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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Aisyi, Nasywa Rihadatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction Putri, Amanda Iksanul; Husna, Nur Alfa; Cia, Neha Mella; Arba, Muhammad Abdillah; Aisyi, Nasywa Rihadatul; Pramesthi, Chintya Harum; Irdayusman, Abidaharbya Salsa
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1110

Abstract

An abnormal or excessive build-up of fat that can negatively impact one's health as a result of an imbalance in energy between calories consumed and burnt is known as obesity. The majority of ailments, such as diabetes, heart disease, cancer, osteoarthritis, chronic renal disease, stroke, hypertension, and other fatal conditions, are linked to obesity. Information technology has therefore been the subject of several studies aimed at diagnosing and treating obesity. Because there is a wealth of information on obesity, data mining techniques such as the K-Nearest Neighbors (K-NN) algorithm, Naïve Bayes Classifier, Support Vector Machine (SVM), and Decision Tree can be used to classify the data. The 2111 records and 17 characteristics of obesity data that were received from Kaggle will be used in this study. The four algorithms are to be compared in this study. In other words, using the dataset used in this study, the Decision Tree algorithm's accuracy outperforms that of the other three algorithms K-NN, Naïve Bayes, and SVM. Using the Decision Tree algorithm, the accuracy was 84.98%; the K-NN algorithm came in second with an accuracy value of 83.55%; the Naïve Bayes algorithm came in third with an accuracy rate of 77.48%; and the SVM algorithm came in last with the lowest accuracy value in this study, at 77.32%.
Implementation of Gated Recurrent Unit, Long Short-Term Memory and Derivatives for Gold Price Prediction Putri, Amanda Iksanul; Syarif, Yulia; Aisyi, Nasywa Rihadatul; Waeyusoh, Nuralisa
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1609

Abstract

Gold is a precious metal with high resale value, often considered a safe investment as its price typically rises with inflation, attracting investors. However, even slight changes in gold prices can have significant impacts. To build an accurate forecasting model, this study applies and compares Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms on global gold prices. GRU and LSTM are recurrent neural networks designed to capture patterns in sequential data, where GRU uses a simplified gating mechanism to retain essential information, and LSTM, with its more complex gates, helps manage long-term dependencies in data. Bi-GRU and Bi-LSTM process data bidirectionally, capturing context from both past and future sequences for better prediction accuracy. This research uses data from Yahoo Finance (01-01-2014 to 12-06-2024) and experiments with optimization techniques (Adam, AdamW, Adamax, and Nadam), batch sizes (8, 16, and 32), time steps (10, 20, and 30), and a learning rate of 0.0001, trained for 1000 epochs with checkpoints and early stopping. Bi-GRU with Nadam, batch size 8, and 20 time steps proved most effective, with MSE of 4.1153, RMSE of 2.0286, MAE of 1.5881, and MAPE of 0.8857%. Forecasts using this model predict a 20-day decline in gold prices.
Co-Authors Arba, Muhammad Abdillah Cia, Neha Mella Husna, Nur Alfa Irdayusman, Abidaharbya Salsa Pramesthi, Chintya Harum Putri, Amanda Iksanul Syarif, Yulia Waeyusoh, Nuralisa