Articles

Stock Price Forecasting on Time Series Data Using the Long Short-Term Memory (LSTM) Model

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Stock price forecasting on time series data is a complex task due to the dynamic and uncertain nature of financial markets. This research aims to forecast stock prices by applying an advanced machine learning model, namely Long Short-Term Memory (LSTM), a deep learning architecture that excels in capturing long-term dependencies in time series data. The dataset used in this study consists of 1221 daily ANTM.JK stock price data over the period April 30, 2019 to April 30, 2024. The model was trained and evaluated using performance metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) in measuring the level of forecasting accuracy. The results show that the LSTM model can accurately predict stock prices on time series data, as evidenced by the MAPE accuracy evaluation value of 2.52% and RMSE of 54.64. These findings indicate that the LSTM model is effective in predicting stock prices on time series data and can be used as a supporting tool in making the right investment decisions.

Comparison of Diagnostic Accuracy of CT scan and USG in Right Upper Quadrant Pain: A Review Analysis

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Purpose: Acute right upper quadrant (RUQ) pain is a common presenting symptom in emergency departments and outpatient medical practices, and is most commonly attributable to biliary and hepatic pathology. The main objective of the study is to systematically analyse the comparison of diagnostic accuracy of ultrasound and computed tomography in right upper quadrant pain.

Material and methods: This study was conducted using a systematic search on Google scholar, Pubmed and Web of science published until 20th June 2020. The cited references of retrieved articles and previous reviews were also manually checked to identify any additional eligible studies with indexed search terms for cholecystitis, US, cholescintigraphy, CT, and MR imaging.

Results: After excluding duplicates and articles that did not meet the inclusion criteria, we obtained 30 articles with full-texts which were read for further evaluation, where another 60 were excluded as irrelevant. Overall, we included 30 articles that directly match on the inclusion criteria.

Conclusion: It is concluded that RUQ pain were not as good as sensitivities reported in prior studies. CT was statistically significantly better for the diagnosis of RUQ pain than US, most likely because of an unclear clinical picture, the patient population, and a high proportion of poor-quality US examinations. However, US is still our first test of choice if RUQ pain is suspected clinically, whereas CT is performed when the clinical picture is unclear.