scholarly journals Analisis Anggaran Pemerintah (APBN dan APBN-P) dalam Perspektif Demokrasi Multipartai dan Koalisi

2016 ◽  
Vol 17 (1) ◽  
pp. 28-43
Author(s):  
Badikenita Sitepu
Keyword(s):  
Time Series ◽  
Political System ◽  
Time Series Data ◽  
The State ◽  
Series Data ◽  
State Budget ◽  
Sector Reform ◽  
Approval Rate ◽  
Budget Analysis ◽  
Financial Sector Reform

Government Budget Analysis in Perspective Democracy Multiparty and CoalitionDemocratic system change in Indonesia resulted in a change of political system and economy of Indonesia. Changes in the political system was also followed by the country’s financial sector reform (or budget) in the process of change towards prosperity residents better. Using time series data from 1982 to 2011, this study found that the coalition is formed to have a positive and significant effect on the level of budget revenues in the state budget approval and the state budget, as well as the approval of the budget at the level of state budget. The Coalition does not have influence on the level of approval signifkan budget on state budget. The number of political parties only aect the level of budget revenues in the state budget approval. The level of tax agreements have a significant impact on the level of state budget approval and the state budget. Revenue budget approval rate has a significant influence on the level of approval of both the state budget expenditures and state budget.Keywords: State Budget; Multiparty System; Coalition; Political Party; Level Tax Agreement AbstrakPerubahan sistem demokrasi di Indonesia berdampak terhadap perubahan sistem politik dan ekonomi Indonesia. Perubahan sistem politik juga diikuti dengan reformasi di bidang keuangan negara (atau anggaran) dalam proses perubahan menuju kemakmuran penduduk yang lebih baik. Dengan menggunakan data time series tahun 1982–2011, penelitian ini menemukan bahwa koalisi yang terbentuk berpengaruh positif dan signifikan terhadap tingkat persetujuan anggaran pendapatan pada APBN dan APBN-P, serta tingkat persetujuan anggaran belanja pada APBN-P. Koalisi tidak berpengaruh signifikan terhadap tingkat persetujuan anggaran belanja pada APBN-P. Jumlah partai politik hanya berpengaruh pada tingkat persetujuan anggaran pendapatan pada APBN. Tingkat persetujuan pajak berpengaruh signifikan terhadap tingkat persetujuan anggaran pada APBN dan APBN-P. Tingkat persetujuan anggaran pendapatan berpengaruh signifikan terhadap tingkat persetujuan anggaran belanja, baik pada APBN maupun APBN-P.

Structural condition assessment using entropy-based time series analysis

2017 ◽  
Vol 28 (14) ◽  
pp. 1941-1956 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Bijan Samali ◽  
Jianchun Li ◽  
Ye Lu ◽  
Samir Mustapha
Keyword(s):  
Time Series ◽  
State Space ◽  
Structural Damage ◽  
Time Series Data ◽  
National Laboratory ◽  
The State ◽  
Frame Structure ◽  
Series Data ◽  
Symbol Space ◽  
Symbol Sequences

We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.

scholarly journals Processing of Rainfall Time Series Data in the State of Rio de Janeiro

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Givanildo De Gois ◽  
José Francisco De Oliveira-Júnior
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The State ◽  
Data Consistency ◽  
Quality Data ◽  
Series Data ◽  
Time Variability ◽  
Rainfall Time Series ◽  
Raw Data ◽  
State Data

The goal was to perform the filling, consistency and processing of the rainfall time series data from 1943 to 2013 in five regions of the state. Data were obtained from several sources (ANA, CPRM, INMET, SERLA and LIGHT), totaling 23 stations. The time series (raw data) showed failures that were filled with data from TRMM satellite via 3B43 product, and with the climatological normal from INMET. The 3B43 product was used from 1998 to 2013 and the climatological normal over the 1947- 1997 period. Data were submitted to descriptive and exploratory analysis, parametric tests (Shapiro-Wilks and Bartlett), cluster analysis (CA), and data processing (Box Cox) in the 23 stations. Descriptive analysis of the raw data consistency showed a probability of occurrence above 75% (high time variability). Through the CA, two homogeneous rainfall groups (G1 and G2) were defined. The group G1 and G2 represent 77.01% and 22.99% of the rainfall occurring in SRJ, respectively. Box Cox Processing was effective in stabilizing the normality of the residuals and homogeneity of variance of the monthly rainfall time series of the five regions of the state. Data from 3B43 product and the climatological normal can be used as an alternative source of quality data for gap filling.

scholarly journals Variability and Sources of Output Growth in Major Oilseeds of Rajasthan

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Kailash Chand Bairwa
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Output Growth ◽  
The State ◽  
Series Data ◽  
Oilseed Crops ◽  
Significant Growth ◽  
Area Effect ◽  
Relative Contribution

