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Unraveling the Research Trends of Artificial Intelligence in Aviation: A Bibliometric Analysis Sulung, Sabam Danny; Nasrullah, Muhammad Nur Cahyo Hidayat; Wibowo, Untung Lestari Nur
Journal of Science Technology (JoSTec) Vol. 5 No. 1 (2023): Journal of Science Technology (JoSTec)
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/jostec.v5i1.696

This study employs bibliometric methods utilizing VOSviewer analysis of Scopus data from 2013 to 2023 to investigate trends in artificial intelligence (AI) research within the aviation industry. The analysis reveals a substantial increase in publication volume over time, peaking at 406 articles in 2022, signifying a heightened interest in AI implementation within the aviation sector. Key publication sources notably include conferences such as AIAA IEEE Digital Avionics Systems Conference Proceedings and ACM International Conference Proceeding Series. Leading contributions in publications emerge from countries such as the United States, China, India, Germany, the United Kingdom, and France, reflecting global involvement in AI research within the aviation industry. Citation analysis identifies highly cited articles addressing topics such as Digital Twin (DT) optimization processes in aviation, AI application in aircraft navigation, and machine learning for weather forecasting. These findings underscore researchers' interest in fundamental topics such as aviation, aircraft-related artificial intelligence, flight delay, and deep learning. Furthermore, co-citation analysis delineates research clusters, illustrating thematic similarities within AI research in the aviation industry. Overall, this bibliometric analysis provides comprehensive insights into the evolution of AI research in the aviation industry, potentially guiding researchers, practitioners, and stakeholders in directing research efforts, formulating policies, and understanding current trends in the application of artificial intelligence within the aviation sector.

Improving Reading Interest of Net Generation Cadets Through the Development of Innovative Library Facilities and Infrastructure Endrawati, Rizki Ocsera; Rumani, Daniel Dewantoro; Hariri, Ahmad; Prasojo, Genny Luhung; Wibowo, Untung Lestari Nur
Sinergi International Journal of Education Vol. 1 No. 3 (2023): November 2023
Publisher : Yayasan Sinergi Kawula Muda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61194/education.v1i3.86

This study explores boosting the reading interest of net generation cadets through innovative library facility and infrastructure development. The independent variable is library facility and infrastructure development, while the dependent variable is next generation cadets' reading interest. Quantitative research employing the Likert scale gathered data from Indonesian Flight Academy cadets in Banyuwangi. Descriptive analysis reveals a positive impact of library facility and infrastructure development on cadets' reading interest, supported by high mean and median values. Inferential statistical analysis further confirms a significant relationship between these factors. These findings significantly contribute to understanding factors influencing net generation cadets' reading interest. Practical implications advocate increased investment in innovative library facility and infrastructure, including relevant book additions, technology for information access, and comfortable reading spaces. Identifying additional influencing factors and promoting literacy programs and reading habit development are recommended. In conclusion, innovative library facility and infrastructure development critically enhance net generation cadets' reading interest. Regular evaluation and updates of implemented facilities, programs, and policies are essential, requiring collaboration among educational institutions, libraries, government, and the community for optimal results in improving reading interest. in one or two sentences.

Unraveling the Research Trends of Artificial Intelligence in Aviation: A Bibliometric Analysis Sulung, Sabam Danny; Nasrullah, Muhammad Nur Cahyo Hidayat; Wibowo, Untung Lestari Nur; Lubis, Julianto
Journal of Science Technology (JoSTec) Vol. 5 No. 1 (2023): Journal of Science Technology (JoSTec)
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/jostec.v5i1.696

This study employs bibliometric methods utilizing VOSviewer analysis of Scopus data from 2013 to 2023 to investigate trends in artificial intelligence (AI) research within the aviation industry. The analysis reveals a substantial increase in publication volume over time, peaking at 406 articles in 2022, signifying a heightened interest in AI implementation within the aviation sector. Key publication sources notably include conferences such as AIAA IEEE Digital Avionics Systems Conference Proceedings and ACM International Conference Proceeding Series. Leading contributions in publications emerge from countries such as the United States, China, India, Germany, the United Kingdom, and France, reflecting global involvement in AI research within the aviation industry. Citation analysis identifies highly cited articles addressing topics such as Digital Twin (DT) optimization processes in aviation, AI application in aircraft navigation, and machine learning for weather forecasting. These findings underscore researchers' interest in fundamental topics such as aviation, aircraft-related artificial intelligence, flight delay, and deep learning. Furthermore, co-citation analysis delineates research clusters, illustrating thematic similarities within AI research in the aviation industry. Overall, this bibliometric analysis provides comprehensive insights into the evolution of AI research in the aviation industry, potentially guiding researchers, practitioners, and stakeholders in directing research efforts, formulating policies, and understanding current trends in the application of artificial intelligence within the aviation sector.

