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All Journal International Journal of Cyber and IT Service Management (IJCITSM) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi
Harry Agustian
Seni Sejahtera Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

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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 (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.
Sharia-Guided Artificial Intelligence for Ethical Transformation in Modern Education Muchlishina Madani; Harry Agustian; Adam Faturahman; Asep Sutarman; Ramiro Santiago Ikhsan
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.975

Abstract

The rapid advancement of digital technologies has accelerated the integration of Artificial Intelligence (AI) into educational systems worldwide. Despite its benefits, concerns about ethics, algorithmic bias, transparency, and value alignment remain significant, particularly in faith-based educational institutions that emphasize moral and spiritual development. This study aims to propose a Sharia-Guided Artificial Intelligence (SGAI) model that supports ethical, accountable, and value-oriented transformation in modern education. The research employs a mixed-method approach by combining qualitative analysis of Islamic educational principles and ethical frameworks with quantitative evaluation of AI-supported governance practices in educational institutions. Data were collected from educators, administrators, and students within Islamic educational environments to assess the feasibility, acceptance, and integrity of AI implementation within a Sharia-compliant framework. The results indicate that AI systems guided by Sharia principles can improve curriculum personalization, administrative efficiency, transparency, and institutional accountability while reducing algorithmic bias and maintaining ethical values in educational processes. Furthermore, the proposed model encourages responsible data governance, ethical decision-making, and inclusive learning environments supported by intelligent technologies. The integration of intellectual, ethical, social, and spiritual dimensions also strengthens holistic educational development. In conclusion, embedding faith-based ethical intelligence into AI-driven educational management can foster sustainable, trustworthy, and socially responsible innovation while providing a practical framework for value-aligned AI adoption in education.
Co-Authors Adam Faturahman Adam Faturahman Asep Sutarman Efa Ayu Nabila Indra Wijaya Muchlishina Madani Ramiro Santiago Ikhsan Rosa Lesmana Sipah Audiah