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All Journal Jurnal Energi Dan Manufaktur International Journal of Aviation Science and Engineering Journal of Applied Science, Engineering and Technology (J. ASET)
Khairul Ummah
ITB
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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A Simple Fight Decision Support System for BVR Air Combat Using Fuzzy Logic Algorithm Khairul Ummah; Herlan Setiadi; Hisar Manongam Pasaribu; Dhani Anandito
AVIA Vol 1, No 1: (Dec, 2019)
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v1i1.8

Abstract

Beyond Visual Range (BVR) air combat is a future trend of war tactic. In this situation, a fighter can attack the opponent before direct encounter. Its complexity arises due to the necessity to take into account the information of target’s maneuver, the specification of the missile, and the advantage of fighter position. In this paper, a simple BVR air combat system has been developed to give a fight strategy for pilot. Some important parameters are considered, such as the distance and the azimuth position of the target’s as well as the range and the energy of missile to reach the target. The information is processed to determine the fighter supremacy and the opponent’s threat factor. The result of the processing is used as an input of fuzzy logic algorithm to determine the optimal fighting strategy. The feasibility of the model and validity of the algorithm are verified by simulation under two typical situations
Pengaruh waktu kontak terhadap kualitas sambungan hasil las gesek (Friction Welding) Magnesium AZ-31 Solihin Solihin; Irza Sukmana; Khairul Ummah
Jurnal Energi Dan Manufaktur Vol 10 No 1 (2017): April 2017
Publisher : Department of Mechanical Engineering, University of Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (930.835 KB)

Abstract

Abstrak: Pengelasan merupakan salah suatu proses penyambungan dua atau lebih bahan teknik, dengan atau tanpa peroses pencairan logam dasarnya. Teknologi Las Gesek (Friction Welding, FW) merupakan salah satu teknik pengelasan padat atau pengelasan tanpa proses pencairan (solid-state welding). Pembangkitan panas dalam proses FW dihasilkan dengan cara menggesekkan permukaan material las (base metal) hingga mencapai temperatur penyambungannya (semi-solid temperature) atau sekitar 80% dari temperature cair bahan, dan dalam hal Magnesium AZ31 adalah sekitar temperatur 5500C. Setelah bahan mencapai temperatur semi-solid tersebut, kemudian diberi tekanan agar terjadi proses penyambungan. Penelitian ini bertujuan untuk mengetahui pengaruh variasi proses terhadap kualitas hasil pengelasan gesek, yang meliputi: kekuatan tarik, struktur makro, dan nilai kekerasan bahan hasil las. Parameter pengujiannya adalah variasi waktu kontak las, yaitu selama 3, 5, dan 10 menit. Kecepatan putar spindle selama proses pengelasan ditetapkan 1400 rpm. Hasil pengelasan menunjukkan bahwa waktu kontak gesek 3 menit menghasilkan kekuatan tarik tertinggi (16,78 MPa), bila dibandingkan dengan dua parameter lain. Hasil uji keras pada daerah las (stir zone) menunjukkan angka kekerasan rata-rata yang relative konsisten, atau sebesar 60 HRE untuk semua parameter, sedangkan angka kekerasan rata-rata di daerah terpengaruh panas (heat affected zone, HAZ) untuk waktu kontak gesek 3, 5 dan 10 menit secara berturut-turut adalah sebesar 69,6; 64,6; dan 60,6 HRE. Hasil penelitian awal ini memberikan potensi studi lanjutan pada berbagai parameter pengelasan lain agar didapatkan kualitas sambungan las gesek yang optimum untuk proses pengelasan gesek Magnesium AZ-31. Kata Kunci: Las gesek, Magnesium AZ-31, struktur makro, cacat void. Abstract: Welding is a process technology aiming to join two or more materials. Friction Welding (FW) is including in a solid-state technology cluster, where the heat is resulted by the friction contact between two welding material’s surface. FW is usually using the lathe machine and the two weld materials were placed on fix- and rotated-tail stocks. The welding process start once the temperature reach about 80% of material’s melting temperature and in the case of Magnesium AZ-31 alloys was about 5500C. Afterwards, the rotated tailstock was push for joining the two materials. In this study, we have tested contact welding at 3, 5, and 10 minutes respectively on rotating speed of 1400rpm. In this study, friction weld of 3 min resulted the highest Tensile Strength, i.e., 16.78MPa of the weld material when compare to other parameters. Also, the hardness number at stir zone of welding parameter 3, 5, and 10minutes are almost the same, i.e., 60 HRE, while at the heat affected zone (HAZ) area were 69.6; 64.6; and 60.6 HRE respectively. This initial results show a potential further research for different friction welding parameters in order to find the optimum welding operational parameters in friction weld Magnesium AZ-31. Keywords: Friction Welding, Magnesium AZ-31, macro structure, void.
Atmospheric Corrosion of Galvanized Low-Carbon Steel at Rural, City, and Industrial area in Bandar Lampung Khairul Ummah; Abdul Azis Muslim; Irza Sukmana
Jurnal Energi Dan Manufaktur Vol 9 No 1 (2016): April 2016
Publisher : Department of Mechanical Engineering, University of Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (186.487 KB)

