ADNAN, FADIL (2025) KOMPARASI PREDIKSI HARGA ETHEREUM MENGGUNAKAN ALGORITMA KNN DAN SVM. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The limited knowledge of the public about Ethereum upgrade events often leads to Fear of Missing Out (FOMO), where many people invest without understanding the risks and market volatility. The Ethereum upgrade is an event that reduces the block rewards for miners by half, which can affect the price and scarcity of Ethereum. This study aims to analyze the price change patterns of Ethereum before and after the upgrade and evaluate the performance of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms in predicting the impact of the event. Using a predictive approach, the research utilizes historical Ethereum data, including variables such as date, closing price, opening price, highest price, lowest price, volume, percentage change, and U.S. interest rates. The SVM and KNN algorithms are selected for their ability to handle complex and non-linear data. The model's performance is evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) to measure prediction accuracy. The study also examines the relationship between macroeconomic variables, such as interest rates, and Ethereum price fluctuations. The results are expected to provide insights into the strengths and weaknesses of each algorithm and help the public manage investment risks more wisely. Kata kunci: Ethereum, Ethereum Upgrade, FOMO, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Price Prediction, Market Volatility, Investment Risk, Mean Square Error (MSE), Root Mean Square Error (RMSE), Macroeconomic Variables, Interest Rates. Minimnya pengetahuan masyarakat tentang momen upgrade Ethereum sering menyebabkan terjadinya Fear of Missing Out (FOMO), di mana banyak orang berinvestasi tanpa memahami risiko dan volatilitas pasar. Upgrade Ethereum merupakan peristiwa yang mengurangi hadiah blok bagi penambang hingga setengahnya, yang dapat memengaruhi harga dan kelangkaan Ethereum. Penelitian ini bertujuan untuk menganalisis pola perubahan harga Ethereum sebelum dan sesudah upgrade serta mengevaluasi algoritma Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) dalam memprediksi dampak peristiwa tersebut. Melalui pendekatan prediktif, penelitian menggunakan data historis Ethereum, mencakup variabel seperti tanggal, harga terakhir, harga pembukaan, harga tertinggi, harga terendah, volume, perubahan persentase, dan suku bunga Amerika Serikat. Algoritma SVM dan KNN dipilih karena kemampuannya menangani data kompleks dan non-linear. Kinerja model dievaluasi menggunakan Mean Square Error (MSE) dan Root Mean Square Error (RMSE) untuk mengukur akurasi prediksi. Penelitian juga mengkaji hubungan antara variabel makroekonomi, seperti suku bunga, dengan fluktuasi harga Ethereum. Hasil diharapkan memberikan wawasan tentang keunggulan dan kelemahan masing-masing algoritma serta membantu masyarakat mengelola risiko investasi secara lebih bijak. Kata kunci: Ethereum, Upgrade Ethereum, FOMO, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Prediksi Harga, Volatilitas Pasar, Risiko Investasi, Mean Square Error (MSE), Root Mean Square Error (RMSE), Variabel Makroekonomi, Suku Bunga.
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