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International Journal of Aviation Science and Engineering
Published by Institut Teknologi Bandung
ISSN : 27215342     EISSN : 27156958     DOI : https://doi.org/10.47355/avia.v1i1.6
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Electrical & Electronics Engineering Mechanical Engineering Transportation
AVIA : International Journal of Aviation Science and Engineering is published by Faculty of Mechanical and Aerospace Engineering, FTMD Institut Teknologi Bandung, Indonesia - in cooperation with Faculty of Engineering, Universitas Lampung and Java Scientific Academy, Indonesia. International Journal of Aviation Science and Engineering aims to publish original research articles and critical review manuscript in the field of Aviation Science and Engineering as well as Aerospace and applied Mechanical Engineering. The topics are including, but limited to: aviation sciences and technology, aerospace engineering, aeronautics, defense system and engineering, safety and energy, mechanical engineering, aeronautics education and training, interdisciplinary engineering and applied sciences.
Arjuna Subject : Teknik (semua kategori) - Teknik Ruang Angkasa
Articles 66 Documents
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Predictive Maintenance for Aircraft Engine Using Machine Learning: Trends and Challenges Adryan, F A; Sastra, K W
International Journal of Aviation Science and Engineering - AVIA Vol. 3 No. 1: (June, 2021)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v3i1.45

Abstract

This article aims to prove that Machine Learning (ML) methods are effective for Predictive Maintenance (PdM) and to obtain other developing methods that suitable applied on PdM, especially for aircraft engine, and potential method that can apply on future research, and also compared between articles in International and Indonesia institution. Maintenance factors are important to prognostic the states of a machine. PdM is one of the factor strategies based on realtime data to diagnosis a failure of the machine through forecasting remaining useful life (RUL), especially on aircraft machine where the safety is priority due to enormous cost and human life. ML is the technique that accurately prediction through the data. Applied ML on PdM is the huge contribution for saving cost and human life guarantee of safety. This work provides the literature survey for recent research which trends and challenges on PdM of aircraft engine using ML that compared the research from international and Indonesia from 2016 to 2021. Result of this work shows that ML method, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are the best method to calculate PdM with more than 99% on rate accuracy, and low level of Indonesia institution research which focused on PdM on aircraft engine using ML
Effect of Production Method on the Mechanical Properties of Resin - Fiber S-GLASS Composite for the Rocket Nose Cone Application Tarkono, Tarkono; Sugiyanto, Sugiyanto; Riszal, Akhmad; Atmoko, Ignatius Bayu; Ibrahim, Fauzi; Djuansjah, Joy Rizki Pangestu
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.v4i2.69

Abstract

Composite materials are increasingly developing in industrial advances both for everyday life or technological applications in industry. Composite material is a combination of two or more different components. Composite materials have certain physical and mechanical properties that are better than the properties of each of their constituent components. This research has been analyzed to determine the effect of the method of making fiber composites s-glass matrix resin 100 as material nose cone rocket rx-450 by using the method of hand lay up and vacuum infusion. Making a nose cone is carried out in several stages which are quite complicated, starting with preparation master mole for print beginning until polishing compound molding release on molding as finishing. The results obtained from this study are by using the method vacuum infusion lighter compared with material results method hand lay-up because on method vacuum infusion resin can be removed from the laminate. Whereas on method hand layup infiltration resin in fiber not enough perfect and administration of resin that cannot be controlled so that it can affect the mass from product composite.
Systematic Comparison of Machine Learning Model Accuracy Value Between MobileNetV2 and XCeption Architecture in Waste Classification System Mulyani, Yessi; Kurniawan, Rian; Budi Wintoro, Puput; Komarudin, Muhammad; Mugahed Al-Rahmi, Waleed
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.v4i2.70

Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the MobileNetV2 architecture with the SGD optimizer and a learning rate of 0.1 has an accuracy of 86.07% and the model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
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

Abstract

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
Deep Learning Implementation on Aerial Flood Victim Detection System Ummah, Khairul; Hidayat, M Thariq; Risano, A Yudi Eka
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.v4i2.73

Abstract

Hydrometeorological hazard such as floods are considered as a regular natural disaster in Indonesia due to its frequent occurrence. To mitigate the risk, search and rescue operations need to be carried out immediately. The sheer magnitude of floods poses a major challenge for responders, and the emerging drone technology could help to alleviate the problem due to its deployment speed and coverage. Automation in drone technology has potential to improve its effectiveness. This paper explores the idea of human detection during floods using a computer vision approach. This approach utilizes a one stage detector model as detection speed is crucial in disaster management case. The dataset used for training consists of 200 labelled and negative images taken from drone point of view. This paper conducted 3 experiments to find out the difference in performance when the model was trained on flood and non-flood dataset, as well as the effect of image input size to the model’s performance. The first experiment was trained on non-flood dataset. The second experiment was trained on flood dataset, and the third experiment is the modified version of the second model. The results show that the model trained on flood dataset performed worse than non-flood counterparts with the non-flood mAP reached 90.80% while flood mAP reached 39.15%. In addition, the experiments also conclude that increasing the input size of image during training, will increase the detection performance of the model at the cost of FPS
Selection of the Use of Formwork in the Holiday Inn Bukit Randu Hotel Project Using the Fuzzy AHP Method Fajarviani, Sc. Elan Lida; Usman, Kristianto; Winarsih, Anita Lestari Condro
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.v4i2.74

