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All Journal TEKNIK Turbo : Jurnal Program Studi Teknik Mesin TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Studi Akuntansi, Keuangan, dan Manajemen Journal of Science Technology (JoSTec) National Multidisciplinary Sciences Sinergi International Journal of Logistics Logistica : Journal of Logistic and Transportation
Muhammad Nur Cahyo Hidayat Nasrullah
Akademi Penerbang Indonesia Banyuwangi
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Transportation

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Journal : Journal of Science Technology (JoSTec)

 1 
Impact of the fuel mixture ratio of AVGAS 100LL and RON 92 fuel on combustion characteristics Sabam Danny Sulung; Daniel Dewantoro Rumani; Ikhwanul Qiram; Muhammad Nur Cahyo Hidayat Nasrullah; Untung Lestari Nur Wibowo
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.v5i1.478

Abstract

AVGAS 100LL is an aviation fuel used in piston engine aircraft, particularly in training aircraft such as the Cessna 172S with Lycoming engines. The use of lead in this fuel can have various health-related concerns. Therefore, reducing the use of leaded fuel has become a solution to address these issues. This study aimed to investigate the combustion characteristics of AVGAS fuels, including AVGAS 100%, AVGAS 75% + PERTAMAX 25%, and AVGAS 50% + PERTAMAX 50%. The research involved conducting combustion tests using a Bunsen burner. The results showed that the addition of PERTAMAX to AVGAS significantly influenced the temperature, color, flame height, and flame area produced. The temperature values were higher for AVGAS 100% compared to AVGAS mixed with PERTAMAX. On the other hand, the flame height and flame area were lower for AVGAS 100% compared to the blended fuels. These findings indicate that the addition of PERTAMAX affects the combustion characteristics of AVGAS fuels. Further studies are recommended to explore and expand our understanding of the effects of blending AVGAS with alternative fuels.
Performance Tests of Cessna 172S Magnetos Under Various Thermal Conditions Muhammad Nur Cahyo Hidayat Nasrullah; Sabam Danny Sulung; Untung Lestari Nur Wibowo; Ikhwanul Qiram
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.v5i1.628

Abstract

Magneto serves as the ignition source in the combustion process of piston aircraft engines. Typically, spark-ignition aircraft like the Cessna 172SP are equipped with two ignition systems (dual magnetos). Dual magnetos are installed so that if one system fails, the aircraft can still continue its flight. During the engine startup process, the magneto plays a significant role as the ignition source for the initial combustion before the aircraft engine comes to life. In the case experienced by pilots at the Indonesian Civil Pilot Academy Banyuwangi, starting the engine became challenging when the aircraft had been previously used for a flight. Therefore, this research is conducted to address the underlying causes of this issue. The parameters examined in this research include outside air temperature (OAT), Cylinder Head Temperature (CHT), and Exhaust Gas Temperature (EGT). Data were collected regarding the duration of the engine startup process. The duration time for starting the engine was found to be the highest at an exhaust gas temperature of 135 °F, a cylinder head temperature of 180 °F, and an outside air temperature of 35 °F, with a duration time of 6.8 seconds. Conversely, the shortest engine startup duration was observed at an exhaust gas temperature of 75 °F, a cylinder head temperature of 75 °F, and an outside air temperature of 26 °F, resulting in a duration time of 1.6 seconds.
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

Abstract

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.
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

Abstract

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.
Co-Authors Aisyah Rahmawati Andi Frianto Peranginangin Arief Rusdyansyah Budiyanto, Albert Citra Arta, Deddy Novie Daniel Dewantoro Rumani Dimas Endrawan Putra Dimas Hari Cahyo Fajar Islam Farhan, Ahmad Raihan Gatut Rubiono Hadi Prayitno Hadi Prayitno, Hadi Hariri, Ahmad Herdian, Rofik Sandra Ikhwanul Qiram Ikhwanul Qiram Lubis, Julianto Mahros Darsin Malau, Olin Yahya Marini Mintje, Quirina Ariantji Patrisia Muh Nurkoyim Kustanto Nasrul Ilminnafik Nurkoyim Kustanto, Muh Sabam Danny Sulung Sulung, Sabam Danny Syuhri, Skriptyan Noor Hidayatullah Untung Lestari Nur Wibowo Untung Lestari Nur Wibowo Wibowo, Untung Lestari Nur