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Bird Detection System Design at The Airport Using Artificial Intelligence Ummah, Khairul; Hidayat, Muhammad Fadly; Kurniawan, Denni; Zulhanif, Zulhanif; Sembiring, Javensius
International Journal of Aviation Science and Engineering - AVIA Vol. 4, No. 2 (December 2022)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v4i2.72

Bird strike is a process of crashing between bird and airplane which occurs in flight phase. Based on data, there are 40 times bird strike occurs every day (FAA, 2019). There are lot of research that already conducted to decrease number of birds at the airport. But it is not given significant changes. Hence, it is needed a model that can detect bird at the airport so that we can decrease the number of birds. Study already conducted by comparing motion detection with object detection and filter which can be used to improve detection quality. Model already developed using YOLOv4 object detection with 71.89% mean average precision. It is expected that object detection can be developed to become a bird repellent system in the future

Aircraft Detection in Low Visibility Condition Using Artificial Intelligence Ummah, Khairul; Widyosekti, M. Dhiku; Arif, Yanuar Zulardiansyah; Saputra, Rizal Adi; Riszal, Akhmad; Sembiring, Javensius
International Journal of Aviation Science and Engineering - AVIA Vol. 5 No. 1: (June 2023)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v5i1.84

Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.

Aircraft Detection in Low Visibility Condition Using Artificial Intelligence Sembiring, Javensius; Ummah , Khairul; Widyosekti, M. Dhiku; Arif, Yanuar Zulardiansyah; Huda, Zulmiftah
Journal of Applied Science, Engineering and Technology Vol. 4 No. 1 (2024): June 2024
Publisher : INSTEP Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/jaset.v4i1.64

Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.

Estimating Air Travel Demand in North Sumatra Using Gravity Model Approach with Economic and Route Analysis Zulkarnain, Ahnis; Pasaribu, Hisar Monongam; Sembiring, Javensius
Langit Biru: Jurnal Ilmiah Aviasi Vol 18 No 1 (2025): Langit Biru: Jurnal Ilmiah Aviasi
Publisher : Politeknik Penerbangan Indonesia Curug

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54147/langitbiru.v18i1.1284

This study estimates regional air travel demand in North Sumatra Province using variations of the gravity model. The objectives are to identify key factors influencing air travel demand, estimate demand through different model formulations, and assess airport infrastructure adequacy for regional connectivity. Three models were developed, progressively incorporating economic indicators such as GDP per capita, population, and distance, alongside socio-economic variables like leisure attractions, hotel accommodations, universities, and health facilities. The methodology involved log-linear transformations and regression analysis to estimate parameters. Results revealed significant variability in air travel demand, driven by proximity and economic activity, with the population coefficient shifting from 0.707 in Model 1 to -0.178 in Model 3, as socio-economic variables like leisure attractions (0.811) and GDP per capita (0.813) became more influential. Findings also exposed disparities in airport coverage, highlighting the need for strategic infrastructure improvements, particularly for high-demand pairs like Medan - Mandailing Natal.

Analisis Topologi Jaringan Penerbangan Indonesia Menurut PM 88 Tahun 2013 Sembiring, Javensius; Fathurrohim, Luqman; Mulyanto, Taufiq; Yarlina, Lita; Widadi, Novyanto
WARTA ARDHIA Vol. 48 No. 1 (2022)
Publisher : Sekretariat Badan Kebijakan Transportasi, Kementerian Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/wa.v48i1.445.15-24

Penelitian ini memodelkan jaringan penerbangan domestik yang diatur dalam PM 88 Tahun 2013 dari sisi topologinya. Analisis topologi dilakukan baik dari sisi sistem jaringan sebagai satu jaringan yang terintegrasi dan analisis dari sisi peran bandara yang secara signifikan berpengaruh terhadap jaringan. Berdasarkan analisis jaringan sebagai sistem yang terintegrasi diperoleh bahwa satu bandara terkoneksi rata-rata dengan 7 bandara lainnya, rute terjauh dapat dijangkau dengan 6 kali penerbangan, jaringan mengikuti distribusi power-law, dua bandara rata-rata dapat ditempuh dengan 3 kali penerbangan. Sedangkan dari sisi peran bandara diperoleh bahwa bandara CGK, SUB, UPG, AMQ, MDC, DPS, BPN, dan KNO memiliki kontribusi yang sangat penting bagi jaringan domestik berjadwal seperti yang diatur dalam PM 88 Tahun 2013. Kontribusi per bandara ini dikuantifikasi melalui metrik seperti degree of centrality, betweenness centrality, eigenvector centrality, dan page rank.

Stochastic Modelling of Aircraft Ground Time at Soekarno-Hatta International Airport Harjono, Okky Sukmawati; Sembiring, Javensius; Pasaribu, Hisar Manongam
Warta Penelitian Perhubungan Vol. 35 No. 2 (2023): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v35i2.2312

Ground time plays an important role in keeping flight on-time performance and passenger smooth flow. It varies depending on the aircraft type, procedures, passenger number and/or cargo amount, maintenance requirements, and ground handling service quality. This research aims to explore the ground time distribution pattern at Soekarno-Hatta International Airport. The daily flight historical data is divided into several categories based on the airline’s service type for local airlines, the airline’s origin for foreign airlines, the type of flight, and aircraft size. Ground time data of each flight category is then fitted to all possible distribution types by using the Distribution Fitting app in Matlab. The best-fitted distribution definition uses the Kolmogorov-Smirnov test by comparing the p-value of each distribution. 6 distributions fit 20 flight categories. Almost all local airlines’ ground time except full-service carrier international flights and low-cost carrier international flights with wide-body aircraft fit to Burr distribution. Full-service carrier international flight with narrow and wide-body aircraft, international flight with narrow-body aircraft operated by airlines from China and other countries fit Generalized Extreme Value distribution. Low-cost carrier international flights with wide-body aircraft and private flights fit to Inverse Gaussian distribution. International flights with wide-body aircraft operated by airlines from Korea, Japan, and other countries airlines fit for Nakagami distribution. While the cargo flights fit t Location-Scale distribution for wide-body aircraft and Weibull distribution for narrow-body aircraft. Then the stochastic models are developed based on each flight category’s distribution parameters. These models are expected to be able to guide future research in ground time or apron capacity management as they provide the data distribution without more primary data needed.

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