Rajasthan state is the second largest oilseeds producer and land coverage in the country. The share of oilseed crops is scheduled the significant growth in area and output in latest 20 years. Nevertheless, compare to wheat and gram, the growth rate of area and production of several oilseeds is less significant and there exist wide instability in their productivity in scattered part of the state. This study investigates to growth, its contributors and variability in area, production and productivity of major oilseed crops. The study period from 1990-91 to 2019-20 was divided into three sub-periods viz., period-I (1990-91 to 2004-05); period-II (2005-06 to 2019-20) and Overall study Period (1990-91 to 2018-19). Time series data were collected from various public E-sources to compute the growth, instability and decomposition in oilseeds production. It was revealed from the analysis that growth of kharif oilseeds was higher than rabi oilseeds. The highest instability (31.78) in production and productivity was reported in period-I for kharif oilseeds. In case of relative contribution, the area effect (416.85) and yield effects (211.10) were more effective in production of taramira and sesame crops, respectively. This analysis suggested that during period –I and II area effect was dominant in changing output of taramira and rapeseed-mustard.

scholarly journals Automated evaluation of agricultural damage using UAV survey

2018 ◽  
Vol 10 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Daniel Stojcsics ◽  
Zsolt Domozi ◽  
András Molnár
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
The State ◽  
Series Data ◽  
Automated Evaluation ◽  
Gis Data ◽  
Automated Process ◽  
Post Production ◽  
Comparison Of The Results

Abstract In the last decade, the rate of the industrial usage of fixed-wing and blended wing aircraft has increased. A 1–2-km2 area can be surveyed by such a drone within 30 to 60 minutes, without any special infrastructure, and this can be repeated at any time. This provides an opportunity to conduct automatized surveys and time series data testing, which can be used as a basis to decide specific processes. The state and the development of the plants can be monitored as well as the spread of pests and the efficiency of the procedures that protect against them. During the surveys, thousands of images are taken of the area, which can be converted to a georeferenced large-sized map within 20 to 40 hours, including post-production and a resolution varying from 0.01 to 0.1 cm/pixel. The paper provides a solution to the industrial post-production of these high-quantity data, in which a deep learning-based automated process using Matlab is presented, including a comparison of the results to the GIS data.

scholarly journals An Application of the Decision Support Model to Louisiana’s Exports

2018 ◽  
Vol 5 (1) ◽  
pp. 4307-4313
Author(s):  
Bukola B. Oluwade
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Time Series Data ◽  
The United States ◽  
Census Bureau ◽  
The State ◽  
Series Data ◽  
Decision Support Model ◽  
United States Census Bureau ◽  
Support Model

Recognizing the relevance of exportation to the development and growth of many nations, government and business entities- mainly in the Louisiana State, the policymakers and other key stakeholders should be devoting more time to expand its export opportunities for more revenue generation. This current study revolved around the Export Decision Support Model (EDSM) propounded by Viviers and Cuyvers (2012).  The time series data was sourced from the United States Census Bureau (2016) survey on State exports from Louisiana. The objective of this study was to demonstrate the modification of the EDSM for the development of the Louisiana State Exportation. The EDSM is designed and modified purposely for the State of Louisiana to enable it identifies the various export opportunities. The study used a time-series data across a variety of export commodities and import countries available in the State of Louisiana from 2013 to 2016. Based on the data from the U.S. Census Bureau and the International trade data between 2013 and 2016, it was underscored that the State’s gross export accounted for 80.4% , while the net export estimated around 8.8% of the Louisiana’s GDP in 2016 with a strong focus across Asia, and Europe. The study recommend that policymakers should pay more attention to the prioritize export commodities outline in the study. 

scholarly journals Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

2021 ◽  
Vol 10 (2) ◽  
pp. 870-878
Author(s):  
Zainuddin Z. ◽  
P. Akhir E. A. ◽  
Hasan M. H.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
The State ◽  
Series Data ◽  
Machine Failure ◽  
Gated Recurrent Unit

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

scholarly journals RAEF: An Imputation Framework Based on a Gated Regulator Autoencoder for Incomplete IIoT Time-Series Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jianyong Zhao ◽  
Jiachen Qiu ◽  
Danfeng Sun ◽  
Baiping Chen
Keyword(s):  
Time Series ◽  
Incomplete Data ◽  
Time Series Data ◽  
State Of The Art ◽  
Negative Impact ◽  
The State ◽  
Series Data ◽  
The World

The number of intelligent applications available for IIoT environments is growing, but when the time-series data these applications rely on are incomplete, their performance suffers. Unfortunately, incomplete data are all too frequent to a phenomenon in the world of IIoT. A common workaround is to use imputation. However, the current methods are largely designed to reconstruct a single missing pattern, where a robust and flexible imputation framework would be able to handle many different missing patterns. Hence, the framework presented in this study, RAEF, is capable of processing multiple missing patterns. Based on a recurrent autoencoder, RAEF houses a novel neuron structure, called a gated regulator, which reduces the negative impact of different missing patterns. In a comparison of the state-of-the-art time-series imputation frameworks at a range of different missing rates, RAEF yielded fewer errors than all its counterparts.

scholarly journals Analysis of The Effect of Monetary Policy on Government Sharia Securities (SBSN) in Indonesia

