Prices In Indonesia Research Articles

Perbandingan Algoritma Support Vector Machine dan Random Forest untuk Analisis Sentimen Terhadap Kebijakan Pemerintah Indonesia Terkait Kenaikan Harga BBM Tahun 2022

The commodity of fuel oil (BBM) is the main commodity and the driving force of business. The increase in world oil prices is a threat to countries around the world, one of which is Indonesia. With the turbulent conditions in several countries, the Indonesian government decided to cut fuel subsidies which had an impact on price increases. The policy invited all Indonesian people and criticized it on various social media. The purpose of this research is to find out which algorithm has a better accuracy rate and to provide input to the government about public opinion regarding the increase in fuel prices in Indonesia. From the test results both work well, this is evidenced by the accuracy value obtained, where the support vector machine algorithm produces an accuracy value of 77%, while the Random Forest algorithm produces an accuracy value of 76%. So it can be concluded that the support vector machine algorithm has a fairly good accuracy rate compared to the Random Forest algorithm.

What Drives the Islamic Stock Market Price in Indonesia?

<p>Saat ini, pasar modal syariah berkembang semakin pesat. Hal ini ditandai dengan munculnya berbagai indeks saham syariah termasuk Jakarta Islamic Index (JII). Pasar modal adalah lembaga keuangan yang berkontribusi dalam pengelolaan dana investasi, serta menjadi penggerak utama dalam sistem ekonomi. Namun, pasar modal syariah dalam beberapa dekade terakhir menunjukkan pergerakan yang fluktuatif, diduga ada faktor-faktor yang mempengaruhi pergerakannya. Tujuan dari penelitian ini adalah untuk mengkaji faktor-faktor yang mempengaruhi pergerakan Jakarta Islamic Index. Metode yang digunakan dalam penelitian ini adalah deskriptif kuantitatif dengan data time series tahun 2009-2021. Variabel independen dalam penelitian ini adalah ekonomi makro yang terdiri dari inflasi (INF), Indeks Produksi Industri (IPI), BI Rate, nilai tukar, dan Indeks Harga Saham Gabungan Indonesia (IHSG) sedangkan variabel dependennya adalah Jakarta Islamic Index (JII). Alat analisis yang digunakan dalam penelitian ini adalah Vector Error Correction Model (VECM). Hasil penelitian ini menunjukkan bahwa secara umum, dalam jangka pendek, pergerakan Indeks Saham Syariah yaitu Jakarta Islamic Index (JII) tidak banyak dipengaruhi oleh variabel makroekonomi. Sementara itu, dalam jangka panjang, pergerakan indeks saham syariah di Indonesia dipengaruhi oleh variabel makroekonomi.</p><em>Currently, the Islamic capital market is growing rapidly. This is marked by the emergence of various Islamic stock indices, including the Jakarta Islamic Index (JII). The capital market is a financial institution that contributes to the management of investment funds as well as being the main driving force in the economic system. However, the Islamic capital market in recent decades has shown fluctuating movements; allegedly, some factors affect its movement. The purpose of this study is to examine the factors that influence the movement of the Jakarta Islamic Index. The method used in this study is quantitative-descriptive with time series data from 2009 to 2021. The independent variable in this study is macroeconomics, consisting of inflation (INF), industrial production index (IPI), BI rate, exchange rate, and Indonesia Composite Stock Price Index (ICI), while the dependent variable is the Jakarta Islamic Index (JII). The analysis tool used in this study is the Vector Error Correction Model (VECM). The results of this study show that, in general, in the short term, the movement of the Sharia Stock Index, namely the Jakarta Islamic Index (JII), is not much influenced by macroeconomic variables. Meanwhile, in the long run, the movement of Islamic stock indices in Indonesia is influenced by macroeconomic variables.</em>

Analisis Pola Harga Saham dengan Modifikasi Metode Eksponen Hurst dan Box Counting