Analysis Of Standby Horizon Gyro Indicator Failure On Cessna 172 Series Aircraft Using FMEA And FTA Methods At API Banyuwangi Dharma, I Made Dwi Surya; Luwihono, Andung; Sulung, Sabam Danny; Wibowo, Untung Lestari Nur; Rahmanda, Nauffal Daffa
Jurnal Teknologi Kedirgantaraan Vol 10 No 2 (2025): Jurnal Teknologi Kedirgantaraan
Publisher : FTK UNSURYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35894/jtk.v10i2.331

In aviation, navigation instruments play a vital role in ensuring flight safety, particularly during adverse weather and night operations. Among these, the Standby Horizon Gyro Indicator, also known as the Attitude Indicator, is critical for displaying aircraft pitch and roll relative to the horizon. Failures of this instrument can significantly compromise safety, making systematic analysis essential. This study investigates failures of the Standby Horizon Gyro Indicator on Cessna 172 Series aircraft using Failure Modes and Effect Analysis (FMEA) and Fault Tree Analysis (FTA). Data were obtained from field observations, pilot reports, and interviews with certified technicians at API Banyuwangi. The analysis identified five primary failure modes: Low Vacuum Indicator, Not Function, Toppled/Spin, Unbalanced Gyro, and Stuck. The Toppled/Spin condition was found to be the most critical, with a Risk Priority Number (RPN) of 126. FTA revealed root causes including vacuum pump aging, contaminated filters, inadequate knowledge, complacency, lack of supervisory cross-checks, and low safety awareness. Corrective actions involve replacing worn components, cleaning filters, and applying strict safety procedures, while preventive measures emphasize scheduled maintenance, double-check protocols, and periodic safety training. The findings highlight the importance of addressing both technical and human factors to enhance reliability, improve maintenance practices, and strengthen aviation safety culture.

Strategic Valuation of Generative AI in Retail: A Real Options Approach to Managing Innovation Uncertainty Yuda, Fardan Zeda Achmadi; Wibowo, Untung Lestari Nur
Novatio : Journal of Management Technology and Innovation Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/novatio.v3i2.861

Generative Artificial Intelligence (AI) is reshaping retail investment strategies, yet traditional evaluation tools such as Net Present Value (NPV) and Internal Rate of Return (IRR) struggle to capture uncertainty and flexibility. This study applies a binomial lattice real options model to assess Generative AI investments in retail, demonstrating that real options provide a more adaptive framework than conventional methods. The model evaluates multi-stage decisions pilot testing, regional scaling, and enterprise adoption and incorporates sensitivity analyses to account for adoption probabilities and volatility scenarios. Results indicate that real options modeling captures strategic flexibility by valuing managerial discretion, phased rollouts, and intangible benefits, which static NPV models overlook. This highlights its relevance for addressing retail-specific challenges such as data integration and organizational readiness. The study concludes that real options offer a superior framework for evaluating AI investments, supporting adaptive planning and long-term strategic value for retailers.

Environmental Determinants of Employee Performance in Air Cargo Logistics: Evidence from Indonesia’s Warehouse Sector Sabang, Yusmiaty; Wibowo, Untung Lestari Nur; Hariri, Ahmad; Jakfar
Logistica : Journal of Logistic and Transportation Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i1.734

Background: The work environment is a critical determinant of employee performance, particularly in air cargo logistics where accuracy, speed, and consistency are essential. Despite the increasing focus on technological and automation strategies, environmental conditions remain relatively underexplored. Objective: This study aims to examine the influence of workplace environmental factors on employee performance in the warehouse division of PT Aerojasa Cargo, Jakarta. Method: A quantitative cross-sectional survey was conducted with 55 respondents selected through stratified random sampling. Data were collected using a structured Likert-scale questionnaire measuring five environmental factors (cleanliness, lighting, air circulation, workspace layout, and team collaboration) and five dimensions of performance (accuracy, timeliness, quality, quantity, and neatness). The data were analyzed using descriptive statistics, Pearson correlation, and multiple linear regression. Results: The findings reveal that 69.9% of the variance in employee performance is explained by workplace environmental conditions (R² = 0.699; p < 0.01). Cleanliness and team collaboration emerged as the strongest predictors across all performance dimensions, while lighting and workspace layout also showed significant contributions. Conclusion: A conducive work environment plays a pivotal role in enhancing warehouse employee performance. Practical implications include continuous investment in cleanliness programs, ergonomic workspace redesign, and participatory evaluation mechanisms. Future research should adopt multi-site and longitudinal approaches to strengthen generalizability.

