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All Journal ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) International Transactions on Education Technology (ITEE) JOT
Mohd Faiz Hilmi
Universiti Sains Malaysia
Humanities Control & Systems Engineering Social Sciences

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Reframing Community Empowerment through Creativepreneurship as a Multidisciplinary Social Service Model Novi Fitria Hermiati; Muhtarom Muhtarom; Mohd Faiz Hilmi; Lukita Pasha; Lakshmi Devi
ADI Pengabdian Kepada Masyarakat Vol 6 No 2 (2026): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i2.1472

Abstract

Community empowerment through creativepreneurship is essential for sustaining the quality of life and economic self-reliance of local communities, yet the implementation of effective multidisciplinary social service models remains limited. This article aims to evaluate the reframing of community empowerment through creativepreneurship as a multidisciplinary social service model that can enhance community participation and capacity building. The community service approach used a participatory method involving structured training, direct mentoring, interviews, and observation on 47 local MSME participants over six months. Data analysis was conducted through source triangulation to ensure the validity of the findings. Service outcomes showed an increase in creative entrepreneurial skills, strengthened cross-sector collaboration, and improved productivity of micro enterprises. Participants reported increased engagement in local economic activities and the application of innovative ideas in products and marketing. The multidisciplinary social service model based on creativepreneurship is effective in strengthening community empowerment, improving UMKM competencies, and reinforcing socio-economic networks within the community. Recommendations for future research include enhancing digital marketing strategies and expanding the scale of training to other social groups.
Anxiety Prediction Model Based on Smartwatch Activity Data and Self-Reported Affect Scale in Adolescents Po Abas Sunarya; Mohd Faiz Hilmi; Nesti Anggraini Santoso; Sondang Visiana Sihotang; Ramiro Santiago Ikhsan
Journal of Orange Technology Vol. 2 No. 1 (2025): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v2i1.43

Abstract

This study proposes an anxiety prediction model based on smartwatch activity data integrated with a self-reported affect scale in adolescents. Anxiety among adolescents is a growing public health concern, often underdetected due to subjective assessment and limited continuous monitoring. To address this gap, this research combines objective physiological and behavioral indicators collected from smartwatches, including heart rate variability, sleep duration, physical activity intensity, and daily movement patterns, with subjective emotional states measured through a validated affect scale. Data were collected longitudinally from adolescent participants over several weeks to capture temporal variations in activity and mood. Machine learning techniques were applied to develop and evaluate predictive models capable of identifying anxiety levels with high accuracy. Model performance was assessed using standard metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the integration of wearable sensor data with self-reported affect significantly improves anxiety prediction compared to single-source data models. The proposed model offers a scalable, non-invasive, and real-time approach for early anxiety detection, supporting timely intervention and personalized mental health monitoring for adolescents. This study contributes to the development of human-centered digital health technologies and highlights the potential of wearable-based analytics in preventive mental healthcare systems for future applications.
Understanding Data Driven Decision Making Practices in Learning Factory Environments John Edwards; Reem Ahli; Mohd Faiz Hilmi
International Transactions on Education Technology (ITEE) Vol. 4 No. 2 (2026): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i2.1096

Abstract

The increasing integration of data science and learning analytics in learning factory environments has created new opportunities to enhance training effectiveness and align educational processes with real industrial needs, yet understanding how decision making practices are enacted in these contexts remains limited. This study aims to explore how stakeholders interpret and utilize data to support instructional, operational, and strategic decisions that influence skill development and adaptive training in learning factories. A qualitative research design was employed through multiple case studies involving semi structured interviews, direct observations, and analysis of institutional documents to capture in depth insights into practices, challenges, and contextual dynamics surrounding data driven decision making. The findings indicate that successful implementation is shaped by factors such as organizational culture, data literacy levels, leadership support, and the availability of integrated information systems, while common challenges include fragmented data sources, limited analytical competencies, and resistance to data informed change; participants reported that collaborative reflection and continuous feedback loops significantly improved training relevance and learner engagement. The study concludes that strengthening governance structures, investing in capacity building, and promoting a culture that values evidence based decision making can enhance both learning outcomes and operational performance in learning factory settings, providing meaningful implications for educators, industry partners, and policymakers seeking to advance sustainable and technology enhanced workforce development.
Co-Authors John Edwards Lakshmi Devi Lukita Pasha Muhtarom Nesti Anggraini Santoso Novi Fitria Hermiati Po Abas Sunarya Ramiro Santiago Ikhsan Reem Ahli Sondang Visiana Sihotang