A stock chart is a graphical representation of a stock's past performance. The dynamic pattern of stock prices is important to know because an investor wants to invest, expecting high returns with low risk. The dynamic pattern of stock prices can be known by fractal dimension analysis because the stock price graph is self-affine, which is one of the properties of fractal objects. In this study, a modification of the Hurst exponent method and box counting are used to analyze the fractal dimension. The calculated results are classified into three types, namely random, persistent and anti-persistent. Two data intervals are observed, namely January 2018-December 2021 stock prices (48 data) and January 2018-June 2022 stock prices (54 data). The resulting Hurst exponents of the two intervals are 0.043 and 0.003. Based on the resulting Hurst exponent value, the data is anti-persistent because the value 0<H<0.5. Then, the fractal dimension value obtained by applying the box-counting method is 1.547 and 1.562, meaning that Bank Rakyat Indonesia's price pattern is anti-persistent. The meaning of anti-persistent is that in certain months the stock has a high price and in the following months the stock has a low price to be traded.

The Impact of The 2020 Health Crisis on Exchange Rates and Stock Prices in Indonesia

The infrastructure sector during the health crisis had an impact by declining by around 12% until the end of 2020. One that can cause a decrease in stock prices is the exchange rate. PT Jasa Marga (Persero) Tbk. is one of the companies that showed active stock price activity during the health crisis period. The data sample of this study is to take daily data from exchange rates and stock prices during 2020 with a sample number of 242. Empirical results show that the correlation coefficient is -0.89, hence the relationship between the exchange rate and stock price is very strong and contradictory. Ha's partial test results were accepted and Ho was rejected, i.e. the exchange rate was partially significant and affected the stock price. The R square results of 79.8 percent that the exchange rate .

PRICE REVIEW ANALYSIS ON CRYPTOCURRENCIES IN INDONESIA

The purpose of this study is to analyze the price review of Cryptocurrencies in Indonesia. This research uses qualitative research methods with the nature of literature review. qualitative research conducted and research purposes, sampling may not be necessary in qualitative research with a literature review. In this case, the researcher did not collect data from respondents or informants, so there was no need to take samples. The results of the study show that cryptocurrency prices in Indonesia are still considered digital assets and have not been recognized as legal currency. Nonetheless, Cryptocurrency transactions still occur in Indonesia and several Cryptocurrency exchanges also operate in Indonesia by complying with regulations set by the government. The development of blockchain technology and Cryptocurrency adoption continues to increase in Indonesia, and the Indonesian government has also introduced initiatives such as the Blockchain Indonesia program to increase awareness and adoption of blockchain technology in Indonesia. As an investor, keep in mind that Cryptocurrency is still very risky and very volatile. Therefore, it is necessary to be careful and carefully consider the risks before deciding to invest. It is also important to always follow the regulations and policies related to Cryptocurrency set by the Indonesian government.

Monetary policy transmission mechanism in Indonesia period 2002: Q1 – 2020: Q4: interest rate and asset price channel

This study aims to determine and analyze the effect of the monetary policy transmission mechanism on the interest rate channel and asset prices in Indonesia in achieving the ultimate goal of price stability throughout the period 2002:Q1 – 2020:Q4. This study uses secondary data related to policy interest rate variables, operational target interest rates, information variables, and final target variables for each channel of the monetary policy transmission mechanism. The analytical tool used in this research is the Vector Error Correction Model (VECM) which is processed using statistical software Eviews 10. The results of the analysis on the monetary policy transmission mechanism model of the interest rate channel indicate that in the short term, investment lending variables have a negative and significant effect on The CPI, meanwhile, the investment credit interest rate variable has a negative and significant effect. The results of the long-term analysis of the monetary policy transmission mechanism model of the interest rate channel show that the interest rate for investment credit, consumption credit, and the BI7DRR interest rate have a positive and significant effect on the CPI, while the variables for consumption credit interest rates, investment credit distribution, and real GDP negative and significant effect on the CPI. The results of the VECM analysis for the monetary policy transmission mechanism model for asset price channels confirm that the positive Error Correction Term (ECT) coefficient indicates that there is no adjustment mechanism towards long-term equilibrium. The results of the classical assumption test for the monetary policy transmission mechanism model of asset price channels also confirm deviations in the assumptions of normality and heteroscedasticity. Therefore, the economic interpretation of the VECM estimation on the monetary policy transmission mechanism model of the asset price channel cannot be carried out and requires the use of other analytical tools to produce a valid model .

