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All Journal Aptisi Transactions on Management IAIC Transactions on Sustainable Digital Innovation (ITSDI)
Terah, Yochebed Anggraini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology

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Journal : Aptisi Transactions on Management

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Decision-Making Techniques using LSTM on Antam Mining Shares before and during the COVID-19 Pandemic in Indonesia Badri, Ahmad Kamal; Heikal, Jerry; Nurjaman, Deden Roni; Terah, Yochebed Anggraini
APTISI Transactions on Management (ATM) Vol 6 No 2 (2022): ATM (APTISI Transactions on Management: July)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v6i2.1776

Abstract

Stocks, apart from having volatile and chaotic characteristics, also have various kinds of noise, non-linear and non-stationary movements, making them difficult to predict accurately. Therefore, the risk of investing in stocks depends on the skills of investors or traders in making judgments and decisions. This study aims to use Long Short-Term Memory (LSTM) as a decision-making technique with historical stock prices as the sole predictor, then implement it in conditions before and during the COVID-19 pandemic. The study results concluded that Long Short-Term Memory (LSTM) could be used as a decision-making technique in conditions before and during the COVID-19 pandemic with historical price inputs as the sole predictor. Based on the research that has been done, the following conclusions can be drawn: The LSTM model can predict stock prices well using historical stock prices as the sole predictor. The LSTM model can be used as a trading decision-making technique for day traders. The risk of stock prediction using the LSTM method in 2019 before the COVID pandemic was proven to be lower than in 2020 during the COVID pandemic. For further research, researchers can conduct more in-depth research on the risk criteria for making trading decisions as an essential reference that can be used to select the LSTM model.
Financial Management of Indonesian Hajj Against the Yield by Using a Dynamics System Model Mariyanti, Tatik; Jayaprawira, Acep R; Terah, Yochebed Anggraini; Pujiharto, Yaya Ruhenda Casmita
APTISI Transactions on Management (ATM) Vol 7 No 1 (2023): ATM (APTISI Transactions on Management: January )
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v7i1.1818

Abstract

This research aims to analyze the financial management of Indonesian hajj on yield using a dynamic system model and determine and simulate the return obtained with the expenditure so that the hajj funds remain safe. In addition, the purpose of this research is to provide input on policy strategies to the BPKH in increasing hajj financial yields. The method used in this research is dynamic systems modeling. The resulting model structure formulation is illustrated by a causal loop diagram and a stock-flow diagram. The results obtained were then simulated, and model validation was carried out using AME and AVE. Operational data used in this study uses time-series data. This study's population or several samples are annual historical data during the research period.  In modeling the dynamic system of hajj financial management on yield, it is divided into 2 (two) sub-models, namely the economic sub-model and the social sub-model. Meanwhile, to find out and simulate the yield obtained with the yield expenditure, 3 (three) scenarios were made, namely the existing, moderate, and optimistic scenarios. From the simulation results, it can be seen that making changes to portfolio policies with an optimistic scheme in the form of placements in Islamic Banks with a maximum of 20% and 80% investment and increasing the initial deposits of pilgrims from IDR 25 million to IDR 30 million in 2022 is a government policy intervention that produces optimal yield.
Co-Authors Acep R Jayaprawira Badri, Ahmad Kamal Bist, Ankur Singh Deden Nurjaman Fazri, Muhammad Faizal Heikal, Jerry Mehra, Nidhi Pujiharto, Yaya Ruhenda Casmita Rawat, Bhupesh Tatik Mariyanti, Tatik