Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Jurnal Simetris IJIE (Indonesian Journal of Informatics Education) IJIES (International Journal of Innovation in Enterprise System)
Nazli Rahmeisi
Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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The Role of Big Data Analytics and Artificial Intelligence in Business Strategy: A Systematic Review Nazli Rahmeisi; Mu'alfi Fahrul Fanani Sachroni; Yehezkiel Nesta Andyanto; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.14909

Abstract

The accelerating digital transformation across industries has intensified the need for datadriven approaches in strategic business management. This study conducts a Systematic Literature Review (SLR) to examine how Big Data Analytics (BDA) and Artificial Intelligence (AI) influence business strategy formulation, risk management, and organizational competitiveness. Guided by the PICOC framework and PRISMA 2020 protocol, 27 peer-reviewed journal articles published between 2020 and 2024 were analyzed through thematic synthesis and bibliometric visualization using VOSviewer. The results indicate that BDA and AI enhance strategic decision-making, operational efficiency, and risk mitigation through predictive insights and real-time analytics. However, their strategic integration remains limited due to socio-technical challenges such as inadequate analytical capability, weak data governance, and organizational resistance. The review highlights that the true strategic value of BDA and AI emerges when these technologies are embedded within long-term strategic planning, data governance, and sustainability frameworks, rather than treated merely as operational tools. This study contributes to strategic management literature by synthesizing cross-sectoral evidence and offering insights into how data-driven intelligence fosters long-term competitiveness and sustainable business transformation.
Real-Time Emotion Recognition in Online Learning Using Google Teachable Nazli Rahmeisi
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.110565

Abstract

Understanding learners’ emotional engagement in e-learning environments remains challenging due to the limited availability of non-verbal cues, despite its importance for motivation and participation. This paper proposes a facial emotion recognition approach using Google's Teachable Machine to support real-time emotion detection within online learning environments. The system analyzes facial expressions captured through a standard webcam to classify four basic emotional states: happy, sad, neutral, and angry. An experimental design was employed using simulated emotional expressions collected under controlled conditions, including adequate lighting and front-facing facial images. The results indicate that the system can provide instructors with additional affective cues to support formative assessment and instructional awareness in synchronous online learning. The proposed approach emphasizes practical instructional feasibility and accessibility compared to more complex emotion recognition models, as it does not require specialized hardware or advanced programming skills.
Random Forest-Based AI Decision Support System for Palm Oil Production Planning Nazli Rahmeisi
IJIES (International Journal of Innovation in Enterprise System) Vol 10 No 2 (2026): International Journal of Innovation in Enterprise System - Article in Press
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v10i02.10771

Abstract

Palm oil milling operations are frequently affected by fluctuating fresh fruit bunch (FFB) availability, variations in Oil Extraction Rate (OER), and limitations in production planning under dynamic operational and environmental conditions. In many mills, production decisions still rely heavily on operator experience and manually recorded information, resulting in inconsistent and less structured planning processes. This study develops an interpretable Artificial Intelligence-based Decision Support System (AI-DSS) that integrates a Random Forest Regression model into a web-based decision support platform to support enterprise-level palm oil production planning. The predictive model was developed using operational and environmental data collected from a palm oil mill and evaluated through 10-fold cross-validation. The proposed Random Forest Regression model achieved an average coefficient of determination (R²) of 0.94 ± 0.02 and an average Root Mean Square Error (RMSE) of 1.06 ± 0.09, demonstrating strong predictive capability for modeling nonlinear relationships among production variables. The trained Random Forest model was integrated into a web-based decision support framework that transforms predicted OER values into structured operational recommendations while preserving managerial decision-making. Feature importance analysis further enhances system interpretability by identifying Fresh Fruit Bunch (FFB) intake and operational hours as the most influential variables affecting OER. The proposed AI-DSS demonstrates the practical feasibility of integrating predictive analytics, explainable machine learning, and enterprise-level decision support to facilitate more structured, transparent, and data-driven production planning in palm oil mill operations.
Co-Authors Feddy Setio Pribadi Mu'alfi Fahrul Fanani Sachroni Rizky Ajie Aprilianto Yehezkiel Nesta Andyanto