Peningkatan Efektivitas Pelatihan Industrial IoT Melalui Integrasi Augmented Reality dan Pendekatan Quality Function Deployment (QFD)

Irbah, Salsabila (2026) Peningkatan Efektivitas Pelatihan Industrial IoT Melalui Integrasi Augmented Reality dan Pendekatan Quality Function Deployment (QFD). Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Di era Industri 4.0, teknologi Industrial Internet of Things (IIoT) memerlukan media pembelajaran dan pelatihan yang mampu menjelaskan sistem industri secara menyeluruh, interaktif, dan sesuai dengan konteks. Namun, pelatihan IIoT yang masih menggunakan media konvensional seperti Demo Box memiliki keterbatasan, terutama dalam hal visualisasi komponen internal, interaktivitas, dan pemahaman peserta tentang alur kerja serta integrasi data dalam sistem IIoT. Keterbatasan ini dapat menurunkan efektivitas pembelajaran, terutama bagi peserta pemula. Untuk mengatasi masalah ini, penelitian ini mengembangkan dan mengevaluasi media pembelajaran berbasis Augmented Reality (AR) untuk meningkatkan kualitas pelatihan IIoT. Metode Agile SCRUM dan Quality Function Deployment (QFD) digunakan untuk mengidentifikasi dan menerjemahkan kebutuhan pengguna dan kebutuhan teknis ke dalam pengembangan AR secara bertahap dan adaptif. Evaluasi dilakukan pada empat aspek utama: kemudahan penggunaan, fungsionalitas sistem, efektivitas pembelajaran, dan tingkat kepuasan, dengan melibatkan 105 responden yang terdiri dari peserta pelatihan Industrial IoT dan instruktur pelatihan. Hasil evaluasi menunjukkan peningkatan di semua aspek setelah penggunaan media AR, berdasarkan nilai median dan modus. Pada aspek kemudahan penggunaan, median naik dari 4 ke 6, dan modus juga naik dari 4 ke 6. Fungsionalitas sistem menunjukkan median naik dari 5 ke 6, dengan modus juga ke nilai tertinggi. Efektivitas pembelajaran dan tingkat kepuasan menunjukkan pola serupa, dengan median naik dari 4-5 ke 6, dan modus terkonsentrasi di nilai 6. Perubahan ini menunjukkan bahwa sebagian besar responden memberikan penilaian lebih tinggi dan seragam pada media AR dibandingkan Demo Box. Media AR yang dikembangkan dapat menampilkan visualisasi data sensor dan actuator secara real-time dalam model digital tiga dimensi melalui protokol Message Queuing Telemetry Transport (MQTT). Peserta dapat langsung berinteraksi dengan simulasi proses industri melalui antarmuka yang mudah digunakan, sehingga mendorong pembelajaran mandiri dan partisipasi aktif selama pelatihan. Secara keseluruhan, penelitian ini menunjukkan bahwa media pembelajaran berbasis AR dapat menjadi alternatif sekaligus pelengkap alat peraga Demo Box dalam pelatihan IIoT. Berdasarkan distribusi median dan modus, AR memberikan pengalaman belajar yang lebih interaktif, konsisten, dan efektif, sehingga cocok diterapkan dalam pelatihan industri dan vokasional di era Industri 4.0.
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The rapid development of the Industrial Internet of Things (IIoT) in the Industry 4.0 era requires learning and training media that explain industrial systems comprehensively, interactively, and contextually. However, conventional IIoT training tools, such as the Demo Box, still have several limitations, particularly in visualizing internal components, providing interactivity, and supporting learners’ understanding of system workflows and data integration. These limitations may reduce training effectiveness, especially for novice learners. To address this issue, this study aims to develop and evaluate an Augmented Reality (AR)-based learning medium to improve the quality of IIoT training. The Agile Scrum method and the Quality Function Deployment (QFD) approach were employed to identify and translate user requirements into an iterative, adaptive development of the AR system. The evaluation involved 105 respondents, consisting of Demo Box users and AR users, and assessed four main aspects: ease of use, system functionality, learning effectiveness, and user satisfaction. The evaluation results indicate consistent improvements across all aspects following implementation of the AR-based learning medium, as assessed using median and mode. In terms of ease of use, the median increased from 4 to 6, while the mode shifted from 4 to 6. System functionality increased from a median of 5 to 6, with the mode also concentrated at the highest value. Similar patterns were observed for learning effectiveness and user satisfaction, with median values increasing from 4-5 to 6 and the mode converging to 6. These results indicate that the majority of respondents provided higher, more uniform evaluations of the AR-based learning medium than of the Demo Box. The developed AR system enables real-time visualization of sensor and actuator data within a three-dimensional digital model through the Message Queuing Telemetry Transport (MQTT) protocol. Users can interact directly with industrial process simulations through an intuitive interface, thereby supporting self-directed learning and active engagement during training sessions. Overall, this study demonstrates that the AR-based learning medium serves as an effective alternative and complementary tool to the Demo Box for IIoT training. Based on analyses of median and mode distributions, AR provides a more interactive, consistent, and effective learning experience, making it highly suitable for industrial and vocational training environments in the Industry 4.0 era.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Industrial Internet of Things, Augmented Reality, Quality Function Deployment, Pelatihan, Industri 4.0. ============================================================== Industrial Internet of Things, Augmented Reality, Quality Function Deployment, Training, Industry 4.0.
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T58.6 Management information systems T Technology > TS Manufactures
Divisions:	Interdisciplinary School of Management and Technology (SIMT) > 61101-Master of Technology Management (MMT)
Depositing User:	Salsabila Irbah
Date Deposited:	29 Jan 2026 02:09
Last Modified:	29 Jan 2026 02:09
URI:	http://repository.its.ac.id/id/eprint/130318

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