Kebijakan Pemerintah dalam Mengatasi Kenaikan Harga Beras di Indonesia

Introduction: BPS recorded that Indonesia's inflation or Consumer Price Index (CPI) was 3.27 per cent in August 2023 annually, causing retail rice prices in August 2023 to increase by 1.43 per cent and rise by 13.76 per cent. This research aims to analyze government policies in dealing with rising rice prices in Indonesia in 2023. Research Methods: The method used is a qualitative method with a literature study approach. With types and sources of secondary data obtained from journals, the Central Statistics Agency and the National Strategic Food Price Information Center. Results: The results of this research show that to overcome this problem, the government is carrying out: 1.) importing rice from several rice-producing countries, 2.) ensuring rice reserves in Perum Bulog, 3.) carrying out market operations, 4.) holding a cheap food movement, 5.) Urge the public not to panic buying, 6.) Distribute 10 kilograms of rice aid to Beneficiary Families (KPM). Conclusion: Apart from these short-term policies, the government must also make long-term policies, such as increasing rice production and distribution.

Abnormal Returns around the Announcement of Covid-19 Cases on Stock Prices in Indonesia

The purpose of this study is to find out whether there is a difference in abnormal returns on the day around the first announcement of the COVID-19 case and to find out whether there is a difference in abnormal returns after and before the first announcement of the COVID-19 case in Indonesia in pharmaceutical industry stocks listed on the Indonesia Stock Exchange. The population in this study are pharmaceutical companies listed on the Indonesian Stock Exchange. While the sampling technique was purposive sampling. The data analysis technique used in this study was a one-sample t-test on normally distributed data and one sample Wilcoxon signed ranked test for abnormally distributed data for days around the announcement of Covid 19. The results of this study show whether there is no difference in abnormal returns around the announcement of Covid -19. And also there is no difference in abnormal returns 5 days before and after the announcement of Covid 19.

Pengaruh Harga Emas, Harga Minyak Dunia, dan Harga Minyak Kelapa Sawit Terhadap Harga Saham Periode 2019 – 2022

This research was conducted to observe the effect of gold prices, world oil prices and palm oil prices as commodity prices on the movement of stock prices in Indonesia from 2019 to 2022. The research method used is a quantitative method, using secondary data. The analysis technique used is multiple linear regression analysis technique. The research data is in the form of time series data, namely monthly data from January 2019 to December 2022. The total data is 48 for each variable. The conclusion of this study is that the price of gold has no significant effect on the Jakarta Composite Index (IHSG). World oil prices have a significant effect on the Jakarta Composite Index (IHSG). The price of Palm Oil has a significant effect on the Jakarta Composite Index (IHSG). The results of this study are to prove that gold price movements cannot be used as an indication of stock prices on the Indonesia Stock Exchange (IDX). Movements in world oil prices and palm oil prices can indicate stock prices on the Indonesia Stock Exchange (IDX). The above conditions apply for the long term.

The Influence of Macroeconomic Factors on the Effectiveness of Manufacturing Company Share Prices in Indonesia: A Case Study at PT Mustika Ratu Tbk.

