Inflation Rate In Indonesia Research Articles

Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia

Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia

The Influence of Interest Rates and Exchange Rates on Inflation in Indonesia

One of the problems in the economy in every country is the problem of inflation. Inflation is the process of increasing general goods which are basic goods needed by the community continuously. This study aims to see the factors that influence the inflation rate in Indonesia. The data used are secondary data obtained through the Provincial Central Statistics Agency. The data taken are inflation (Y), interest rates (X1), and exchange rates (X2) during the period 2015-2024. The research method used in this study is the time series method. The results of the analysis show that interest rates (X1) and exchange rates (X2) have a negative and significant effect on the inflation rate in Indonesia.

Spatial impact on inflation of Java Island prediction using Autoregressive Integrated Moving Average (ARIMA) and Generalized Space-Time ARIMA (GSTARIMA)

Inflation is one of macroeconomic issues in Indonesia that needs to be controlled. Inflation could happen because of widespread increases in the cost of goods and services. Annual inflation rate in Indonesia on 2008 to 2023 are quite fluctuating and several periods are not achieved inflation target yet. One of the ways to control inflation is by making predictions for the upcoming period. Java Island is the biggest contributor on economy and Gross Domestic Product (GDP) in Indonesia so it can be considered as general indicator to measure overall inflation rate of Indonesia. Thus, data used in this study is monthly inflation at each province in Java Island from January 2008 to December 2023. This study using two methods, Autoregressive Integrated Moving Average (ARIMA) for univariate time series prediction and Generalized Space-Time ARIMA (GSTARIMA) for multivariate time series prediction with a spatial factor. The results of both models will be compared to determine which model has better accuracy. Based on RMSE value, GSTARIMA model has least average RMSE value, which is 0.113 compared with ARIMA model which has average RMSE value 0.319 thus it can conclude that spatial factors addition could increase accuracy on inflation prediction in Java Island.•This paper purposes to get Java Island's inflation rate prediction to determine better policy on controlling cost of goods and services.•Best model using GSTARIMA methods is GSTARMA(1,1) with distance invese matrix that indicate that coordinate point of each location increase performance of inflation rate prediction.•The result indicate GSTARIMA has better accuracy than ARIMA for inflation prediction in Java Island based on RMSE value.

Effectiveness of Monetary Policy Transmission Path on Usecashless Paymentt and Amount of Money Supply on the Increasing Inflation Rate in Indonesia

Effectiveness of Monetary Policy Transmission Path on Usecashless Paymentt and Amount of Money Supply on the Increasing Inflation Rate in Indonesia

Dampak Guncangan Harga Minyak Dunia Terhadap Dinamika Inflasi di Indonesia

This study aims to identify and analyze the impact of world oil price shocks on inflation dynamics in Indonesia. This research is a quantitative research using secondary data with quarterly data from 1993-2022. The analysis used is descriptive analysis and inductive analysis. In inductive analysis there are several tests, namely: Vector Auto Regression (VAR) Analysis, Empirical Models Vector Auto Regression (VAR) Analysis, Vector Auto Regression (VAR) Analysis Steps such as: Stationarity Test, Cointegration Test, Optimum Lag, Model Stability Test, Impulse Response Function (IRF), Variance Decomposition. The results of this study indicate that (1) world oil prices have a significant influence on the dynamics of inflation in Indonesia. This means that world oil prices can affect the dynamics of inflation in Indonesia, especially in the short term. (2) Shocks to world oil prices also have an impact on aggregate demand and supply of world oil which then affect the dynamics of the national macro economy which in turn will cause changes in the inflation rate in Indonesia

ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia

Extreme events in a time series model can be detected when the precise timing of the event, known as the intervention, is known. When the exact timing of an event is unknown, it is referred to as an outlier. If these factors are neglected, the model's accuracy will be affected. To overcome this situation, it is possible to add the intervention or outlier factor into the time series model. This study proposes the combination of intervention and outlier analysis in time series models, especially ARIMA. It is intended to minimize the residuals and increase the accuracy of the model so that it is suitable for forecasting. Using the data of inflation rate in Indonesia, the conflict between Russia and Ukraine was used as an intervention factor in this case. Pre-intervention data (before February 2022) is used to construct the ARIMA model (1st model). After that, the modeling process continued by adding the intervention factor to the ARIMA model. The effect caused by the intervention allows an outlier to appear, so the process is continued by adding the outlier factor, called an additive outlier, into the model before (2nd model). The MAPE for the first and second models is 7.96% and 7.57%, respectively. The finding of this research shows that the ARIMA model with intervention and outlier factors, named as the 2nd model, is the best model. This study shows that combining the intervention and outlier factors into ARIMA model can improve the accuracy. The forecasting of the inflation rate in Indonesia for one period ahead in 2023 is in the range of 2.06%.

