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This Author published in this journals
All Journal Engineering and Technology International Journal (EATIJ) JADI (Jurnal Teknik Industri)
Fathan Dewadi
Politeknik Negeri Jakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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 1 
Design, Development, and Performance Evaluation of a Closed-System Batik Fabric Drying Machine for Small-Scale Industry Applications Arifia Ekayuliana; Fathan Dewadi; Nabila Yudisha; Ibnu Rosid; Al Fauzi; Muhamad Purdiatama; Adinda Ludwika; Ahmad Royan; Muhammad Nurcholis
Engineering and Technology International Journal Vol 7 No 03 (2025): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v7i03.1156

Abstract

This study presents the design and performance evaluation of a closed-system batik fabric drying machine for small-scale industries. The system integrates a spinner for initial dewatering and a hot-air blower with thermostatic control at 60 °C. Experimental tests examined drying time, temperature stability, and fabric quality. Results showed that 2 kg of batik fabric dried in 18 minutes—about 70 % faster than traditional sun drying. The closed-loop air circulation improved thermal uniformity (±1.5 °C deviation) and reduced energy use by 25 %. No color fading or fiber damage occurred, and ultraviolet lamps prevented microbial growth. This design demonstrates that combining mechanical efficiency, thermal control, and ergonomics can enhance energy efficiency and production reliability for micro-scale batik industries.
Digital Twin in Smart Manufacturing: A Systematic Literature Review on Predictive Decision-Making, Industrial Sustainability, and Process Optimization Fathan Dewadi; Ahmad Royan; Muhammad Nurcholis; Muhammad Pratama
JADI (Jurnal Teknik Industri) Vol. 2 No. 1 (2026): Smart Manufacturing
Publisher : CV. Indie Press Edutaste

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66865/bdb53m45

Abstract

The rapid advancement of Industry 4.0 has accelerated the adoption of Digital Twin (DT) technology in smart manufacturing systems. DT enables virtual replication of physical assets, supporting real-time monitoring, predictive analytics, simulation, and data-driven decision-making. This systematic literature review investigates the role of Digital Twin technology in predictive decision-making, process optimization, and industrial sustainability within smart manufacturing environments. The review synthesizes recent peer-reviewed studies from Scopus, Web of Science, and IEEE Xplore databases. Findings show that DT enhances operational efficiency through integration with machine learning, cyber-physical systems, and Industrial Internet of Things (IIoT), enabling improved maintenance strategies, energy efficiency, and production optimization. In addition, DT contributes to sustainability by reducing waste generation and supporting circular manufacturing practices. Despite its advantages, implementation challenges remain, including high deployment costs, interoperability issues, cybersecurity risks, and lack of data standardization. Overall, the study concludes that Digital Twin is a key enabling technology for future smart factories, particularly when integrated with artificial intelligence, edge computing, and cloud-based manufacturing systems.
Data-Driven Innovation in Smart Manufacturing:A Systematic Review of Predictive Decision-Making and Industrial Optimization Adinda Ludwika; Fathan Dewadi; Fadli Robbi; Abel Sulaiman
JADI (Jurnal Teknik Industri) Vol. 2 No. 1 (2026): Smart Manufacturing
Publisher : CV. Indie Press Edutaste

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66865/cz7nsk48

Abstract

Industry 5.0 has accelerated the transformation of manufacturing industries toward autonomous, intelligent, and human-centric production systems. Smart manufacturing technologies increasingly integrate Artificial Intelligence (AI), Internet of Things (IoT), Digital Twin, and Cyber-Physical Systems (CPS) to support predictive decision-making and industrial optimization. This study aims to analyze the development of autonomous smart manufacturing systems through a systematic literature review approach. The review process followed the PRISMA methodology using publications indexed in Scopus, Web of Science, and Google Scholar from 2016–2026. The results indicate that AI-driven manufacturing systems improve predictive maintenance, operational efficiency, production flexibility, and industrial sustainability. However, challenges related to cybersecurity, infrastructure readiness, implementation costs, and ethical AI governance remain significant barriers. This study concludes that autonomous smart manufacturing systems possess strong potential to support intelligent and sustainable industrial transformation in the Industry 5.0 era.
Autonomous Smart Manufacturing Systems in the Era of Industry 5.0: A Systematic Review of AI-Driven Decision Intelligence and Industrial Optimization Ade Suhara; Dibyo Setiawan; Fathan Dewadi; Fisika Putra; Gunawan Musyaffa; Surotun Nabawiyah
JADI (Jurnal Teknik Industri) Vol. 2 No. 1 (2026): Smart Manufacturing
Publisher : CV. Indie Press Edutaste

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66865/xv9yr868

Abstract

The emergence of Industry 5.0 has accelerated the transformation of manufacturing industries toward autonomous, intelligent, and human-centric production systems. Smart manufacturing technologies increasingly integrate Artificial Intelligence (AI), Internet of Things (IoT), Digital Twin, and Cyber-Physical Systems (CPS) to support predictive decision-making and industrial optimization. This study aims to analyze the development of autonomous smart manufacturing systems through a systematic literature review approach. The review process followed the PRISMA methodology using publications indexed in Scopus, Web of Science, and Google Scholar from 2016–2026. The results indicate that AI-driven manufacturing systems significantly improve predictive maintenance, operational efficiency, production flexibility, and industrial sustainability. However, challenges related to cybersecurity, infrastructure readiness, implementation costs, and ethical AI governance remain significant barriers. This study concludes that autonomous smart manufacturing systems possess strong potential to support intelligent and sustainable industrial transformation in the Industry 5.0 era.
Generative AI and Data-Driven Innovation in Smart Manufacturing: A Systematic Literature Review of Intelligent Industrial Transformation Ibnu Rosid; Fathan Dewadi; Friscilia Simanullang; Mochamad Pangestu
JADI (Jurnal Teknik Industri) Vol. 2 No. 1 (2026): Smart Manufacturing
Publisher : CV. Indie Press Edutaste

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66865/jkyhm884

Abstract

The rapid advancement of Industry 4.0 technologies has accelerated the transformation of manufacturing industries toward intelligent and data-driven systems. Smart manufacturing integrates technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, and Digital Twin to improve predictive decision-making and industrial optimization. This study aims to analyze the development of data-driven innovation in smart manufacturing through a systematic literature review approach. Data were collected from Scopus, Web of Science, and Google Scholar databases covering publications from 2015–2026. The review process followed the PRISMA methodology for systematic article selection and analysis. The results indicate that major research themes include predictive analytics, AI-based decision support systems, industrial optimization, digital twin implementation, and smart factory integration. The study also reveals increasing adoption of machine learning and industrial analytics to improve manufacturing efficiency, sustainability, and operational resilience. However, challenges related to cybersecurity, data integration, infrastructure readiness, and implementation costs remain significant barriers. This study concludes that data-driven innovation possesses substantial potential to support intelligent industrial transformation and sustainable manufacturing optimization.
Co-Authors Abel Sulaiman Ade Suhara Adinda Ludwika Ahmad Royan Al Fauzi Arifia Ekayuliana Fadli Robbi Fisika Putra Friscilia Simanullang Gunawan Musyaffa Ibnu Rosid Mochamad Pangestu Muhamad Purdiatama Muhammad Nurcholis Muhammad Pratama Nabila Yudisha setiawan, Dibyo Surotun Nabawiyah