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All Journal International Journal of Electronics and Communications Systems
Alvionita, Mika
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Computer Science & IT Electrical & Electronics Engineering

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Indonesian Consumer Price Index Forecasting Using Autoregressive Integrated Moving Average Ishak, Shahnaz Salsabila; Abednego, Michael; Sari, Dian Maya; Sabila, Viyonisa Syafa; Khoirunnisa, Khoirunnisa; Alvionita, Mika; Muthoharoh, Luluk
International Journal of Electronics and Communications Systems Vol. 3 No. 1 (2023): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v3i1.18252

Abstract

The Consumer Price Index is one of the indicators used to confirm financial success in inflation management. This study aims to help determine the CPI prediction value in Indonesia for the next twelve periods in a month using the ARIMA (Autoregressive Integrated Moving Average) method using the data from January 2015 to March 2022. The results obtained show that the best model that can be used for forecasting is the ARIMA model (2,1,2) with drift with Akaike's Information Criterion (AIC) values of 2190.84. The results of Indonesia's accurate CPI forecasting can be used to assess inflation management for policymaking in the context of controlling inflation.It can be concluded that Based on the analysis, the optimal ARIMA model for forecasting Indonesia's CPI is ARIMA (2,1,2) with drift, aiding in evaluating inflation management for policymaking
Analysis of Google Stock Prices from 2020 to 2023 using the GARCH Method Athaulloh, M Farhan; Mubarok, Husni Na’fa; Sharov, Sergii; Hati, Berliyana Kesuma; Muthoharoh, Luluk; Alvionita, Mika
International Journal of Electronics and Communications Systems Vol. 3 No. 2 (2023): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v3i2.20899

Abstract

This research focuses on Google's share price movements, considering their significant impact on the financial market, using Google's share price data from 2020 to 2023. The aim is to analyze error variance and forecast and provide valuable information to stockbrokers and investors. The ARMA model has shortcomings in dealing with volatility, so the GARCH model is used to overcome it. Research methods include financial data analysis, preprocessing, and modeling with GARCH. The rolling forecast method describes changes in price patterns over time. Evaluation using MAPE validates the prediction accuracy of the ARIMA model. The best model chosen with the most negligible AIC value criteria was the ARIMA(3,0,2)GARCH(1,1) model. The forecasting results show accurate stock price predictions with an average MAPE value of 20.7 percent. This research provides an essential basis for brokers and investors in making investment decisions based on a deep understanding of the dynamics of Google's share price movements in the above time frame.
Co-Authors Abednego, Michael Athaulloh, M Farhan Dian Maya Sari Hati, Berliyana Kesuma Ishak, Shahnaz Salsabila Mochammad Imron Awalludin Mubarok, Husni Na’fa Muthoharoh, Luluk Sabila, Viyonisa Syafa Sharov, Sergii