Beauty industry sector in Indonesia can be used as a profitable investment location for both local and foreign investors. The Investment growth levels in a country can be influenced by the level of economic growth in the country concerned. The capital market is an activity that occurs in the economic market which is used for various financial instruments that have long term terms with a time span of more than 1 year. A share is a sign of participation in capital that has been invested by a person or business entity in the company concerned. Inflation itself can be said to be a a situation when the price of an item is experiencing increase that occurs continuously and also occurs globally. In general, the rupiah exchange rate is a value or can also be called the price level of a country's currency which can be measured and expressed in another country's currency. The interest rate is an interest rate that has been determined and then issued by Bank Indonesia (BI) for the issuance of securities or certificates that will also be issued by Bank Indonesia. The research being carried out also has a goal, namely to be able to analyze the independent variables, namely inflation, the rupiah exchange rate and interest rates which can influence the dependent variable, namely the share price of the company PT Mustika Ratu Tbk. The method that can be used is Ordinary Least Squares (OLS). Data processing results show that this inflation rate variable can influence, but not significantly, the level of share prices in PT Mustika Ratu Tbk. The rupiah exchange rate has a significant effect influence on the share price of PT Mustika Ratu Tbk. The interest rate variable has no influence on the share price of PT Mustika Ratu Tbk.

Symmetric and Asymmetric Effects of Exchange Rate on Indonesia Stock Prices

Understanding the effect of exchange rates on stock prices is essential essential because of how they are tied to various aspects of the economy. This study examines the symmetric and asymmetric effects of exchange rate changes on stock prices in Indonesia using both auto regressive distributed lag (ARDL) and nonlinear ARDL (NARDL) techniques. To determine short run and the long run association between the study variables. This study using Indonesia monthly data collected from January 2022 to December 2022. The study discovered a long-run equilibrium for both the ARDL and NARDL models, showing a symmetric short and longrun relationship between exchange rates and stock prices. Exchange rates have a negative effect on stock prices, which lowers investor trust in the market. Even in linear and nonlinear analysis, inflation has a negative impact on stock prices over the and long terms. High inflation can reduce consumer purchasing power, which can result in a drop in sales and profitability for businesses that significantly rely on consumer spending. authorities and investors require taking consider the relationship between exchange rates and stock prices to manage foreign exchange and stock markets.

Analysis Forecasting of Gasoline Prices in Some ASEAN Countries by Using State Space Representation on Vector Autoregressive Model

Research on the price of gasoline has become a topic of research that has been carried out by many researchers. The topic is interesting because the price of gasoline has a relationship with many aspects of people's lives. This study aims to examine the relationship pattern of gasoline prices in several ASEAN countries: Indonesia, Malaysia, and Vietnam, and to make gasoline price forecasting in these three countries for the next 12 months. This study uses a multivariate time series approach; first, the best vector autoregressive (VAR(p)) model will be built based on Akaike's Information Criterion (AIC). Based on the best VAR(p) model, granger-causality analysis is discussed, and for forecasting gasoline prices, a state space model will be developed based on the best VAR(p). State vectors are built based on canonical correlation analysis. Based on the results of granger causality analysis, gasoline prices in Indonesia are affected by past gasoline prices in Vietnam; gasoline prices in Malaysia are affected by past gasoline prices in Indonesia and Vietnam. The results of forecasting analysis for the next 12 months using the state space model show that gasoline prices in Indonesia for the next 12 months tend to have a downward trend; gasoline prices in Malaysia for the next 12 months tend to have an upward trend; and the price of gasoline in Vietnam for the next 12 months tends to have an upward trend for the first 6 months and then has a downward trend for the next 6 months.

Solar Cell Manufacturing Cost Analysis and its Impact to Solar Power Electricity Price in Indonesia

World energy consumption continues to increase, with a growth of 1,3% annually during 2011 - 2021. To deal with that situation, in 2021, Indonesia Electrity Stated-Own Company (PLN) issued a report about the electricity supply business plan (RUPTL) 2021-2030. The power plant projected to grow is solar photovoltaic (PV), reaching 4,6 GW within ten years. However, in reality, the achievement of developing solar power plants is still 0,02 GW. Several factors that caused low realization are the solar power plant component industry needs to be better developed, and the regulated electricity price based on Presidential Decree 112/2022 needs to be increased. This study aims to determine the cost structure of solar module manufacturing and the impact on electricity prices. The calculation method used is financial modeling with economic parameters such as Internal Rate of Return (IRR), payback period, and Net Present Value (NPV) as parameters for project feasibility. The results of this study show that the economic price of solar power plants in Indonesia is USD 0,149/kWh. Meanwhile, based on a sensitivity analysis using electricity prices based on Presidential Decree, reducing solar module costs up to 50% still does not make the project feasible.

