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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Fudholi, Dzikri Rahadian
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Computer Science & IT Control & Systems Engineering

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Analysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests Kumoro, Rabbani Nur; Fattima, Audrey Shafira; Susatyo, William Hilmy; Fudholi, Dzikri Rahadian
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90202

Abstract

Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.
The Application of LSTM in the AI-Based Enhancement of Classical Compositions Fudholi, Dzikri Rahadian; Putri, Delfia Nur Anrianti; Wijaya, Raden Bagus Muhammad AdryanPutra Adhy; Kusnadi, Jonathan Edmund; Amarissa, Jovinca Claudia
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1628

Abstract

Music enhancement through deep learning methodologies presents an innovative approach to refining and augmenting classical compositions. Leveraging a comprehensive dataset of classical piano MIDI files, this study employs LSTM networks with attention mechanisms for music refinement. The model, trained on diverse compositions, demonstrates proficiency in capturing tempo nuances but faces challenges in replicating varied pitch patterns. Assessments by 28 individuals reveal positive reception, particularly in melody integration, scoring notably high at 8 out of 10. However, while praised for cohesion, bass lines received slightly lower scores, suggesting opportunities for enhancing originality and impact. These findings underscore the LSTM model's capability to generate harmonious melodies and highlight refinement areas, particularly in innovating bass lines within classical compositions. This study contributes to advancing automated music refinement, guiding further developments in LSTM-based music generation techniques.
Co-Authors Amarissa, Jovinca Claudia Fattima, Audrey Shafira Kumoro, Rabbani Nur Kusnadi, Jonathan Edmund Putri, Delfia Nur Anrianti Susatyo, William Hilmy Wijaya, Raden Bagus Muhammad AdryanPutra Adhy