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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Dialektika: Jurnal Ekonomi dan Ilmu Sosial
Junita, Tarisya Permata
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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APPLICATION OF THE NEURAL NETWORK AUTOREGRESSIVE (NNAR) METHOD FOR FORECASTING THE VALUE OF OIL AND GAS EXPORTS IN INDONESIA Junita, Tarisya Permata; Kartikasari, Mujiati Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0341-0348

Abstract

Indonesia is one of the countries with the most diversity and abundant natural resources, consisting of many commodities, and has enormous trade potential with other countries The success of economic activity a country can be measured by the amount of economic growth that occurs in the country. A recession is when a country's economic condition is getting worse. Meanwhile, a recession in Indonesia is expected to occur in 2023. In a 2022 news issue written by the editorial team, tirto.id said that some experts say that if 2023 is a recession, the cause is due to a spike in inflation from the impact of the Russia-Ukraine conflict. It is known that the value of oil and gas exports affects the Indonesian economy. Any increase in the value of oil and gas exports will be followed by an increase in economic growth, and vice versa. However, over time, the value of oil and gas exports has decreased every year. Therefore, forecasting the value of oil and gas exports is needed so that the country's economic sector development strategy can be on target. In addition, oil and gas export forecasting is also needed to determine the distribution of goods exports that must be carried out. In this study, we forecast the value of oil and gas exports using the neural network autoregressive (NNAR) method. The choice of this method is made because there is no assumption of normality of the residuals and white noise like in autoregressive models. From the NNAR method, the best model results are obtained, namely NNAR (2,3) with a MAPE value of 11.75640%, which means that this model has very good forecasting performance.
Analisis Pengaruh Kenaikan Harga BBM Terhadap Pergerakan Saham Sektor Transportasi dan Logistik Tanza, Alifia; Maulidya, Rizka Putri; Junita, Tarisya Permata; Widodo, Edy
DIALEKTIKA: Jurnal Ekonomi dan Ilmu Sosial Vol 8 No 1 (2023): Dialektika : Jurnal Ekonomi dan Ilmu Sosial
Publisher : Prodi Manajemen Fakultas Ekonomi dan Bisnis Universitas Islam Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36636/dialektika.v8i1.2044

Abstract

The capital market or also known as the stock exchange is one of the financial market activities, which carry out economic activities and financial functions, where the activities carried out on the stock exchange are very dependent on government policy. On April 1, the government issued a new policy, which was an increase in the price of Pertamax fuel oil. Although the dynamics of the capital market were not directly affected, the increase in fuel prices could not be separated from stock market activity. This research was conducted with the intention of seeing whether there was a stock market reaction, especially shares in the transportation and logistics sector, to the increase in fuel prices on April 1, 2022. This study used the Abnormal Return (AR) and Trading Volume Activity (TVA) variables. ) with a period of 30 days, before and after the notification of the increase in fuel prices. The method used is to use the Wilcoxon test. From this study, it was found that the capital market did not react to the policy of increasing fuel prices. This can be seen from the absence of differences in AR and TVA, before and after the notification of the increase in fuel prices.
Co-Authors Edy Widodo Kartikasari, Mujiati Dwi Maulidya, Rizka Putri Tanza, Alifia