Abstract

Along with the development of the construction world, formwork has also progressed from being assembled on site to being assembled first at the factory. In Indonesia, many types of formwork have been used, which each have their own advantages and disadvantages. In selecting the type of formwork used, many factors or criteria need to be considered. The purpose of this study is to determine the type of formwork that is relatively best for use in the Holiday Inn Bukit Randu Hotel Project by calculating the weight of the criteria, sub criteria, and also the alternatives used using the Fuzzy AHP Method. Based on the criteria and alternatives that have been compiled by the researcher, as well as the analysis carried out using the Fuzzy AHP method, it is known that metal (system) formwork is the relatively best formwork with the largest final weight of 43.6%, while semi-system formwork with a final weight of 24, 6% and conventional formwork by 31.8%. However, after being reviewed based on the cost aspect, the semi-system formwork is the relatively best formwork to be used in the Holiday Inn Bukit Randu Hotel Project.
Evaluation Of Swelling – Shrinkage Of Soil Stabilized With Cement and Matos Soil Stabilizer Saputra, Muhammad Dzulhaj
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.v5i1.79

Abstract

This study aims to investigate the effect of matos soil stabilizer on soil stabilized with cement. The study utilizes 5% Portland Composite Cement (PCC) ratio by weight of dry soil and matos soil stabilizer at 0%, 1%, 2%, 4%, and 8% by weight of Portland Composite Cement, with curing periods of 0 days, 7 days, and 14 days, focusing on the swelling and shrinkage characteristics. The best result showed in a mixture with a variation of 8% matos soil stabilizer under 14 days curing with a plasticity index 12,227% Swelling (CBR Soaked) 1,423, Swelling Pressure 1,469 kg/cm2 and free swelling index 16,667%. The test results indicate that the mixtures with varying amounts of matos soil stabilizer in the cement-treated soil with different curing periods can reduce soil swelling, as observed from the values of plasticity index, Swelling (soaked CBR (California Bearing Ratio)), swelling pressure, and free swelling index, which decrease progressively. Therefore, it can be concluded that increasing the amount of matos soil stabilizer and prolonging the curing time can reduce the swelling and shrinkage characteristics of the soil.
Simulation of the impact of Covid-19 outbreak for airport terminal operations at Sam Ratulangi International Airport Pandiangan, Melania Lidwina
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.v5i1.82

Abstract

Indonesian airport regulators have taken steps to keep their airports operational during the Covid-19 pandemic. Measures such as implementing physical distance, checking body temperature and checking health documents have affected the efficiency of the airport terminal. Covid-19 Crisis Management has made major operational adjustments. This action has been effectively carried out in situations where passenger transport has been limited due to regulatory agency flight restrictions. However, it is still unclear what the output will be when the demand normalizes. The purpose of this paper is to compare terminal operations before and after the occurrence of Covid-19. This paper evaluates the impact of Covid-19 measures on airport terminal performance using simulation tests of CAST 8 terminal simulation software. The model represents Sam Ratulangi International Airport (IATA: MDC) in Manado, Indonesia. This model covers the departure and the arrival areas. The flight schedule of the selected date taken from the flight radar website generates the passengers, the farewellers, and the meeters. IATA ADRM 11th Edition is the guidelines to determine the level of service. Adjustments for Covid-19 scenarios include number of passengers, physical distance, processing flow, and waiting time. The results show that the waiting time of each process has increased latency and at some point the latency exceeds the optimum service level. The simulation results allow local airport authorities to maintain a specified level of service at MDC airport.
Development of Anti-UAV System Using Visual Artificial Intelligence Sembiring, Javen; Prianggoro, Dimas; Saputra, Rizal Adi; Tarkono, Tarkono; Ummah, Khairul
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.v5i1.83

Abstract

Unmanned Aerial Vehicles (UAV) was first developed as a tool for military purposes. Due to the rapid growth in technology, UAVs are now used in various applications including civil needs. Of course, there are consequences for this where UAVs can be misused by irresponsible parties. One example is the use of UAVs in airport fields which can disrupt the airport operations and possibly become a serious threat towards security and safety of flights in the airport. This paper will discuss the artificial intelligence (AI) modeling to detect UAVs. This AI modeling is the first step in designing counter unmanned aerial system (C-UAS). UAV detection will use deep learning using YOLOv4 (single-stage detection) for optimal detection speed and accuracy. There are a total of 500 image data processed and used in two AI modeling experiments in this study. Gaussian blur filter is used to create dataset variations so that the training can be processed more efficiently and the model can detect better. The results shows that the training dataset that has been processed with gaussian blur (filtered dataset) increases the AI model’s detection performance in rainy and clear conditions. Therefore, the model trained using filtered datasets is more suitable for use in detecting UAV objects in anti-UAV systems.
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

Abstract

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.

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