Analysis of Affecting Variables Actual Inflation Rate at Indonesia

The discussion in this empirical study is to find out what variables can influence the achievement of the actual inflation rate variable in Indonesia in the last ten years. The writer considers that achieving the actual inflation rate targeted by a Central Bank is not simply related to supply and demand imbalances for primary goods alone, but more broadly than that related to dynamic micro and macroeconomic conditions. The writer initially suspected that variables such as the inflation target rate, previous inflation rate, loan interest rate, and gross domestic product had a significant influence on fluctuations in the actual inflation rate in Indonesia. After statistical tests were carried out using the secondary data in the form of the rates of the four independent variables and the actual inflation rate in Indonesia as the dependent variable every year for the last ten years it’s found that only the loan interest rate had a significant and positive effect on the actual inflation rate in Indonesia. which means that the higher the loan interest rate will also increase the actual inflation rate. The three other variables tested in this empirical study, namely the inflation target rate, the previous inflation rate, and the gross domestic product rate, did not have a significant effect.

Prediksi Tingkat Inflasi Di Indonesia Menggunakan Metode Average Based Fuzzy Time Series

Inflation is a very important indicator in maintaining the stability of the country's economy, so it is necessary to predict the inflation rate to determine the movement of the inflation rate in the future. To predict the inflation rate in Indonesia in 2023, this research uses the Average Based Fuzzy Time Series (FTS) method based on Python programming. This method uses the principle of fuzzy set as the basis of its calculation. The data used in this study are monthly data on inflation rates in Indonesia for the period January 2013 - December 2022 obtained from the official website of Bank Indonesia (BI). The results showed that the prediction of the inflation rate in Indonesia in 2023 was in the range of 5.23% - 5.51% with the highest inflation value occurring in April and the lowest inflation value occurring in January. The accuracy level of the Average Based FTS method is calculated using the Mean Absolute Percentage Error (MAPE) of 0.820849835% which indicates that the method can be used to predict the inflation rate.
 Keywords : Average Based, FTS, Inflation, Prediction, Python

Pengaruh Inflasi Terhadap Tingkat Pengangguran Di Kota Bima

The existence of inflation at the same time as unemployment has become a problem in an economy. Inflation is almost unavoidable for a system that has exceeded its peak production capacity. Inflation has an important role in changes in the number of unemployed. (Andiny, Desrini Ningsih, 2018) The higher the inflation rate itself, the higher the level of fulfillment of needs can result in the emergence of poverty among the community which is caused by the higher prices of goods and services and the inflation rate in Indonesia which is capable of fluctuating from year to year. According to (Putong, 2017) inflation is calculated based on index numbers collected from several types of goods bought and sold on the market at each level of economic growth (these goods are of course the most numerous and are the basic needs of society). Based on the economic growth data, an index number is compiled. The index number that takes into account all goods purchased by consumers during each economic growth is called the Consumer Economic Growth (HK) index. According to (Sukirno, 2016) inflation increases suddenly or occurs as a result of a certain event that occurs in outside the government's expectations, for example the effect of calculating the value of money which is very large or political instability. Factors that influence the level of inflation on unemployment in Bima City are because the higher inflation that occurs can result in decreased economic growth, so that there will be an increase in the number of unemployed

Autoregressive Vector (VAR) Method in Analyzing Inflation Persistence in Indonesia

One of the most important economic variables is inflation. It is known that some changes in each major variable in the economy are caused by the rate of inflation, such as unemployment and future economic conditions. This is what makes inflation a variable that is often observed and tested, both theoretically and empirically. Economic development is said to run well if supported by stable inflation values, which will later serve to make the population more prosperous in the future. This research is a quantitative research using secondary data, namely inflation and CPI data from January 2018 to December 2022. Data obtained from Bank Indonesia and the Indonesian Central Bureau of Statistics. This study aims to see persistence in Indonesia and see the effect of CPI on inflation both in the long and short term. Where the model used in this study is using the VAR model of differential levels, because the data is not stationary at the level level. Determination of the optimal lag length is obtained from the lowest Akaike Information Criterion (AIC) value. The results of the analysis show that the persistence of inflation in Indonesia is fairly high. This is because the lag value in inflation is close to 1 or even more, which is 6.71. The high persistence of inflation is expected to cause shocks that affect the inflation rate in Indonesia. Then the length of time it takes for inflation to return to the equilibrium position is about 1 year and a half or 18 months. Furthermore, for the effect of inflation on CPI, in this case it has an influence in the long run, while for the short term influence is only influenced by its own variables.

NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA

The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.

THE IMPACT OF E-MONEY ON INFLATION IN INDONESIA

The purpose of this study was to estimate the influence of using e-money on the inflation rate in Indonesia, using research data in the form of monthly time series data for e-money payments and inflation for 2016-2021 collected from www.bi.go.id, www.bps.go.id/, and www.kemendag.go.id/. This research is quantitative in nature. Regression analysis is a data analysis technique. H0 = Using e-money has no significant influence on the inflation rate; H1 = Using e-money has a substantial effect on the inflation rate. As a result, e-money affects inflation by 0.298 (29.8%). Because t counts t table, H0 is accepted, implying that utilizing e-money has no major effect on the inflation rate. The result is that e-money affects 0.298 (29.8%) inflation. Because t count <t table, H0 is accepted, which means that there is no significant effect of using e-money on the inflation rate. The regression model is Y = 25.237 + 0.583X. With β = 0.435, assuming the use of e-money has a fixed (unchanging) value, then every increase in e-money by 1 unit will increase the inflation rate by 0.583. The conclusion is that using e-money does not significantly affect the inflation rate.

Evaluation of Interest Rate Policies in Creating Financial Stability and Price Stability in Indonesia

This study aims to evaluate the interest rate policy (rediscount policy) by Bank Indonesia to create financial stability and price stability in Indonesia. This research used data on the SBI interest rate, exchange rate of rupiah to US dollar, inflation rate, and money supply in Indonesia from January 2014 to December 2021. The econometric model to test the research hypothesis is the structural equation model - partial least square. The results showed that the interest rate instrument to create financial stability in Indonesia was appropriate because the interest rate had a negative impact on the exchange rate. It means that if desired exchange rate will decrease (the rupiah appreciated) it can be done by increasing the interest rate. However, the policy of raising the interest rate can cause the inflation rate to rise. Because the interest rate has a positive effect on the inflation rate. The money supply can mediate the effect of the interest rate on the inflation rate in Indonesia.

Government expenditure and inflation (Empirical study using Granger causality in Indonesia 2000-2021)

A Government expenditure is one of important things in determine the economic performance of the country, but when it comes to increasing the economic growth, it can cause inflation. The high inflation rate will impact in economics of the nation as a whole. This study aims to investigate the casual relationship between government expenditure and inflation rate in Indonesia during 2000-2021 period. Time series data obtained from the official website of Central Bureau of Statistics of Indonesia and the Finance Ministry of Indonesia is analyzed using Granger Causality Test. The result show that the variables are stationery and have long run relationship. However, they only have one-way relationship in which government expenditure cause inflation rate but inflation rate doesn’t cause government expenditure in Indonesia. We suggest appropriate fiscal-monetary policy mix, redirecting government expenditure to productive sector and maintained strategic in controlling inflation rate.

Indonesia’s tax revenue: The effect of inflation rate and economic growth

<p class="Default">In order to ascertain and analyze the impact of Indonesia's inflation rate and economic growth on tax collections, between 2012 and 2020, the Ministry of Finance's Directorate General of Taxes performed research. Data collection approaches were acquired through literature and documentation for this research, which is categorized as descriptive and verificative research with a quantitative approach. The sampling method employs non-probability sampling, which creates a saturated sample by taking samples from the entire population. Multiple linear regression analysis is used to analyze data, while the t-test and f-test are used to assess hypotheses. The results show that inflation rate has no significant positive effect on tax revenue. Besides, economic growth has no significant negative effect on tax revenue. It is hoped that government will pay attention for tax revenue every year.</p>

Gerakan Pangan Murah (GPM) Terhadap Masyarakat Terdampak Bencana (Dinas Pangan Provinsi Sulawesi Tengah )

Natural disasters are events or series of events that threaten and disrupt people's lives which are caused by natural or non-natural factors, causing casualties, environmental damage, and much more. (Isa, 2016). The occurrence of natural disasters on the regional economy is of course very influential, especially in changes in food commodity prices. Food commodities are a basic need that is important for maintaining human survival. Food commodities have a very important role in economic, social and political aspects. However, in terms of the economy, food commodity prices in Indonesia often experience price changes, especially for disaster-affected areas. Price changes in food commodities are the biggest contributor to the inflation rate in Indonesia. one of the efforts made to control inflation is to carry out a work program, namely the "Cheap Market Movement" which is the government's effort to control the inflation rate. The government agency that has the task of carrying out government affairs in the food sector is the Food Service of the Province of Central Sulawesi. In carrying out the tasks as intended. In this activity, the author carried out the target of activities in the Permanent Residence (HUNTAP) Layana, Mantikulore District.

