Bitcoin USD Closing Price (BTC-USD) Comparison Using Simple Moving Average And Radial Basis Function Neural Network Methods

Yudono, Muchtar Ali Setyo; Sidik, Aryo De Wibowo Muhammad; Kusumah, Ilman Himawan; Suryana, Anang; Junfithrana, Anggy Pradiftha; Nugraha, Adi; Artiyasa, Marina; Edwinanto, Edwinanto; Imamulhak, Yufriana

doi:10.52005/fidelity.v4i2.74

Cited by 6 publications

(3 citation statements)

References 13 publications

(16 reference statements)

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“…Data cleaning is a data cleansing process that involves evaluating data quality and making changes, updates, or deleting incorrect, incomplete, and inaccurate data. The data cleaning process can be done manually or automatically depending on the needs and complexity [15]. In data cleaning, several types of data problems need to be considered, such as missing values, outliers, duplicates, inconsistencies, and mislabels [16].…”

Section: Data Cleaningmentioning

confidence: 99%

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

Setyarini,

Gayatri,

Aditya

et al. 2024

matrik

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A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.

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Section: Data Cleaningmentioning

confidence: 99%

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

Setyarini,

Gayatri,

Aditya

et al. 2024

matrik

View full text Add to dashboard Cite

show abstract

“…Additionally, feature selection is performed to identify the most relevant features for predicting prices, usually used for both cryptocurrency and stock markets. Among the various potential indicators, simple moving average (SMA) [9], relative strength index (RSI) [10], moving average convergence divergence (MACD) [11] and on-balance volume (OBV) [12] are selected as the most influential indicators based on previous literature and domain knowledge. SMA is a suitable feature for prediction because it provides a smoothed representation of historical data, reducing noise and highlighting underlying trends.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

Saha

2023

JDAIP

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Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.

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“…Investors and traders usually choose the wrong time to buy or sell Bitcoin because they simply take into account fluctuations in the price of other currencies and act instinctively. We will forecast the closing price of Bitcoin USD from January 1, 2021, to January 31, 2021, using a comparison of two approaches, the Simple Moving Average and RBFNN [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Detection of Skin Cancer using Deep Learning

Pangsibidang,

Tuba

2023

Fidelity

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Skin cancer is caused by the formation of abnormal cells that can assault or spread to numerous body sections. The skin cancer symptoms may include a mole with varying size, shape, and color, irregular edges, multiple hues, and sometimes, itching or bleeding. Exposure to the sun's UV radiation is attributed to more than 90 percent of known occurrences of Skin Cancer. In order to categorize cancer as malignant or benign, this study outlines the development of a classification system for skin cancer using deep learning. This system would use TensorFlow and Keras. The technique is used to classify skin cancer using the images from the data set that have been collected. After deployment, it was determined that the created Convolutional 2-D layer system was 78% accurate.

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Bitcoin USD Closing Price (BTC-USD) Comparison Using Simple Moving Average And Radial Basis Function Neural Network Methods

Cited by 6 publications

References 13 publications

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

Optimization Detection of Skin Cancer using Deep Learning

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