The Influence of Interest Rate, Money Circulation, Inflation, and Cpi Against Export and Import in Indonesia 2012-2018

Sumantri, Fazhar; Latifah, Umi

doi:10.22219/jep.v17i2.10242

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…All activities which issue or sell goods or services from within to abroad are called exports. Exports in economic growth aim to meet the needs of foreign markets and increase a country's consumption to increase foreign exchange and per capita income [15,16]. Indonesian exports constantly fluctuate both in volume and value [3].…”

Section: Indonesian Exportsmentioning

confidence: 99%

New recommendation to predict export value using big data and machine learning technique

Nooraeni¹,

Nickelson²,

Rahmadian

et al. 2022

SJI

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Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

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Section: Indonesian Exportsmentioning

confidence: 99%

New recommendation to predict export value using big data and machine learning technique

Nooraeni¹,

Nickelson²,

Rahmadian

et al. 2022

SJI

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“…The study obtained that fuel prices, especially gasoline, significantly impacted inflation. Sumantria and Latifah (2019) stated in their research that inflation had a prominent effect on exports and imports in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Fuel Prices Increasing on Inflation in South Sulawesi using Pulse Function Intervention Analysis

Wasani

Projo²

2022

JEP

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South Sulawesi Province is considered capable of controlling inflation, as seen from its not very large volatility. However, this does not mean that the increase in fuel prices does not impact inflation in South Sulawesi. This study aims to obtain the best model to analyze the impact of rising fuel prices on inflation in South Sulawesi using Pulse Function Intervention Analysis and to find out how significant the impact of rising fuel prices on inflation in South Sulawesi is. The data in this study were obtained from the Central Statistics Agency (BPS) of South Sulawesi Province, namely the monthly inflation of South Sulawesi Province for the period January 2014 – September 2022, with a total of 105 observations. The best model in this study is the MA ([12]) bbm1, bbm5, and bbm6 models with AIC 104.09. Statistical test results show that 3 of 6 times increases in fuel prices since 2014 still influence the rising inflation, namely in November 2014, April 2022, and September 2022. The increase in fuel prices in 2014 impacted rising inflation by 1.97 percent, while in 2022, it increased inflation by 0.92 and 0.99 percent.

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Partial modeling of macroeconomic variables in industrial fields

Valentika

Abdullah

Ilmadi

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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This study modifies Catalbas’s (2016) and Pratikto’s (2012) research. The analysis used in this study is cross-correlation and analysis of the Vector Autoregression (VAR) method, Vector Autoregression in difference form (VARD) or Vector Error Correction Model (VECM) to test the relationship between variables with the ordinary least square (OLS) method and the causality test to test the relationship (causal relationship) between variables with the Granger Causality (GC) Test. Based on the correlation analysis, it is obtained that exports, imports, and interest rates have a negative and statistically significant relationship at the real level of 5% of the rupiah exchange rate. The model between exports and the rupiah exchange rate is the VAR (2) model. The model between imports and the rupiah exchange rate is a VECM with lag 3, there is 1 cointegration and the deterministic trend used is none intercept no trend. The model between the interest rate and the rupiah exchange rate is a VARD vector model with a lag 1. The model between inflation and the rupiah exchange rate is a VECM with lag 2 and the deterministic trend used is none intercept no trend and there is 1 significant cointegration at the 5% level.

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The Influence of Interest Rate, Money Circulation, Inflation, and Cpi Against Export and Import in Indonesia 2012-2018

Cited by 3 publications

References 1 publication

New recommendation to predict export value using big data and machine learning technique

New recommendation to predict export value using big data and machine learning technique

The Impact of Fuel Prices Increasing on Inflation in South Sulawesi using Pulse Function Intervention Analysis

Partial modeling of macroeconomic variables in industrial fields

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