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Your search keyword '"Nopianti, Rina"' showing total 17 results
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17 results on '"Nopianti, Rina"'

1. A Study of Accounting Using Bibliometric Methods in the Age of Blockchain

Author

Nopianti, Rina, Ismail, Tubagus, Hanifah, Imam Abu, Mulyasari, Windu, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Suryanto, Tulus, editor, Sulejmani, Liza Alili, editor, Ali, Juhary, editor, Rena, Ravinder, editor, Grima, Simon, editor, and Piryu, Ramona, editor

Published
2023
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2. A Conceptual Framework for Sustainable Business Growth in Small and Medium Enterprises.

Author

Nopianti, Rina, Ismail, Tubagus, Hanifah, Imam Abu, and Mulyasari, Windu

Subjects
SMALL business, BUSINESS expansion, CORPORATE sustainability, DISRUPTIVE innovations, PHENOMENOLOGY
Abstract

This study aims to enhance the existing conceptual framework in order to assist SMEs in attaining sustainable business growth by enhancing their performance in a sustainable manner. This study entails a systematic analysis of the existing body of literature pertaining to small and medium-sized enterprises (SMEs). The 105 research papers included in the analysis have been categorized based on their geographical distribution, paper type, major theme, and theoretical underpinnings. Subsequently, a thorough thematic analysis was conducted. This study introduces a conceptual framework that facilitates the development of sustainable business growth, thereby assisting SMEs in mitigating the impact of uncertain occurrences or shocks. SMEs that actively engage in the adoption of sustainable business growth have the potential to create innovative pathways for business growth and gain a competitive edge. The literature highlights the unexpected and unanticipated challenges experienced by SMEs, and the subsequent development of methods to address these severe disruptions. [ABSTRACT FROM AUTHOR]

Published
2024
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8. LSTM ALGORITHM ANALYSIS OF BANKING SECTOR STOCK PRICE PREDICTIONS

Author

Mutmainah, Umi Marfuah, and Nopianti, Rina

Subjects
LSTM Algorithm Stock Price Analysis Prediction Machine Learning
Abstract

Investing, buying or selling on the stock exchange demands data analytical expertise and skill. Because the stock market is so dynamic, it takes data modelling to predict stock prices accurately. Machine learning can currently process and forecast data with high accuracy. We proposed using the Long-Short Term Memory (LSTM) algorithm to model data to anticipate market prices. This study's primary goal is to assess the machine learning algorithm's accuracy in forecasting stock price data and the optimal model construction epochs. The RMSE value of the LSTM method and the data model obtained the variation of the epochs value.

Published
2022
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14. Stock Price Index Prediction Using Adaptive Neural Fuzzy Inference System.

Author

Nopianti, Rina, Panudju, Andreas Tri, and Permana, Angrian

Subjects
STOCK price indexes, FUZZY logic, STOCK prices, FUZZY systems, ARTIFICIAL neural networks, ECONOMIC research
Abstract

This paper aims to predict stock prices using open, high, low, close variables using artificial neural networks, especially the adaptive fuzzy neural inference system (ANFIS). Each stock has a different pattern and can be predicted if you have complete data. This study is limited by stock data for 2012-2019. The survey was conducted to collect stock data from the Yahoo Finance website. The stock data used is data from 2001-2018. Learning patterns of data patterns using the Adaptive Neural Fuzzy Inference System (ANFIS) were compared with regression analysis, Mean Square Error (MSE) and Mean Prediction Error. The results show that stock price predictions using the Adaptive Neural Fuzzy Inference System (ANFIS) have a small error rate (below 1 percent). The stock price at closing is determined by the open price and the volume of the stock. The value of the highest price of the stock and the lowest value of the stock follows the determined value of the opening price. This paper contributes to existing research in economics, especially stock investment and Financial Technology. [ABSTRACT FROM AUTHOR]

Published
2021
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