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All Journal Jurnal Teknologi dan Manajemen Industri Terapan International Journal of Educational Technology and Society International Journal of Mechanical, Industrial and Control Systems Engineering
Helmi Wibowo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Investigating Longitudinal Effects of Adaptive Digital Learning Ecosystems on Self Regulated Learning and Academic Persistence Helmi Wibowo; Benny Daniawan; Erna Auparay
International Journal of Educational Technology and Society Vol. 2 No. 4 (2025): December: International Journal of Educational Technology and Society
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijets.v2i4.466

Abstract

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.
Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems Irlon Irlon; Siti Shofiah; Helmi Wibowo; Erick Fernando; Genrawan Hoendarto; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 2 (2025): June :IJMICSE: International Journal of Mechanical, Industrial and Control Syst
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i2.404

Abstract

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.
Analisis dan Redesain Kursi Pengemudi Truk Pertamina Ditinjau dari Aspek Ergonomi Agung Tri Febrianto; Rifano; Helmi Wibowo; Sugiyarto
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 5 No. 2 (2026): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v5i2.1748

Abstract

Pengemudi truk tangki PT Pertamina (Persero) menghadapi risiko gangguan muskuloskeletal akibat penggunaan kursi pengemudi yang belum sepenuhnya memenuhi prinsip ergonomi. Penelitian ini bertujuan menganalisis tingkat ergonomis kursi pengemudi truk Pertamina berbasis sasis Hino di Fuel Terminal Rewulu serta merumuskan rekomendasi redesain berbasis antropometri. Metode yang digunakan meliputi pengukuran sudut postur dengan goniometer pada 58 responden, kuesioner Nordic Body Map (NBM) dengan skala Likert, serta simulasi software Jack versi 8.4 menggunakan metode Posture Evaluation Index (PEI) yang mengintegrasikan Low Back Analysis (LBA), Ovako Working Posture Analysis System (OWAS), dan Rapid Upper Limb Assessment (RULA). Hasil menunjukkan bahwa sudut torso tidak memenuhi standar rekomendasi, nilai PEI tertinggi sebesar 2,100 dan terendah 1,408, serta keluhan muskuloskeletal terbesar terdapat pada batang tubuh dan leher. Rekomendasi redesain kursi berbasis data antropometri Perhimpunan Ergonomi Indonesia berhasil menurunkan nilai PEI hingga 1,835 pada responden dengan risiko tertinggi, membuktikan bahwa penyesuaian dimensi kursi secara ergonomis efektif meningkatkan kenyamanan dan keselamatan pengemudi.
Co-Authors Agung Tri Febrianto Benny Daniawan Erick Fernando Erna Auparay Genrawan Hoendarto Irlon Irlon Mursalim Mursalim Rifano Siti Shofiah Sugiyarto