1,724,643 research outputs found
La bella Easo
No full text"La bella Easo" fue escrita por Arturo Campión. Crítica a la novela escrita en castellano. Echegaray destaca el dominio del español por parte de Campión y sus descripciones, sin embargo considera que se utilizan excesivamente ciertos procedimientos naturalistas que están pasados de moda"La bella Easo" was written by Arturo Campión. Criticism on the novel written in Castilian Spanish. Echegaray emphasises its descriptions and Campión's proficiency in Spanish, however he considers that he uses certain naturalist procedures that clearly unfashionabl
Fuzzy Time Series Saxena-Easo Pada Peramalan Laju Inflasi Indonesia (Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting)
No full textSaxena-Easo Fuzzy Time Series
(FTS
) is a softcomputing method for forecasting using fuzzy
concept. It doesn’t need any assumption like conventional forecasting method. Generally it’s
focused on three important steps like percentage change as the universe of discourse, interval
partition, and defuzzification. In this research, this method is applied to Indonesia’s inflation rate
data. The aim of this research is to forecast Indonesia’s inflation rate in 2017 by using input from
Autoregressive Integrated Moving Average
(ARIMA
) process, Saxena-Easo FTS, and actual data
from 1970-2016. ARIMA is focused on four steps like identifying, parameter estimation,
diagnostic checking, and forecasting. The result for Indonesia’s inflation rate forecasting in 2017
is about 5.9182 using Saxena-Easo FTS. Root Mean Square Error
(RMSE
) is also computed to
compare the accuracy rate from each method between Saxena-Easo FTS and ARIMA. RMSE from
Saxena-Easo FTS is about 0.9743 while ARIMA is about 6.3046
Criteria for EASO-collaborating centres for obesity management
No full textObesity is recognised as a global epidemic and the most prevalent metabolic disease world-wide. Specialised obesity services, however, are not widely available in Europe, and obesity care can vary enormously across European regions. The European Association for the Study of Obesity (EASO, www.easo.org) has developed these criteria to form a pan-European network of accredited EASO-Collaborating Centres for Obesity Management (EASO-COMs) in accordance with accepted European and academic guidelines. This network will include university, public and private clinics and will ensure that the obese and overweight patient is managed by a holistic team of specialists and receives comprehensive state-ofthe-art clinical care. Furthermore, the participating centres, under the umbrella of EASO, will work closely for quality control, data collection, and analysis as well as for education and research for the advancement of obesity care and obesity scienc
Upaya European Asylum Support Office (EASO) dalam Mengurangi Arus Imigran dari Jalur Laut Mediterania Tahun 2014-2016
Get PDFABSTRACT AGUNG SETIO NUGROHO, student number D0413005, research title EUROPEAN ASYLUM SUPPORT OFFICE (EASO) EFFORTS IN REDUCING IMMIGRANTS INFLUX THROUGH THE MEDITERRANEAN SEA LANE 2014-2016. International Relations Department, Faculty of Social and Political Sciences, Universitas Sebelas Maret, Surakarta. September 2017 The EU faces the problem of an unprecedented flood of immigrants. The Mediterranean Sea becomes a potential gateway, both for refugees and for irresponsible agents. Open borders offer not only greater opportunities in various fields but also cause new problems. The absence of consensus on immigrant handling shows that the EU has no spurs in addressing the internal state issues, especially in handling Immigrants entering Europe through Mediterranean Sea. This research aims to give a brief and general picture on how a regional organization, in this case European Union managed to work to secure its territory from threat and crisis using its special office working in the field of migrant management which is European Asylum Support Office (EASO). In order to answer the question about how European Union with its EASO managed to overcome Migrant Crisis and the problem caused by migrants in the year of 2014-2016, the author uses the concept of international migration and regional security. Then in analyzing these efforts the author uses the theory of Institutionalism. This qualitative research using literature review as data collection method. By the end of this research, the author came to a conclusion that the efforts done by the EASO has been able to reduce the influx of immigrants entering Europe through Mediterranean Sea Lane by 2014-2016 eventhough still need to be maximized. Keywords: Asylum, EASO, European Union, Migrant Crisis, Refuge