CNN-BASED ARTIFICIAL INTELLIGENCE (AI) IMPLEMENTATION TO IDENTIFY BASMATI RICE IN SUBANG DISTRICT

The existence of Basmati rice among the upper middle class in Indonesia is increasingly popular. Unfortunately, this rice is only grown in northern India and Pakistan. Fulfillment of rice must be imported and the price in Indonesia is relatively expensive. Responding to this phenomenon, the Center for Rice Research (BB Padi), the Agricultural Research and Development Agency succeeded in assembling a special rice variety Basmati. And given the name Baroma, an abbreviation of type Basmati Aromatic rice. And Baroma rice was launched in Subang in 2019 until now it has been recorded that several agricultural lands in Subang have planted this type. The more types of rice varieties, the more types of rice will be found. So that it will make consumers difficult to distinguish between types of rice with one another. Therefore, we need a solution to overcome this problem. And one solution that can be used is to use AI technology, as in the research we did. Using the CNN algorithm produces very good accuracy for detecting types of rice such as the type of data used for training data and test data. From the results of the model training carried out, it produces an accuracy rate of 98,52% while model testing to see how well the model predicts the label correctly is 97,80%.

APPLICATION OF ARCH/GARCH MODELS IN ANALYSIS OF PRICE VOLATILITIES LOCAL, IMPORTS (USA), AND WORLD

Soybeans are one of Indonesia’s strategis agricultural commodities. Soybean prices fluctuation due to high demand and low stock of soybeans. This study was conducted to determine the behavior of local soybean prices in Indonesia, imported soybeans from the USA, and world soybeans. This study uses monthly soybean price time series data from January 2017 to March 2022 for local and world soybean price data and for imported soybean price data (USA) from January 2018 to March 2022. The data is obtained from the Kementrian Perdagangan, UN Comtrade, and the World Bank. The research method used is the ARCH/GARCH analysis method. The result of this study is the estimation of the GARCH model on the volatility of local soybean prices in Indonesia, imported soybeans (USA), and world soybeans obtained the GARCH (1,1) model. Based on the GARCH (1,1) model, the price of local soybeans in Indonesia is more volatile than the price of imported soybeans (USA) and the price of world soybeans is more volatile than the price of imported soybeans (USA). The prices of local soybeans in Indonesia, imported soybeans, and world soybeans are more volatile after the Covid-19 pandemic than before the Covid-19 pandemic. Future forecasts of local soybean prices in Indonesia, imported soybeans, and world soybeans are predicted to increase with the highest price in December 2024 of IDR 15,391.21 for local soybeans, IDR 11,303.92 for imported soybeans, and IDR 12,069.11 for world soybeans. This price forecast can be used as a reference by the government in planning soybean import policies.

Pengaruh Pembangunan Pertanian terhadap Pertumbuhan Ekonomi Indonesia Tahun 1985-2022

