Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Cyber and IT Service Management (IJCITSM) Startupreneur Business Digital (SABDA Journal)
Efa Ayu Nabila
CAI Sejahtera Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Enhancing Market Trend Analysis Through AI Forecasting Models Rosa Lesmana; Indra Wijaya; Efa Ayu Nabila; Harry Agustian; Sipah Audiah; Adam Faturahman
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.162

Abstract

Accurate market trend analysis is crucial for strategic decision making in industries, yet traditional forecasting models often struggle to provide reliable predictions in rapidly changing environments. This study investigates the application of advanced Artificial Intelligence (AI) models Long Short Term Memory (LSTM), Random Forest, Decision Trees, and Support Vector Machines (SVM) to improve the accuracy and robustness of market forecasting. Data was collected from sources like Bloomberg and Yahoo Finance, encompassing stock prices, economic indicators, and sector specific trends over five years. The models were evaluated using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess their predictive performance. Results show that AI models, especially LSTM, outperform traditional models like Auto Regressive Integrated Moving Average (ARIMA), offering superior handling of complex temporal dependencies and short term market fluctuations. For instance, LSTM achieved a MAPE of 1.8% and RMSE of 0.045, significantly improving forecast precision over ARIMA. Random Forest and Decision Trees also provided valuable insights into market drivers, adding interpretability to the forecasting process. This research highlights the potential of AI to enhance decision making by offering more accurate, data driven predictions and provides practical guidelines for implementing these models in real world market forecasting. Future research should explore hybrid AI approaches and broader datasets to further enhance forecasting adaptability across diverse market conditions.
Enhancing Market Trend Analysis Through AI Forecasting Models Rosa Lesmana; Indra Wijaya; Efa Ayu Nabila; Harry Agustian; Sipah Audiah; Adam Faturahman
International Journal of Cyber and IT Service Management Vol. 4 No. 2 (2024): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.162

Abstract

Accurate market trend analysis is crucial for strategic decision making in industries, yet traditional forecasting models often struggle to provide reliable predictions in rapidly changing environments. This study investigates the application of advanced Artificial Intelligence (AI) models Long Short Term Memory (LSTM), Random Forest, Decision Trees, and Support Vector Machines (SVM) to improve the accuracy and robustness of market forecasting. Data was collected from sources like Bloomberg and Yahoo Finance, encompassing stock prices, economic indicators, and sector specific trends over five years. The models were evaluated using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess their predictive performance. Results show that AI models, especially LSTM, outperform traditional models like Auto Regressive Integrated Moving Average (ARIMA), offering superior handling of complex temporal dependencies and short term market fluctuations. For instance, LSTM achieved a MAPE of 1.8% and RMSE of 0.045, significantly improving forecast precision over ARIMA. Random Forest and Decision Trees also provided valuable insights into market drivers, adding interpretability to the forecasting process. This research highlights the potential of AI to enhance decision making by offering more accurate, data driven predictions and provides practical guidelines for implementing these models in real world market forecasting. Future research should explore hybrid AI approaches and broader datasets to further enhance forecasting adaptability across diverse market conditions.
Digital First Banking Transformation and Sustainable Performance in the Evolving Financial Services Industry Yoyo Sudaryo; Deni Hamdani; Agung Lorenzo; Efa Ayu Nabila
Startupreneur Business Digital (SABDA Journal) Vol. 5 No. 2 (2026): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v5i2.1107

Abstract

The banking industry is undergoing a strategic shift toward digital-first and branchless operating models, driven by consumer demand for fast and flexible services, advances in financial technology, and the need to reduce physical branch-related costs. However, this transformation creates challenges related to cybersecurity, financial inclusion, and customer trust. This research aims to identify key factors driving digital transformation in banking and evaluate their implications for operational efficiency, customer experience, and business sustainability. The study adopts a literature-based sequential mixed-methods approach. First, quantitative analysis of secondary financial and operational data identifies patterns in operational efficiency, customer growth, and banking performance across traditional and digital-first banks. Second, qualitative evidence from academic literature, industry reports, case studies, and stakeholder perspectives is analyzed to interpret the organizational, technological, and strategic mechanisms underlying digital transformation. The findings indicate that digital-first banking improves operational efficiency, accelerates service delivery, enhances customer personalization, and supports customer growth. Nevertheless, cybersecurity risks, financial inclusion gaps, and reduced customer trust arising from limited face-to-face interactions remain significant challenges. Based on the integrated findings, an Integrated Digital-First Banking Transformation Framework (IDBTF) is proposed, incorporating Application Programming Interface (API) integration, data analytics, and organizational dynamic capabilities. The framework explains how digital-first banking can enhance operational efficiency, customer experience, and business sustainability while addressing cybersecurity, financial inclusion, and trust challenges, providing theoretical and practical guidance for sustainable digital transformation strategies.
Co-Authors Adam Faturahman Agung Lorenzo Deni Hamdani Harry Agustian Indra Wijaya Rosa Lesmana Sipah Audiah Yoyo Sudaryo