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All Journal PROSIDING SEMINAR NASIONAL Jurnal Sains dan Teknologi Scientific Journal of Informatics
Rahmat Gernowo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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HUBUNGAN FAKTOR PENERIMAAN APLIKASI UJIAN SEKOLAH BERBASIS KOMPUTER MENGGUNAKAN MODEL UTAUT Aris Puji Widodo; Rahmat Gernowo
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.397 KB)

Abstract

Information Technology (IT) implementation has been applied in various areas of life, including education. The field of education of any IT application is toorganize a computer-based school exam model. Computer-based school examsprincipally refer to all paper-based test processes, such as questioning, testexecution, and promising results. This paper discusses the relationship offactors that affect the acceptance of the use of computer-based school examapplications using Unified Theory of Acceptance and Use of Technology(UTAUT). Data analysis techniques using Structural Equation Model (SEM,) todetermine the relationship of UTAUT factors that affect the acceptance ofcomputer-based school exam applications.The number of respondents used asmuch as 132 taken from users of computer-based school exam applications. Theresult of descriptive analysis shows that Performance Expectancy (PE), SocialInfluence (SI) and Facilitating Condition (FC) have significant influence onBehavioral Intention variable, while Effort Expectancy (EE) variable hasinsignificant influence. Keywords: UTAUT, SEM, Acceptance, Exam, and School,
Adaptation of Information Systems Strategic Planning of Universities Using COBIT 2019 in Post Covid-19 Harits, Abdurrahman; Rahmat Gernowo; Djatmiko Endro Suseno
JST (Jurnal Sains dan Teknologi) Vol. 11 No. 2 (2022)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (458.694 KB) | DOI: 10.23887/jstundiksha.v11i2.48365

Abstract

COVID-19 has changed universities in managing processes and services with more relevant systems. This is a challenge for universities to design strategies both during and after this pandemic ends. This study aims to apply the methodology and design principles of COBIT 2019 to adapt the operational model and strategic planning of higher education institutions in the post-pandemic period. The COVID-19 challenge has shown that digital information systems are a critical factor in ensuring robust business processes. This study uses Syarif Hidayatullah State Islamic University Jakarta as a case study, the results of calculation of institutional capability level are currently in the range of level 3 with desired capability level target being level 4, this means that governance in the agency has been carried out according to plan but is not ready to implement fully used after the pandemic. This research produces several that are useful as the best adaptation in the New Normal era.
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 Aris Puji Widodo Djatmiko Endro Suseno Harits, Abdurrahman Indra Waspada Pramudya, Naufal Daffa