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All Journal Infotech: Journal of Technology Information Journal of Computer and Information Systems Ampera
Alexandra, Jennifer
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Computer Science & IT

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PERANCANGAN ARTIFICIAL INTELLIGENCE UNTUK KURIKULUM PEMBELAJARAN DI PERGURUAN TINGGI Alexandra, Jennifer; Budiyantara, Agus
Infotech: Journal of Technology Information Vol 8, No 1 (2022): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v8i1.128

Abstract

Information technology which is growing rapidly nowadays has become one of the main needs in all fields and aspects of life, whether in business, economy, or education. In the formal education world, the curriculum is very familiar, because the curriculum is considered the main point in achieving learning success. Because the curriculum have a goal to guide students, thus they can contribute positively to social life. Along with the development of times, the curriculum often undergoes changes due to digital transformation that makes a change in paradigm of education. Currently, in the world of formal education, the curriculum is only made based on learning materials with results that are in accordance with the majors taken and have not been adjusted to the job requirements in the Company. Meanwhile, for now, companies need more curriculum design that can meet the requirements set for the job position in the company. Because the basic curriculum is dynamic, a new way is needed to develop a curriculum with a design that can directly adapt to the job requirements of a company with the help of Artificial Intelligence technology. With the help of Artificial Intelligence technology, after students graduate, it is hoped that students will already have expertise in this field. With the development of the curriculum, it is also hoped that the level of quality of education can be improved.
NAV of Equity Fund Forecasting Using ARIMA and RNN Alexandra, jennifer
Journal of Computer and Information Systems Ampera Vol. 1 No. 01 (2025): Journal of Computer and Information Systems Ampera
Publisher : APTIKOM SUMSEL

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Abstract

This study aims to predict NAV of equity fund by comparing two commonly used methods, namely ARIMA and RNN. Both of these methods have their own characteristics which will be calculated based on their accuracy based on the MSE and RMSE values from the forecasting results. This study uses the CRIPS-DM framework as a reference, for the data used are Sucorinvest Equity Fund mutual fund data from 2017 to 2019. For ARIMA, the variable used is only NAV. For RNN, besides NAVs, stock indexes are also used that affect the value of NAV. Based on the results of the study, the ARIMA and RNN error rates are not too much different, but RNN is better. Keywords: Equity Fund, NAV, ARIMA, RNN, CRIPS-DM
Co-Authors Budiyantara, Agus