Abstract

Abstract:This research conducted to continue the previous study about atmospheric corrosion phenomenon on uncoated lowcarbon steel at Lampung Province, where it using coated low carbon steel. The atmospheric corrosion rate at LampungProvince are 152.910 g/m2/y at rural area, 267.593 g/m2/y at city, and 420,503 g/m2/y for industrial are. Based on ISOcategory, the atmospheric corrosion rate is C2 at rural, C3 at city and C4 at industrial area. Coating processes of lowcarbon steel can decreased the atmospheric corrosion rate about 172.023 g/m2/year or 39% at rural, and 91.746g/m2/year or 18% at industrial area.Keywords: Galvanized, low carbon steel, atmospheric corrosion, corrosion ratAbstrak:Penelitian ini untuk melengkapi studi terhadulu dimana dilakukan penelitian fenomena korosi atmosfer pada baja karbonrendah yang tidak dilapisi di Propinsi Lampung. Dalam penelitian ini digunakan baja karbon rendah yang telah dilapisi.Hasil penelitian ini menunjukkan bahwa laju korosi atmosfer di Propinsi Lampung untuk daeerah pedesaan adalah152,910 g/m2/y, perkotaan 267,593 g/m2/y, dan daerah industri 420,503 g/m2/y. Berdasarkan standar katagori ISO,maka laju korosi atmosfir untuk baja karbon rendah yang dilapisi di propinsi Lampung adalah pada kategori C2 dipedesaan, C3 di Kota, dan C4 di daerah industri. Pelapisan menurunkan angka laju korosi sebesar 172.023 g/m2/yearatau 39% untuk daerah perkotaan dan 91.746 g/m2/year atau 18% di daerah industri.Kata Kunci: Pelapisan galvanik, baja karbon rendah, korosi atmosfir, laju korosi.
Bird Detection System Design at The Airport Using Artificial Intelligence Khairul Ummah; Muhammad Fadly Hidayat; Denni Kurniawan; Zulhanif Zulhanif; Javensius Sembiring
AVIA Vol. 4, No. 2 (December 2022)
Publisher : 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 Khairul Ummah; M Thariq Hidayat; A Yudi Eka Risano
AVIA Vol. 4, No. 2 (December 2022)
Publisher : 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
Intelligent Eyes on the Battlefield: Developing an AI-Vision Based Military Vehicle and Infantry Detection System Wibowo, Pasha R A; Ummah , Khairul; Arifianto, Ony; Widagdo, Djarot; Riszal, Akhmad; Arif, Yanuar Zulardiansyah
Journal of Applied Science, Engineering and Technology Vol. 3 No. 2 (2023): December 2023
Publisher : INSTEP Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/jaset.v3i2.63

Abstract

The importance of accurate, real-time intelligence in modern warfare is crucial, especially in reconnaissance and surveillance operations. Currently, drones are widely used for reconnaissance, but generally rely only on the operator's ability to monitor operation targets. This research is aimed at developing an AI vision assistance system to enhance the ability to detect military vehicles and infantry. The method used is computer vision trained to recognize and differentiate several military objects. The YOLO model is used to detect and distinguish objects. To improve detection capabilities, the YOLO v8 model was retrained with an additional dataset sourced from battle recordings on the battlefield. The results show a detection accuracy rate of 95% in detecting vehicles and infantry under normal visual conditions. The model from this research can be used to enhance the capabilities of reconnaissance drones and the effectiveness of monitoring operations.
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

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.
Co-Authors A.Yudi Eka Risano Abdul Azis Muslim Arif, Yanuar Zulardiansyah Arifianto, Ony Denni Kurniawan Dhani Anandito DJAROT WIDAGDO, DJAROT Herlan Setiadi Huda, Zulmiftah Irza Sukmana Javensius Sembiring Javensius Sembiring, Javensius M Thariq Hidayat Muhammad Fadly Hidayat Pasaribu, Hisar Manongam Riszal, Akhmad Solihin Solihin Wibowo, Pasha R A Widyosekti, M. Dhiku Zulhanif Zulhanif