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Jurnal Penelitian dan Pengkajian Ilmiah

cendekia
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Published by Lembaga Pendidikan dan Penelitian Manggala Institute

ISSN : - EISSN : 30318939 DOI : https://doi.org/10.62335/dm8f6t74

Core Subject : Economy, Humanities, Health, Science, Social,

Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Public Health

CENDEKIA : Jurnal Penelitian dan Pengkajian Ilmiah accomodates original research, or theoretical papers. We invite critical and constructive inquiries into wide range of fields of study with emphasis on interdisciplinary approaches: Humanities and Social sciences, that include: Economics, Health, Social, Science, Engineering, Computer Science & IT, Politic and Law.

Arjuna Subject : Umum - Umum

Articles 422 Documents

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Pengaruh Dimensi e-WOM terhadap Minat Beli Konsumen (Studi pada Pengguna Aplikasi Shopee di Kota Kendari) Yusuf Yusuf
CENDEKIA : Jurnal Penelitian dan Pengkajian Ilmiah Vol. 3 No. 7 (2026): CENDEKIA : Jurnal Penelitian Dan Pengkajian Ilmiah, Juli 2026
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/cendekia.v3i7.2850

The research was conducted to determine the influence of e-WOM Credibility, e-WOM Quality and e-WOM Quantity on buying interest in Shopee application users in Kendari City. The samples used were 80 samples with the criteria of being 15 years old and above. The sampling technique Accidental sampling, which is a sampling technique based on chance, that is, anyone who happens to meet a researcher, can be used as a sample, if it is seen that the person who happens to meet is suitable as a data source. The researcher used a quantitative approach using multiple linear regression analysis with the help of the IBM 23 version of the SPSS program. The results of this study show that the e-WOM Credibility variable (X1) has a positive and significant effect on the buying interest of Shopee application users in Kendari City (Y), the e-WOM Quality (X2) variable has a positive and significant effect on the buying interest of Shopee application users in Kendari City (Y), the e-WOM Quantity (X3) variable has a positive and significant effect on the buying interest of Shopee application users in Kendari City (Y) e-WOM Credibility, e-WOM Quality and e-WOM Quantity simultaneously have a positive and significant effect on the buying interest of Shopee application users in Kendari City.

Virtual Real-Time Artificial Intelligence Learning Platform for Adaptive Drone Operation Training Using Digital Twin and Vision-Based Simulation Nur Rachman Supadmana Muda; Sudirman Syam
CENDEKIA : Jurnal Penelitian dan Pengkajian Ilmiah Vol. 3 No. 7 (2026): CENDEKIA : Jurnal Penelitian Dan Pengkajian Ilmiah, Juli 2026
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/cendekia.v3i7.2869

The rapid advancement of Unmanned Aerial Vehicles (UAVs) has significantly expanded their applications in surveillance, disaster management, precision agriculture, logistics, infrastructure inspection, and defense. Consequently, the demand for competent drone operators has increased substantially. Conventional drone training primarily relies on physical flight exercises, which require considerable operational costs, specialized instructors, extensive training areas, and involve risks of equipment damage and safety incidents. These limitations highlight the need for a safer, more efficient, and adaptive learning platform. This study proposes a Virtual Real-Time Artificial Intelligence (VRTAI) learning platform that integrates Digital Twin technology, Large Language Models (LLMs), Vision Transformer (ViT), and real-time flight simulation into an intelligent educational environment for drone operation training. The proposed platform provides immersive simulation scenarios, AI-assisted instruction, automatic performance assessment, and adaptive learning based on trainee competency. A Digital Twin replicates the physical UAV and its operational environment, enabling realistic flight dynamics and mission planning. Vision-based AI performs object detection and situational awareness training, while the AI tutor delivers interactive guidance and immediate feedback. The system architecture consists of five major components: a virtual simulation engine, AI learning engine, Digital Twin module, real-time analytics dashboard, and learning management system. Performance evaluation is conducted through simulation scenarios involving manual flight, autonomous navigation, waypoint missions, emergency procedures, and object detection tasks. Learning outcomes are assessed using flight stability, navigation accuracy, collision avoidance, mission completion time, and AI-assisted competency scores. The proposed framework demonstrates that integrating artificial intelligence with virtual real-time simulation can significantly enhance learning effectiveness, reduce operational costs, improve safety, and provide personalized learning experiences. This platform offers a promising solution for future drone education and professional operator certification programs.

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Marthadiansyah

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INDONESIA

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Marthadiansyah

Contact Email cendekia.manggalajournal@gmail.com

Phone +6281935181060

Journal Mail Official cendekia.manggalajournal@gmail.com

Editorial Address Jalan Merdeka Raya No.5, Karang Pule, Kecamatan Sekarbela, Kota Mataram, NTB 83116, Indonesia

Location Kota mataram, Nusa tenggara barat INDONESIA

Abstract

Abstract

Contact Name
Marthadiansyah

Contact Email
cendekia.manggalajournal@gmail.com

Phone
+6281935181060

Journal Mail Official
cendekia.manggalajournal@gmail.com

Editorial Address
Jalan Merdeka Raya No.5, Karang Pule, Kecamatan Sekarbela, Kota Mataram, NTB 83116, Indonesia

Location
Kota mataram,

Nusa tenggara barat

INDONESIA