EASO collaborating centers for obesity management (COMs)
No full textObesity is a global epidemic and the most prevalent metabolic disease in the world. Preventive and treatment measures taken are still not adequate and these services are still not equally widespread. In response to this situation, EASO has developed a network of 'accredited specialized obesity centers', where the quality and efficacy of the care offered to patients are of the highest standards. Under the EASO Collaborating Centers for Obesity Management (COM) scheme, these centers will be accredited in accordance with accepted European and academic guidelines
EASO: informe general anual de 2017
Get PDFEl ISBN e ISSN corresponden a la versión electrónica del documentoEl informe general anual de la EASO describe los logros de la Oficina en 2017
EASO: informe general anual de 2018
Get PDFEl ISSN e ISBN corresponden a la versión electrónica del documentoEl informe general anual de la EASO describe los logros de la Oficina en 2018 y se elabora según lo establecido en el artículo 29, apartado 1, letra c), del Reglamento de la EASO. Este informe, previa aprobación del Consejo de Administración, se envía al Parlamento Europeo, el Consejo, la Comisión, el Servicio de Auditoría Interna y el Tribunal de Cuentas. El informe general anual es un documento público que se traduce a todas las lenguas oficiales de la UE
Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting
Get PDFSaxena-Easo Fuzzy Time Series (FTS) is a softcomputing method for forecasting using fuzzy concept. It doesn’t need any assumption like conventional forecasting method. Generally it’s focused on three important steps like percentage change as the universe of discourse, interval partition, and defuzzification. In this research, this method is applied to Indonesia’s inflation rate data. The aim of this research is to forecast Indonesia’s inflation rate in 2017 by using input from Autoregressive Integrated Moving Average (ARIMA) process, Saxena-Easo FTS, and actual data from 1970-2016. ARIMA is focused on four steps like identifying, parameter estimation, diagnostic checking, and forecasting. The result for Indonesia’s inflation rate forecasting in 2017 is about 5.9182 using Saxena-Easo FTS. Root Mean Square Error (RMSE) is also computed to compare the accuracy rate from each method between Saxena-Easo FTS and ARIMA. RMSE from Saxena-Easo FTS is about 0.9743 while ARIMA is about 6.3046. Keywords: saxena-easo fuzzy time series, ARIMA, inflation rate, RMSE
PERBANDINGAN METODE FTS LEE DAN FTS SAXENA EASO PADA PREDIKSI HARGA NIKEL
Get PDFIndustri nikel di Indonesia telah menjadi perhatian yang meningkat seiring dengan potensi ekonomi yang dimilikinya. Namun, masalah seputar industri nikel di Indonesia salah satunya yaitu terkait keberlanjutan ekonomi. Tujuan penelitian ini untuk mengetahui mana metode yang lebih baik antara FTS Lee dan FTS Saxena Easo pada prediksi harga nikel di Indonesia. Data penelitian didapat dari kementerian energi dan sumber daya mineral rentang waktu Januari 2018 – Januari 2024. Hasi penelitian menunjukkan bahwa FTS Saxena Easo melakukan prediksi lebih baik daripada FTS Saxena Easo dibuktikan dengan nilai MAPE masing-masing yaitu 0.84% dan 7.99%
An Algorithm of Saxena-Easo on Fuzzy Time Series Forecasting
No full textThe 1st International Conference of Combinatorics, Graph Theory, and Network Topology 25–26 November 2017, The University of Jember, East Java, IndonesiaThis paper presents a forecast model of Saxena-Easo fuzzy time series
prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use
MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy
time series doesn’t need stationarity like conventional forecasting method, capable of
dealing with the value of time series which are linguistic and has the advantage of
reducing the calculation, time and simplifying the calculation process. Generally it’s
focus on percentage change as the universe discourse, interval partition and
defuzzification. The result indicate that between the actual data and the forecast data
are close enough with Root Mean Square Error (RMSE)= 1.5289
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