Panic Buying as a Cause of Inflation According to al-Maqrizi's Perspective

This article was written based on the results of qualitative literature research which aims to determine the impact of panic buying in March 2020 on the inflation rate in Indonesia and to classify panic buying as the cause of inflation based on al-Maqrizi's perspective. The background of this research is because there are only a few studies on panic buying in Islamic economic discourse that can be found when browsing in Google Scholar page, even though panic buying at that period was a problem in various countries. The results of this study are: 1) the panic buying that occurred in Indonesia was not severe, so it does not have an impact on the inflation rate; 2) according to al-Maqrizi’s perspective, panic buying can be classified into human error inflation.

Forecasting Indonesian inflation using a hybrid ARIMA-ANFIS

This paper discusses the prediction of the inflation rate in Indonesia. The data used in this research is assumed to have both linear and non-linear components. The ARIMA model is selected to accommodate the linear component, while the ANFIS method accounts for the non-linear component in the inflation data. Thus, the model is known as the hybrid ARIMA-ANFIS model. The clustering method is performed in the ANFIS model using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 to 6 clusters. The optimal number of clusters is assessed according to the minimum value of the error prediction. To evaluate the performance of the fitted hybrid ARIMA-ANFIS model, it can be compared to the classical ARIMA model and with the ordinary ANFIS model. The result reveals that the best ARIMA model for inflation prediction in Indonesia is ARIMA(2,1,0). In the hybrid ARIMA(2,1,0)-ANFIS model, two clusters are optimal. Meanwhile, the optimum number of clusters in the ordinary ANFIS model is six. The comparison of prediction accuracy confirms that the hybrid model is superior to the individual model alone of either ARIMA or ANFIS model.

The Effect of Cashless Payment and the Amount of the Money Circulation on the Inflation Rate in Indonesia for the Period of 2018-2021

This study aims to determine the effect of cashless payments and the money supply on the inflation rate in Indonesia in the period 2018-2021. The independent variables used are cashless payment and the money supply. While the dependent variable used in this study is the inflation rate. The research data used is secondary data, namely the number of cashless payment transactions and the inflation rate whose data source comes from the official website of Bank Indonesia (BI) and the money supply whose data source comes from the official website of the Central Statistics Agency (BPS). This research data consists of 48 data in which the data used is monthly data. The research method used in this research is quantitative with a descriptive approach. The analytical technique used in this research is multiple linear regression analysis using SPSS Version 22.00 analysis tool. The results obtained in this study are simultaneously independent variables in the form of cashless payments and the money supply has an effect on the dependent variable, namely the inflation rate in Indonesia in the period 2018-2021. While partially, the independent variable in the form of cashless payments affects the inflation rate in Indonesia in the period 2018-2021 and the independent variable in the form of the money supply also affects the inflation rate in Indonesia in the period 2018-2021.

The Effect of Cashless Payment and the Amount of the Money Circulation on the Inflation Rate in Indonesia for the Period of 2018-2021

This study aims to determine the effect of cashless payments and the money supply on the inflation rate in Indonesia in the period 2018-2021. The independent variables used are cashless payment and the money supply. While the dependent variable used in this study is the inflation rate. The research data used is secondary data, namely the number of cashless payment transactions and the inflation rate whose data source comes from the official website of Bank Indonesia (BI) and the money supply whose data source comes from the official website of the Central Statistics Agency (BPS). This research data consists of 48 data in which the data used is monthly data. The research method used in this research is quantitative with a descriptive approach. The analytical technique used in this research is multiple linear regression analysis using SPSS Version 22.00 analysis tool. The results obtained in this study are simultaneously independent variables in the form of cashless payments and the money supply has an effect on the dependent variable, namely the inflation rate in Indonesia in the period 2018-2021. While partially, the independent variable in the form of cashless payments affects the inflation rate in Indonesia in the period 2018-2021 and the independent variable in the form of the money supply also affects the inflation rate in Indonesia in the period 2018-2021.