuntutan perdata atas kerugian yang ditimbulkan. Tujuan dari penulisan skripsi ini adalah untuk mengetahui bagaimana proses pembayaran ganti kerugian terhadap korban kecelakaan lalu lintas, lalu untuk mengetahui bagaimana pertimbangan-pertimbangan hakim dalam menjatuhkan putusannya apabila para pihak telah berdamai. Metode penelitian yang digunakan dalam penelitian ini adalah penelitian yuridis empiris metode penelitian ini mengkaji konsep normatif, atau yuridis mengenai pembayaran ganti kerugian terhadap korban kecelakaan lalu lintas dalam konstelasi peradilan pidana. Jenis penelitian yang digunakan adalah penelitian deskriftif yang menggabunggkan data-data kepustakaan dengan datadata yang diperoleh dari lapangan (wawancara) dengan menggunakan data hukum primer dan bahan hukum sekunder

YUK LES: INFORMATION SYSTEMS ON ONLINE PRIVATE COURSE SERVICES BASED ON MOBILE APPLICATION

One way to improve students' academic and non-academic abilities is by taking tutoring. Tutoring is an effort to achieve maximum learning outcomes in accordance with the field of interest. In the era of technological revolution 4.0, we are always demanded to be up to date on their abilities in non-academic / soft skills such as video editing, programming, dance, music, multimedia, etc. Education 4.0 should be able to provide easy access to education and mobility in learning formal and non-formal fields. The application "Yuk Les" brings together the community / students who need private tutoring and people who have abilities in various non-academic fields based on Android. With this application, it is expected to facilitate the public in finding private lessons in accordance with the needs of the field of interest and opening new jobs for the community / students who have abilities in non-academic fields

IMPLEMENTASI JAMINAN FIDUSIA DI PEGADAIAN SYARIAH SOLO BARU

ur riba atau haram maka masyarakat muslim memerlukan lembaga pembiayaan dengan system syariah. Tujuan penelitian untuk mengetahui implementasi dan perbedaan jaminan fidusia di PT. Pegadaian Persero Syariah dan PT. Pegadaian Persero Konveisonal, serta upaya yang ditempuh agar debitor tidak wanprestasi. Metode pendekatan, meggunakan metode socio legal. Meneliti norma yang berlaku, diperoleh menggunakan study pustaka (libray research) dengan kajian dokumen (dokumen review). Serta penerapan hukum dalam masyarakat dengan kajian wawancara/ observasi partisipasi. Berdasarkan penelitian kualitatif diperoleh, jaminan fidusia di PT. Pegadaian Persero Syariah Solo Baru diterapkan dengan Fatwa No 68/DSN- MUI/III/2008 Tentang Rahn Tasjily dan perbedaan terletak pada nilai tafsirbarang anggunan, serta upaya yang ditempuh agar debitor tidak wanprestasi yaitu dengann adaya perjanjian klausula baku dan pemberitahuan ketika akan memasuki masa jatuh tempo. Saran bagi notaris, untuk terpenuhinya asas publisitas pada hakekatnya sertifikat fidusia tidak hanya dapat dilihat oleh noataris yang membuat saja, meliakan oleh masyarakt umum. Hal ini untuk adanaya keterbukaan dan juga agar berlakunya sertifikat tersebut mengikat pihak ketiga

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

Thrombotic and hemorrhagic complications of COVID-19 in adults hospitalized in high-income countries compared with those in adults hospitalized in low- and middle-income countries in an international registry

Background: COVID-19 has been associated with a broad range of thromboembolic, ischemic, and hemorrhagic complications (coagulopathy complications). Most studies have focused on patients with severe disease from high-income countries (HICs). Objectives: The main aims were to compare the frequency of coagulopathy complications in developing countries (low- and middle-income countries [LMICs]) with those in HICs, delineate the frequency across a range of treatment levels, and determine associations with in-hospital mortality. Methods: Adult patients enrolled in an observational, multinational registry, the International Severe Acute Respiratory and Emerging Infections COVID-19 study, between January 1, 2020, and September 15, 2021, met inclusion criteria, including admission to a hospital for laboratory-confirmed, acute COVID-19 and data on complications and survival. The advanced-treatment cohort received care, such as admission to the intensive care unit, mechanical ventilation, or inotropes or vasopressors; the basic-treatment cohort did not receive any of these interventions. Results: The study population included 495,682 patients from 52 countries, with 63% from LMICs and 85% in the basic treatment cohort. The frequency of coagulopathy complications was higher in HICs (0.76%-3.4%) than in LMICs (0.09%-1.22%). Complications were more frequent in the advanced-treatment cohort than in the basic-treatment cohort. Coagulopathy complications were associated with increased in-hospital mortality (odds ratio, 1.58; 95% CI, 1.52-1.64). The increased mortality associated with these complications was higher in LMICs (58.5%) than in HICs (35.4%). After controlling for coagulopathy complications, treatment intensity, and multiple other factors, the mortality was higher among patients in LMICs than among patients in HICs (odds ratio, 1.45; 95% CI, 1.39-1.51). Conclusion: In a large, international registry of patients hospitalized for COVID-19, coagulopathy complications were more frequent in HICs than in LMICs (developing countries). Increased mortality associated with coagulopathy complications was of a greater magnitude among patients in LMICs. Additional research is needed regarding timely diagnosis of and intervention for coagulation derangements associated with COVID-19, particularly for limited-resource settings

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83-7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97-2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14-1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25-1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable