8 research outputs found
Prediksi Nilai Tukar Dolar Amerika Serikat Terhadap Rupiah Menggunakan Metode Support Vector Regression Dengan Whale Optimization Algorithm
No full textSetiap negara melakukan perdagangan dengan negara lain untuk memenuhi kebutuhan rakyatnya, termasuk Indonesia. Sayangnya, setiap negara memiliki mata uang yang berbeda-beda. Oleh karena itu, diperlukan nilai tukar mata uang untuk melakukan perdagangan dengan negara lain. Nilai tukar adalah harga mata uang suatu negara terhadap mata uang negara lain yang telah disepakati bersama. Setiap perubahan nilai tukar mata uang akan memberikan dampak terhadap perekonomian suatu negara. Oleh karena itu, penting untuk mengetahui nilai tukar yang akan datang. Untuk menyelesaikan permasalahan ini, dibutuhkan metode prediksi seperti Support Vector Regression (SVR). Akan tetapi, hasil prediksi SVR bergantung kepada pemilihan nilai hyperparameter. Oleh karena itu, SVR akan dioptimasi menggunakan Whale Optimization Algorithm (WOA). Data nilai tukar mata uang yang digunakan adalah Dolar Amerika Serikat terhadap Rupiah yang berasal dari situs Yahoo Finance menggunakan harga penutupan harian mulai dari tanggal 27 Mei 2020 sampai dengan 2 Juni 2023. Penelitian ini berfokus untuk memprediksi harga penutupan harian berikutnya dengan menggunakan beberapa harga penutupan harian sebelumnya sebagai masukan. Metode Support Vector Regression yang dioptimasi dengan Whale Optimization Algorithm memiliki kinerja yang baik dalam memprediksi nilai tukar Dolar Amerika Serikat terhadap Rupiah dengan hyperparameter SVR, yaitu ε (epsilon) sebesar 1.00000605110973×10^(-12), C (penalty cost) sebesar 2.22663363867838, dan γ (gamma) sebesar 0.205687967011551 serta dengan jumlah lag variabel sebesar 5 mampu menghasilkan kinerja terbaik dengan nilai MAPE prediksi sebesar 0.238335527152796%.
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Every country trades with other countries to meet the needs of its people, including Indonesia. Unfortunately, each country has a different currency. Therefore, a currency exchange rate is needed to trade with other countries. The exchange rate is the price of a country's currency against another country's currency that has been mutually agreed. Every change in currency exchange rate will impact a country's economy. Therefore, it is crucial to know the upcoming exchange rates. To solve this problem, a prediction method such as Support Vector Regression (SVR) is needed. However, the SVR prediction results depend on the choice of hyperparameter values. Therefore, SVR will be optimized using the Whale Optimization Algorithm (WOA). The currency exchange rate data used is the United States Dollar against the Rupiah, which comes from the Yahoo Finance site using daily closing prices from 27 May 2020 until 2 June 2023. This research focuses on predicting the next daily closing price using several previous daily closing prices as input. Support Vector Regression method optimized with the Whale Optimization Algorithm has good performance in predicting the exchange rate of the United States Dollar against the Rupiah with SVR hyperparameters, namely ε (epsilon) of 1.00000605110973×10^(-12), C (penalty cost) of 2.22663363867838, and γ (gamma) of 0.205687967011551 and with a variable lag number of 5, it can produce the best performance with a predicted MAPE value of 0.238335527152796%
Knitting Indonesian Unity in the Momentum of Mohammad Natsir's Integral Motion: Studi analyses
Full text linkUnity is an important aspect of a state institution. Unity can be a strength for a nation and state. One of the historical moments that seeks to unite Indonesia after the proclamation is Natsir's Integral Motion. The concept promoted by Indonesian Muslim political figure Mohammad Natsir succeeded in uniting Indonesia into the Republic of Indonesia from the previous RIS (United Republic of Indonesia). This article will discuss the role of Mohammad Natsir with his integral motion in building unity and integrity in Indonesia. This research is library research and the data source used consists of primary data sources of Mohammad Natsir's works related to the Integral Movement and Indonesian Unity, and is assisted by secondary data sources, namely books, which are relevant to this research to strengthen arguments and to complete the data from the research results. The data analysis technique used by the author in this research is content analysis. The results that the author got from this study are that Natsir's Integral Motion has a big and important influence on Indonesia. Not only does it unite Indonesia, but it also has a significant influence in the fields of politics, economics, education and international relations. Based on these findings, the author hopes that there will be further studies on Natsir's integral motion, so that it can become material for reflection and discussion together
Prediksi Nilai Tukar Dolar Amerika Serikat Terhadap Rupiah Menggunakan Metode Support Vector Regression Dengan Whale Optimization Algorithm
Full text linkSetiap negara melakukan perdagangan dengan nega-ra lain untuk memenuhi kebutuhan rakyatnya, termasuk In-donesia. Sayangnya, setiap negara memiliki mata uang yang berbeda-beda. Oleh karena itu, diperlukan nilai tukar mata uang untuk melakukan perdagangan dengan negara lain. Nilai tukar adalah harga mata uang suatu negara terhadap mata uang negara lain yang telah disepakati bersama. Setiap peru-bahan nilai tukar mata uang akan memberikan dampak ter-hadap perekonomian suatu negara. Oleh karena itu, penting untuk mengetahui nilai tukar yang akan datang. Untuk me-nyelesaikan permasalahan ini, dibutuhkan metode prediksi seperti Support Vector Regression (SVR). Akan tetapi, hasil prediksi SVR bergantung kepada pemilihan nilai hyper-parameter. Oleh karena itu, SVR akan dioptimasi menggunakan Whale Optimization Algorithm (WOA). Data nilai tukar mata uang yang digunakan adalah Dolar Amerika Serikat terhadap Rupiah yang berasal dari situs Yahoo Finance menggunakan harga penutupan harian mulai dari tanggal 27 Mei 2020 sampai dengan 2 Juni 2023. Penelitian ini berfokus untuk memprediksi harga penutupan harian berikutnya dengan menggunakan beberapa harga penutupan harian sebelumnya sebagai masuk-an. Metode Support Vector Regression yang dioptimasi dengan Whale Optimization Algorithm memiliki kinerja yang baik dalam memprediksi nilai tukar Dolar Amerika Serikat terhadap Rupiah dengan hyperparameter SVR, yaitu ε (epsilon) sebesar 1,00000605110973×10-12, C (penalty cost) sebesar 2,22663363867838, dan γ (gamma) sebesar 0,205687967011551 serta dengan jumlah lag variabel sebesar 5 mampu menghasilkan kinerja terbaik dengan nilai MAPE prediksi sebesar 0,238335527152796%
UPAYA PENGEMBANGAN PRODUK UNTUK EFEKTIVITAS UMKM WAJIK IBU OOT DI DESA CIMANDE
Full text linkEfforts to increase sales productivity and effectiveness for UMKM in Cimande Village through attractive product packaging and marketing innovations. The development of this innovation aims to make business actors, especially in Cimande Village, get maximum results from selling or the production process of their business. Especially in the current modernization era, it will greatly help them in making sales online, be it on the Marketplace or Social Media. Besides that with the existence of attractive product packaging than before, it will make it easier for consumers to get to know the type of UMKM product
Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
Full text linkBackground: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. Methods: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. Findings: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. Interpretation: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Funding: Gates Foundation
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Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021
No full textBackground Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality. In 2020-21, non-pharmaceutical interventions associated with the COVID-19 pandemic reduced not only the transmission of SARS-CoV-2, but also the transmission of other LRI pathogens. Tracking LRI incidence and mortality, as well as the pathogens responsible, can guide health-system responses and funding priorities to reduce future burden. We present estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 of the burden of non-COVID-19 LRIs and corresponding aetiologies from 1990 to 2021, inclusive of pandemic effects on the incidence and mortality of select respiratory viruses, globally, regionally, and for 204 countries and territories.
Methods We estimated mortality, incidence, and aetiology attribution for LRI, defined by the GBD as pneumonia or bronchiolitis, not inclusive of COVID-19. We analysed 26 259 site-years of mortality data using the Cause of Death Ensemble model to estimate LRI mortality rates. We analysed all available age-specific and sex-specific data sources, including published literature identified by a systematic review, as well as household surveys, hospital admissions, health insurance claims, and LRI mortality estimates, to generate internally consistent estimates of incidence and prevalence using DisMod-MR 2.1. For aetiology estimation, we analysed multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature data using a network analysis model to produce the proportion of LRI deaths and episodes attributable to the following pathogens: Acinetobacter baumannii, Chlamydia spp, Enterobacter spp, Escherichia coli, fungi, group B streptococcus, Haemophilus influenzae, influenza viruses, Klebsiella pneumoniae, Legionella spp, Mycoplasma spp, polymicrobial infections, Pseudomonas aeruginosa, respiratory syncytial virus (RSV), Staphylococcus aureus, Streptococcus pneumoniae, and other viruses (ie, the aggregate of all viruses studied except influenza and RSV), as well as a residual category of other bacterial pathogens.