2018 ◽  
Vol 2 (2) ◽  
pp. 109
Author(s):  
Juaris Juaris ◽  
Raja Masbar ◽  
Chenny Seftarita
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Interest Rate ◽  
Time Series Data ◽  
The State ◽  
Series Data ◽  
Positive Contribution ◽  
State Budget ◽  
Effect Of Monetary Policy

Outstanding Government Sharia Securities (SBSN) in Indonesia from the first published on 2008 continued to experience significant growth. Monetary indicators often associated with capital markets are inflation, exchange rate and interest rate (BI Rate) show a fluctuating pattern, these factors can inhibitSBSN growth.This study aims to analyze the effect of monetary policy(inflation, exchange rate and BI Rate) on Government Sharia Securities (SBSN) and the contribution of Government Sharia Securities (SBSN) tothe state budget(APBN). Using monthly time series data from January 2010 until July 2016 and Autoregressive Distributed Lag (ARDL), the estimation results conclude that there is a co-integration in the models studied. While the estimation result of ARDL shows in the long term, exchange rate significantly has an effect on SBSN. While inflation and BI Rate have no significant effect on SBSN either in the short or long term. This study also shows the positive contribution of SBSN as deficit financing and development project. Therefore, the government must optimize the state sukuk by increasing the issuance of state sukuk in the structure of the state budget and supported by the control of inflation and exchange rate. For investors can take advantage of the state sukuk to invest, this is consistent with the insignificant effect of interest rate so that the investment is safe with sharia principles.

China's Urbanization Levels: Reconstructing a Baseline from the Fifth Population Census

2003 ◽  
Vol 173 ◽  
pp. 176-196 ◽  
Author(s):  
Zhou Yixing ◽  
Laurence J. C. Ma
Keyword(s):  
Time Series ◽  
Urban Population ◽  
Total Population ◽  
Time Series Data ◽  
The State ◽  
Series Data ◽  
Urban Residents ◽  
Population Census ◽  
State Statistical Bureau ◽  
Statistical Criteria

China's fifth population census taken on 1 November 2000 reveals that the mainland had a total population of 1,265.83 million, of which 455.94 million were urban residents (chengzhen renkou). This suggests that the level of urbanization was 36.09 per cent. Whereas this is a reasonable figure that appears to fit well the general rising trend of urbanization shown in the previous four censuses, the levels of urbanization reported in the five censuses are not really comparable because the criteria used to enumerate “urban” population have been different for different censuses. Before the State Statistical Bureau produces a set of comparable figures on the levels of China's urbanization based on a set of uniform criteria, the problem of data incomparability concerning the levels of urbanization will continue to baffle users. This report analyses the statistical criteria defining China's urban population used in the 2000 census, compares them with the criteria of the previous censuses and presents two sets of adjusted and internally coherent time-series data to remedy the problem of data incomparability.

scholarly journals PENGARUH TENAGA KERJA INDONESIA DI LUAR NEGERI DAN REMITANSI TERHADAP PDB PER KAPITA DI INDONESIA

2019 ◽  
Vol 4 (2) ◽  
pp. 231-248
Author(s):  
Ade Eka Afriska ◽  
T. Zulham ◽  
Taufiq C. Dawood
Keyword(s):  
Time Series ◽  
International Finance ◽  
Direct Investment ◽  
Oil And Gas ◽  
Time Series Data ◽  
Gdp Per Capita ◽  
Series Data ◽  
State Budget ◽  
Money Transfer ◽  
Per Capita

Money transfer or remittances is one of the main sources of international finance that sometimes exceed the flow of foreign direct investment. This research aims to observe the influence of TKI and the remittance to GDP per Capita in Indonesia by using time series data from the years 1990-2016. Method of the research used Autoregressive Distributed Lagged (ARDL). In Indonesia, the money transfer (remittance) is second after oil and gas (state budget sources or APBN). The result showed that the TKI and positive and significant influential remmitance to GDP per capita Indonesia. Although GDP per capita increased Indonesia result of remittance, but government should increase employment in Indonesia so that Indonesia does not labor must fight and workabroad.Keywords: Remittance, TKI, GDP Per capita, the ARDL.AbstrakPengiriman uang (remitansi) merupakan salah satu sumber keuangan internasional utama yang terkadang melebihi arus investasi langsung asing. Penelitian ini bertujuan untuk mengamati pengaruh TKI dan remitansi terhadap PDB Per Kapita di Indonesia dengan menggunakan data time series dari tahun 1990-2016. Metode analisis yang digunakan yaitu Autoregressive DistributedLagged (ARDL). Di Indonesia, pengiriman uang (remitansi) merupakan sumber APBN kedua setelah Migas. Hasil penelitian menunjukkan bahwa TKI dan remitansi berpengaruh positif dan signifikan terhadap PDB per kapita Indonesia. Meskipun PDB per Kapita Indonesia meningkat akibat dari remitansi, akan tetapi pemerintah harus meningkatkan lapangan pekerjaan di Indonesia agar tenaga kerja Indonesia tidak harus berjuang dan bekerja di luar negeri

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