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All Journal Horizon: Indonesian Journal of Multidisciplinary
Rachman, Akmal Kherudin
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Agriculture, Biological Sciences & Forestry Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Investasi di Era AI: Analisis Tren Harga Cryptocurrency Berbasis Kecerdasan Buatan (AI) dan Korelasi dengan Saham NVIDIA Rachman, Akmal Kherudin; Triana, N. Neni; Nadaek, Thomas
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 4 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.7315

Abstract

The advancement of artificial intelligence (AI) has driven growth in the technology sector and the emergence of AI-based crypto assets, making the relationships between these assets a subject worthy of study. This research analyzes the price relationships between Nvidia (NVDA) stock and Bitcoin (BTC), Bittensor (TAO), NEAR Protocol (NEAR), and Internet Computer (ICP), while comparing the performance of Prophet and Long Short-Term Memory (LSTM) models. A quantitative approach utilizing time-series analysis was employed. Daily closing price and volume data were obtained from CoinMarketCap for the crypto assets and Investing.com for NVDA, covering the period from January 16, 2025, to February 18, 2026—comprising 273 observations aligned with NVDA's trading days. The analysis encompassed descriptive statistics, Pearson correlation, the Augmented Dickey-Fuller (ADF) stationarity test, Prophet and LSTM modeling, and Mean Absolute Percentage Error (MAPE) evaluation. The results reveal varying strengths in inter-asset relationships and differing stationarity characteristics across the price series. LSTM tended to project price increases for the crypto assets and a decline for NVDA, whereas Prophet yielded more conservative projections. Based on MAPE, Prophet exhibited an average prediction error 12.9% lower than that of LSTM, indicating superior predictive performance during the study period.
Co-Authors Nadaek, Thomas Triana, N. Neni