Findings Globally, in 2021, we estimated 344 million (95% uncertainty interval [UI] 325-364) incident episodes of LRI, or 4350 episodes (4120-4610) per 100 000 population, and 2.18 million deaths (1.98-2.36), or 27.7 deaths (25.1-29.9) per 100 000. 502 000 deaths (406 000-611 000) were in children younger than 5 years, among which 254 000 deaths (197 000-320 000) occurred in countries with a low Socio-demographic Index. Of the 18 modelled pathogen categories in 2021, S pneumoniae was responsible for the highest proportions of LRI episodes and deaths, with an estimated 97.9 million (92.1-104.0) episodes and 505 000 deaths (454 000-555 000) globally. The pathogens responsible for the second and third highest episode counts globally were other viral aetiologies (46.4 million [43.6-49.3] episodes) and Mycoplasma spp (25.3 million [23.5-27.2]), while those responsible for the second and third highest death counts were S aureus (424 000 [380 000-459 000]) and K pneumoniae (176 000 [158 000-194 000]). From 1990 to 2019, the global all-age non-COVID-19 LRI mortality rate declined by 41.7% (35.9-46.9), from 56.5 deaths (51.3-61.9) to 32.9 deaths (29.9-35.4) per 100 000. From 2019 to 2021, during the COVID-19 pandemic and implementation of associated nonpharmaceutical interventions, we estimated a 16.0% (13.1-18.6) decline in the global all-age non-COVID-19 LRI mortality rate, largely accounted for by a 71.8% (63.8-78.9) decline in the number of influenza deaths and a 66.7% (56.6-75.3) decline in the number of RSV deaths.
Interpretation Substantial progress has been made in reducing LRI mortality, but the burden remains high, especially in low-income and middle-income countries. During the COVID-19 pandemic, with its associated non-pharmaceutical interventions, global incident LRI cases and mortality attributable to influenza and RSV declined substantially. Expanding access to health-care services and vaccines, including S pneumoniae, H influenzae type B, and novel RSV vaccines, along with new low-cost interventions against S aureus, could mitigate the LRI burden and prevent transmission of LRI-causing pathogens. Copyright (c) 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017
No full textAbstract: Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk outcome pairs, and new data on risk exposure levels and risk outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017,34.1 million (95% uncertainty interval [UI] 33.3-35.0) deaths and 121 billion (144-1.28) DALYs were attributable to GBD risk factors. Globally, 61.0% (59.6-62.4) of deaths and 48.3% (46.3-50.2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10.4 million (9.39-11.5) deaths and 218 million (198-237) DALYs, followed by smoking (7.10 million [6.83-7.37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6.53 million [5.23-8.23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4.72 million [2.99-6.70] deaths and 148 million [98.6-202] DALYs), and short gestation for birthweight (1.43 million [1.36-1.51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4.9% (3.3-6.5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23.5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18.6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd
A remote-controlled global navigation satellite system based rover for accurate video-assisted cadastral surveys
Get PDFOne of the main tasks of a cadastral surveyor is to accurately determine property boundaries by measuring control points and calculating their coordinates. This paper proposes the development of a remotely-controlled tracking system to perform cadastral measurements. A Bluetooth-controlled rover was developed, including a Raspberry Pi Zero W module that acquires position data from a VBOX 3iSR global navigation satellite system (GNSS) receiver, equipped with a specific modem to download real-time kinematic (RTK) corrections from the internet. Besides, the Raspberry board measures the rover speed with a hall sensor mounted on a track, adjusting the acquisition rate to collect data at a fixed distance. Position and inertial data are shared with a cloud platform, enabling their remote monitoring and storing. Besides, the power supply section was designed to power the different components included in the acquisition section, ensuring 2 hours of energy autonomy. Finally, a mobile application was developed to drive the rover and real-time monitor the travelled path. The tests indicated a good agreement between rover measurements and those obtained by a Trimble R10 GNSS receiver (+0.25% mean error) and proved the superiority of the presented system over a traditional metric wheel
