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Journal of Applied Engineering and Technological Science (JAETS)
Published by Yayasan Pendidikan Riset dan Pengembangan Intelektual
ISSN : 27156087     EISSN : 27156079     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Journal of Applied Engineering and Technological Science (JAETS) is published by Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI), Pekanbaru, Indonesia. It is academic, online, open access, peer reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. Journal of Applied Engineering and Technological Science (JAETS) is published annually 2 times every June and Desember.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 405 Documents
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Why Digital Capabilities Alone Don’t Drive SME Performance: The Mediating Role of Process Innovation in Retail SMEs Nurul Binti Samsuden; Alhamzah F. Abbas; Nor Amira Syairah Zulkarnain; Wai Chuen Poon; Ummu Ajrah Abdul Rauf
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/6x82ry42

Abstract

This study explores the role of digital capabilities (DC) in enhancing the business performance (BP) of retail SMEs in Johor, Malaysia, with a particular focus on the mediating effect of process innovation (PI) and the moderating role of competition intensity (CI). Despite the rising relevance of digital transformation, retail SMEs in Johor, Malaysia, continue to face structural constraints, including limited technological readiness, skill shortages, and intensified competitive pressures. Using primary data from 371 retail SMEs, this study employs Partial Least Squares Structural Equation Modelling (PLS-SEM) to test a multidimensional model grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT). The findings reveal that while DC do not exert a direct influence on BP, they significantly enhance PI, which in turn fully mediates the impact on performance. CI does not significantly moderate the relationship between DC and business outcomes. These results suggest that the BP benefits of DC are only realised through innovation-enabling mechanisms rather than direct technological adoption. The study underscores the importance of investing in internal innovation processes and strategic integration to translate digital potential into sustained competitive advantage within the retail SME context.
Adoption of Berastagi Verse in Mobile Metaverse Learning: Extending TAM With Immersion and Cultural Value Rian Farta Wijaya; Randi Rian Putra
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.10584

Abstract

This study examines user acceptance of Berastagi Verse, a Roblox-based mobile metaverse learning platform that reconstructs tourism destinations in Berastagi, Indonesia, for cultural learning. The study extends the Technology Acceptance Model by adding immersive experience and cultural value as predictors of behavioral intention. A purposive sample of 180 respondents who used the platform on smartphones completed a questionnaire after interacting with the system. Data were analyzed using PLS-SEM. Perceived ease of use positively influenced perceived usefulness, and both constructs positively predicted behavioral intention. Immersive experience and cultural value also had significant direct effects on intention. The model explained 64.2% of the variance in behavioral intention, suggesting meaningful explanatory power for this context. The findings indicate that mobile metaverse learning is adopted not only for usability and usefulness, but also for the extent to which it feels immersive and culturally relevant. For educational designers, the results suggest that mobile learning platforms should combine intuitive interaction, clear pedagogical value, and culturally authentic content when seeking user adoption.
Current State of Innovation and Management Control, Ready Made Garments Industry Towards Circular Economy Mohammed Masum Billah; Mohd Helmi Ali; Syed Shah Alam; Mazzlida Mat Deli
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.10593

Abstract

This study investigates the impact of innovative strategies and management control systems (MCS) on the implementation of circular economy (CE) practices within Bangladesh’s ready-made garment (RMG) sector. Utilizing the Resource-Based View (RBV), Natural Resource-Based View (NRBV), and Stakeholder Resource-Based View (SRBV), the study constructs a comprehensive framework that differentiates between internal innovation capabilities and externally acquired knowledge. Data were gathered from 142 management-level professionals using a standardized questionnaire and analyzed using PLS-SEM. The results indicate that open innovation and management control systems significantly enhance CE adoption, but green product and process innovation have favorable yet statistically negligible correlations. Moreover, MCS does not substantially influence the link between innovation and CE. The results indicate that, in buyer-driven and compliance-intensive sectors, the adoption of a circular economy is predominantly influenced by institutional factors rather than by organizational capabilities. The research enhances theoretical understanding by illustrating the contextual constraints of NRBV in low-autonomy supply chain settings and emphasizing the preeminent influence of external collaboration and compliance-driven governance. The findings indicate that organizations ought to prioritize strategic relationships and organized control systems rather than solitary internal innovation efforts to expedite circular transformation.
Operational AI Opportunity Formation in Small Industry: A TOE-DOI Explanation Ikhwan Arief; Prima Fithri; Angelina Ajeng Masayu; A Aisyah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.10664

Abstract

Artificial intelligence (AI) is increasingly accessible to small firms, but prior studies mainly explain readiness, adoption, or downstream value rather than the stage at which AI first becomes visible as an operational opportunity. This study examines that upstream stage in small industrial firms by combining technology-organization-environment (TOE) antecedents with diffusion of innovation (DOI) outcomes. A purposive, census-oriented survey was conducted on the official West Sumatra small-industry frame. The final dataset contains 51 usable responses from 69 registered small industrial firms, equal to 73.91% official-frame coverage, and was analyzed with PLS-SEM in SmartPLS 4. A conservative rerun removed one weak infrastructure indicator while retaining the same inner model. The main supported path is human resources and digital literacy to observability (β = 0.609, p = 0.001, f² = 0.318), followed by governance, strategy, and budget support to observability (β = 0.447, p = 0.030, f² = 0.179). Observability reaches R² = 0.496, whereas infrastructure, data management, competitive pressure, and ecosystem support do not show comparable direct effects. The findings indicate that early AI opportunity becomes visible mainly through interpretive workforce capacity and bounded managerial support.
Early Detection of Foetal Pathological Conditions with Neural Network Method: Implementation of Backpropagation Neural Network and SMOTE on Cardiotocography Data Elin Haerani; Fadhilah Syafria; Novriyanto Novriyanto; Ismail Marzuki
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/3n6z5n26

Abstract

This research focuses on the development of an effective classification model for early detection of foetal pathological conditions using Cardiotocography (CTG) data by utilising the Backpropagation Neural Network (BPNN) method. The high maternal mortality rate (MMR) and infant mortality rate (IMR) in Indonesia, including Riau Province, emphasise the importance of accurate prenatal diagnosis. The main challenge of this research is to address the class imbalance issue in the CTG dataset, which is biased towards the Normal class (77.9%) compared to the Suspect (13.9%) and Pathological (8.2%) classes. This problem was addressed by applying the Synthetic Minority Oversampling Technique (SMOTE). The model's performance was evaluated using K-Fold Cross Validation (5-Fold and 10-Fold). The test results showed that the combination of BPNN and SMOTE significantly improved performance, achieving a highest average accuracy of 92.66% and a maximum accuracy of 94.84% in the 10-Fold Cross Validation scheme. The resulting model is stable, has a high generalisation capability, and has great potential to be integrated into an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) to support evidence-based health policies in reducing Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR).

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