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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.

Penggunaan Flight Data Logger untuk Menganalisis Dampak Modifikasi Seaplane pada Kinerja Take Off Cessna PK-APH: Studi Komparasi Nasrullah, Muhammad Nur Cahyo Hidayat; Rubiono, Gatut; Sulung, Sabam Danny; Prayitno, Hadi
TEKNIK Vol. 45, No. 1 (2024): May 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v45i1.57634

Penelitian ini dilakukan untuk membandingkan take off performance antara pesawat yang telah dimodifikasi menjadi pesawat seaplane (amfibi) dan pesawat Cessna standar sebelum dimodifikasi. Komparasi dilakukan menggunakan data dari flight data recorder Garmin G1000 dengan flight data logger. Data yang dipilih adalah berdasarkan pada satu pesawat yang sama, yakni dengan registrasi PK-APH, namun data difilterisasi dengan berbagai kondisi. Tujuan penelitian ini adalah untuk mengetahui dampak yang ditimbulkan oleh modifikasi seaplane (amfibi) yang telah dilakukan dari segi fase climbing, perbandingan ground roll, maksimal ground speed serta maksimal airspeed. Analisis menunjukkan perbedaan signifikan antara pesawat sebelum dan setelah dimodifikasi menjadi pesawat seaplane. Sebelum modifikasi, pesawat mencapai ketinggian 478,4 kaki diatas permukaan laut dalam 60 detik setelah lepas landas, sedangkan setelah modifikasi hanya mencapai 355,7 kaki di atas permukaan laut. Ground speed pada detik ke-20 juga berbeda, dengan pesawat sebelum modifikasi mencapai 60,69 knots dan pesawat seaplane hanya mencapai 48,65 knots. Perbedaan terlihat pada airspeed awal saat take-off, di mana pesawat sebelum modifikasi memiliki angka 71 knots pada detik ke-24, sedangkan pesawat seaplane memiliki angka 66 knots.

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.

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