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All Journal Jurnal Transformatika Scientific Journal of Informatics
Pramudya, Naufal Daffa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Comparative Study of Information System Governance Frameworks: Foundations for IT Risk Management Using COBIT 2019 and ITIL Sholeh, Moch. Badrus; Pramudya, Naufal Daffa
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/fh0vee39

Abstract

In this study, COBIT 2019 and ITIL V4 are compared in the context of managing IT risk. Through systematic literature review (SLR), the theoretical and practical foundations of both frameworks are evaluated. COBIT 2019 offers a structured approach, while ITIL emphasizes adaptive operational practices. Analysis of strengths and weaknesses helps organizations choose an approach that aligns with their strategic objectives. With this understanding, organizations can enhance their ability to manage IT risks and achieve business goals effectively.
Rainfall Prediction at Ahmad Yani Meteorological StationUsing Integration ARIMA and LSTM Pramudya, Naufal Daffa; Rahmat Gernowo; Indra Waspada
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.39297

Abstract

Purpose: Predicting rainfall using ARIMA, LSTM, and Hybrid ARIMA-LSTM models to obtain accuracy values ​​on data at the Ahmad Yani Semarang station. Methods: This study implements the ARIMA, LSTM, and hybrid ARIMA-LSTM models to determine which of these models produces the most significant predictions using rainfall data at the Ahmad Yani Meteorological Station in Semarang. This method proves whether using the hybrid ARIMA-LSTM, which is a combination of the two models, is able to provide greater accuracy compared to the ARIMA/LSTM model. The results of these predictions can certainly help relevant stakeholders to improve rainfall accuracy, especially at the Ahmad Yani Meteorological Station. Result: By utilizing the power of statistical models (ARIMA) with deep learning (LSTM), the results of these two models provide higher accuracy compared to each model, as seen from the accuracy of the best ARIMA model using RMSE 15.8 and MAE 8.7, the best LSTM model RMSE 14.65 and MAE 9.06, while in the HYBRID ARIMA-LSTM model the best RMSE is 14.1 and MAE 9.06. Novelty: This research adds to the knowledge regarding the accuracy or combination of ARIMA and LSTM models which are rarely used, especially in the world of meteorology or rainfall. By utilizing the ARIMA model which is able to read linear patterns and the LSTM model which reads non-linear patterns, the accuracy of rainfall increases and can help related stakeholders.
Co-Authors Indra Waspada Rahmat Gernowo Sholeh, Moch. Badrus