Industry 4.0 and the Future of Supply Chains: A Narrative Review of Digital Integration Judijanto, Loso; Wibowo, Untung Lestari Nur; Putra, Dimas Endrawan; Pratiwi, Sekar Widyastuti
Logistica : Journal of Logistic and Transportation Vol. 2 No. 3 (2024): July 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i3.1056

The rapid emergence of Industry 4.0 has reshaped supply chain management by introducing advanced digital technologies such as artificial intelligence, blockchain, Internet of Things, and big data analytics. This study aims to explore how the integration of these technologies influences efficiency, resilience, and sustainability in global supply chains. A systematic literature review was conducted using major academic databases, including Scopus, Web of Science, and Google Scholar, applying carefully selected keywords to identify relevant studies published between 2010 and 2025. Inclusion criteria focused on empirical, conceptual, and review studies addressing digital transformation in supply chain management, while irrelevant and non-peer-reviewed sources were excluded. Results indicate that IoT improves real-time visibility, AI enhances demand forecasting and risk management, blockchain strengthens transparency and trust, and big data analytics provides actionable insights for strategic decision-making. Collectively, these technologies reduce costs, mitigate risks, and support environmental sustainability by reducing waste, emissions, and inefficiencies. However, the findings also reveal systemic barriers, including inadequate infrastructure, limited resources in developing economies, regulatory inconsistencies, and organizational resistance to change. The discussion emphasizes the importance of supportive policies, public–private collaboration, and organizational cultural shifts to enable effective adoption. While theoretical models of digital supply chains are validated, empirical gaps remain, particularly concerning interoperability and long-term impacts. Future research should pursue longitudinal and sector-specific studies to address these limitations. Overall, digital transformation emerges as both a strategic necessity and a pathway toward inclusive, resilient, and sustainable supply chain management.

Unlocking Renewable Potential in Logistics Hubs: Policy Frameworks for Inclusive Energy Transitions udijanto, Loso; Wibowo, Untung Lestari Nur; Putra, Dimas Endrawan
Logistica : Journal of Logistic and Transportation Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v3i3.1148

Logistics hubs play a vital role in global decarbonization due to their high energy use and strategic position within supply chains, yet they are often excluded from national renewable energy policies. This exclusion limits their potential to become active clean energy producers. This study examines the regulatory and institutional barriers that hinder renewable energy integration in logistics hubs and proposes an inclusive policy roadmap tailored for this sector—offering a novel contribution beyond prior studies focused on residential and industrial contexts. Using a comparative methodology, the research analyzes energy regulatory frameworks across Germany, Brazil, the UAE, Indonesia, and the United States. Data were collected from government reports, institutional documents, and peer-reviewed studies. A hybrid analytical framework combining stakeholder mapping and policy categorization was employed to identify existing gaps and opportunities. The findings show that logistics hubs are frequently excluded from mechanisms such as net metering, feed-in tariffs, and tax incentives due to outdated classifications that overlook their dual industrial-service roles. Case studies from Germany, Brazil, and the UAE highlight the effectiveness of targeted measures like grid fast-tracking, specific subsidies, and integrated municipal approaches in advancing renewable adoption. Furthermore, strong public-private partnerships and dynamic pricing systems are key to aligning logistics operations with renewable goals. The study concludes that infrastructure development must be complemented by policy innovation through harmonized, inclusive, and multi-level governance to embed logistics hubs effectively in renewable energy strategies.

Design Pitot Tube Cover with Artificial Intelligence (Arduino) Based Warning System on Piper Seneca V Aircraft Putra, Dimas Endrawan; Wibowo, Untung Lestari Nur; Kuncoro, Wisnu; Anam, Muhamad Khoirul
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.5614

The pitot tube is a component in the fuselage to measure the pressure generated by air movement or wind, while the placement of the pitot tube is usually on the wing. A trivial thing that can be fatal is that before flying, sometimes the pilot/flight instructor neglects to remove the pitot tank cover. This negligence resulted in the malfunctioning of the Air Speed Indicator (ASI), Vertical Speed Indicator and Altimeter. Appropriate technology in the form of a pitot tube cover with a warning using an ultrasonic sensor with the support of artificial intelligence technology using Arduino has been applied to the Piper Seneca V aircraft to prevent negligence in removing the pitot tube cover before flying. The Piper Seneca V has been chosen as the object of the application of technology because the Piper Seneca V type aircraft is of the low wing type so that the pitot tubes are not visible when not inspecting the underside of the aircraft's wings. During 30 days of testing with 12 hours of testing time each day and exposure to different temperatures and humidity each day, both battery life and component resistance can be ensured that they are still in optimal conditions. During testing in the simulation room, the tool worked well with a 1 meter object detection configuration. Using a 3.7 volt battery with a storage capacity of 1600 mAh can make the device last for 4 days with 2 hours of recharging time.

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