This quantitative research aims to determine the short and long-term relationship between agricultural development and economic growth in Indonesia from 1985-2022. It employed the ECM (Error Correction Model) method. The data involved secondary data from 1985-2022 collected from the official website of the World Bank. There were two variables in this research: Economic Growth and Agricultural Development. Economic growth data were based on constant prices in Indonesia for 1985-2022 from the World Bank website. The data was manifested in percent. The second variable was agricultural development, namely agriculture, forestry, and fisheries. It referred to ISIC divisions 1-3 and included forestry, hunting, and fisheries, as well as food crop cultivation and livestock production. Agricultural development data covering 1985-2022 was gathered from the World Bank website. The data was shown in percent. The results indicated that the agricultural development variable had a short-term effect on economic growth in Indonesia. The ECT coefficient was significant in the short term but had a negative relationship. Thus, the correlation was negative. Meanwhile, the long-term relationship between agricultural development and economic growth did not show a significant effect. Agricultural development variables had a long-term negative effect on economic growth in Indonesia. This research is hoped to provide new information and discussions that are in line with the analysis of the influence of agricultural development on economic growth in Indonesia. In addition, it can share the views of the Indonesian government in formulating policies regarding the analysis of agricultural development in Indonesia.

Predicting daily precision improvement of Jakarta Islamic Index in Indonesia’s Islamic stock market using big data mining

PurposeThis paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.Design/methodology/approachThis research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.FindingsThe experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.Practical implicationsThis research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.Originality/valueThis research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

PROSPEK EKONOMI INDONESIA: PERSPEKTIF MAKROEKONOMI

This study is a literary study that aims to analyze Indonesia's economic prospects from a macroeconomic perspective. This is very important considering that macroeconomic variables are very much used as indicators in economic development and growth. Such as inflation, the rate of population growth and so on. From this literature study, it was obtained that the Indonesian Economic Prospects for the future cannot be categorized as having a better prospect, this can be seen in the high level of displacement until 2022, even world economic observers think it is difficult for Indonesia to be able to suppress the growth rate of unemployment considering that during the Covid-19 pandemic alone there have been many layoffs. In addition, the inflation rate tends to increase due to the impact of the increase in consumer prices in Indonesia coupled with the Russian invasion of Ukraiana which also has an indirect impact on Indonesia considering that Russia controls world gas and many European Union countries are very dependent on Russia.

Analysis of beef market integration between consumer and producer regions in Indonesia

Abstract The research employs secondary data consisting of time series data on beef prices from the consumer regions (Jakarta, Banten, and West Java Provinces) and the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) obtained from the Center for Information of Strategic Food Prices period January 2018–July 2022. The analytical model utilizes the estimated VAR (vector autoregression)/VECM (vector error correction model). The mean beef price in Indonesia’s producer regions exceeds the normal level (above the reference price), except for East Nusa Tenggara Province, as well as in the consumer regions, which are significantly above the normal level. Beef prices in the producer and consumer regions have a mutual influence in the current and previous periods. The presence of cointegration implies that in the long term, the beef market in the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung) is integrated with the consumer regions (Jakarta, Banten, and West Java Provinces) in Indonesia. The most substantial impact of the shock of beef prices in the region of the largest producer is Lampung Province. This research concludes that there is a cointegration of beef prices; in the long term, the beef market in producer regions is integrated with the consumers in Indonesia. Similarly, the beef market will be integrated into producer regions with the consumers in the short term. The VECM is a beef price forecasting model in the producer and consumer regions, which can be considered to have excellent performance.

Pengaruh Volume Impor Bawang Putih Terhadap Harga Eceran Bawang Putih di Indonesia

Garlic import has been carried out by Indonesian government in 2011-2020 because domestic production is only sufficient for about three to five percent of people’s needs. The addition supply of imported garlic in domestic market due to the import policy is expected to reduce garlic retail price. This study aims to examine the effect of garlic import volume on garlic retail price in Indonesia. By using Distributed Lag Model (DL-Model) and Vector Error Correction Model (VECM) with monthly data for Januari 2011 – December 2020, this study shows that the effect of garlic imports volume begins to reduce the retail price of garlic after one month of import implementation. The effect continues to be significant up to 6 months after the import implementation. These results indicate, within a period of one month (short term), the policy of importing garlic has not been able to affect the retail price of garlic. However, in the long term (after one month to 6 months), the policy of importing garlic significantly affects the decline in retail prices of garlic.