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All Journal International Journal of Research and Applied Technology (INJURATECH) Indonesian Journal of Statistics and Its Applications
Prabowo, H
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics Other

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Digital Transformation in Higher Education Soegoto, Yudistira; Meyliana, M; Prabowo, H; Hidayanto, A Nizar; Trisetyarso, A; Pradipto, Y Dedy
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v3i2.14196

Abstract

At this time, in the pandemic season of Covid-19, digital transformation is developing twice as fast; as usual, the organizations or institutions that are most demanded to adapt to this pandemic are universities. The purpose of this research is to identify the development of digital transformation that occurs in universities from year to year and to identify the development of digital transformations that occur based on areas in higher education using the core concepts of digital transformation and By using the systematic literature review (SLR) method, The results of this study are expected to provide insight into the development of digital transformation that occurs in higher education
Forecasting Currency in East Java: Classical Time Series vs. Machine Learning Putri, J A; Suhartono, Suhartono; Prabowo, H; Salehah, N A; Prastyo, D D; Setiawan, Setiawan
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p284-303

Abstract

Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.
Co-Authors Hidayanto, A Nizar Meyliana, M Pradipto, Y Dedy Prastyo, D D Putri, J A Salehah, N A Setiawan Setiawan Soegoto, Yudistira Suhartono Trisetyarso, A