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All Journal Jurnal Simetris BIOEDUSAINS: Jurnal Pendidikan Biologi dan Sains Majalah Ilmiah Teknologi Elektro
Feddy Setio Pribadi
Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy

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Review on Impact of Artificial Intelligent on Efficiency and Productivity in Industrial Automation Ega Nur Fawwaz; Lita Dwi Setianingsih; Satria Krisna Prabantara; Fatahillah Nabil Fawwaz; Dwi Alvin Hidayat; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Majalah Ilmiah Teknologi Elektro Vol 24 No 1 (2025): ( Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Study Program of Magister Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.205.v24i01.P09

Abstract

As Industry 4.0 technologies evolve, the application of Artificial Intelligence (AI) in the manufacturing sector has become a major factor in improving operational efficiency, optimizing production processes, and reducing costs, enabling predictive analytics, data-driven maintenance, and automation of tasks that previously required human intervention. This study conducts a systematic literature review (SLR) on various AI methods applied in industrial automation, evaluates the effectiveness of their implementation, and identifies key challenges in their adoption. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population,   Intervention, Comparison, Outcome, Context (PICOC) approaches are adopted. The sources used to search the literature included four electronic databases, comprising ScienceDirect, Taylor & Francis, Scopus, and Emerald Insight, resulting in 33 selected articles. The result shows that AI contributes significantly to improving production efficiency, but it still faces challenges in system integration, implementation costs, and workforce readiness. This study provides a comprehensive overview of the effectiveness of AI implementation in industrial automation and the challenges that need to be overcome to optimize competitiveness and production efficiency
Penggunaan Metode Random Forest, Support Vector Machine dan Artificial Neural Networks dalam Prediksi Suhu Udara di Balikpapan Erika Meinofelia; Mochamad Aryono Adhi; Achmad Fahruddin Rais; Djunaidi Djunaidi; Feddy Setio Pribadi
BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains Vol. 8 No. 6 (2025): BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/9s0xxr05

Abstract

This study aimed to compare the performance of three algorithmic models, namely Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN), in predicting air temperature in Balikpapan. Changes in air temperature influenced by various climatic and geographical factors present a major challenge in urban planning; thus, accurate predictions are crucial to support sustainable and climate-adaptive city planning. The dataset used consists of observational data from the Balikpapan Meteorological Station, BMKG, over ten years, from January 2014 to December 2024. The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R Squared (R²) metrics. The results show that the SVM method produced an MAE of 0.17, RMSE of 0.21, and R² of 0.95, providing better predictions than ANN and Random Forest. In conclusion, SVM is an effective method for air temperature prediction in Balikpapan. Keywords: Artificial Neural Networks, Random Forest, Support Vector Machine, Machine Learning, Air Temperature Prediction
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.
Co-Authors Achmad Fahruddin Rais Djunaidi Djunaidi Dwi Alvin Hidayat Ega Nur Fawwaz Erika Meinofelia Fatahillah Nabil Fawwaz Lita Dwi Setianingsih Mochamad Aryono Adhi Mu'alfi Fahrul Fanani Sachroni Nazli Rahmeisi Rizky Ajie Aprilianto Satria Krisna Prabantara Yehezkiel Nesta Andyanto