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All Journal International Journal of Computer and Information System (IJCIS) International Journal of Aviation Science and Engineering Journal of Innovation and Technology Journal of Applied Science, Engineering and Technology (J. ASET)
Arif, Yanuar Zulardiansyah
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Analysis Of Cloud Computing Usage In Vocational School Environment Using Delone MClean Wasiran, Wasiran; Setiawan, Antonius Darma; Arif, Yanuar Zulardiansyah
International Journal of Computer and Information System (IJCIS) Vol 3, No 4 (2022): IJCIS : Vol 3 - Issue 4 - 2022
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v3i4.93

Abstract

Data is very important. In today's era, any activity will involve data. Likewise, schools of course have large piles of data. A lot of data needs to be stored so that it can be managed better, the storage certainly requires a computer system in the form of a server. The server itself has several types that can be used, one of which is a local server and a cloud server. Of the several types of servers, of course, there are many variants of servers that are applied by agencies, especially in this case educational institutions or schools. In this study, several secondary schools were taken to compare which one had a higher success rate of implementation. The analysis was carried out using the Delone MClean method. The result is that the implementation of local servers has a higher success rate than cloud servers.
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.
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.
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; Sadono, Mahardi
Jurnal Inovasi Teknologi Vol 5 No 1 (2024): April
Publisher : Engineering Forum of Western Indonesian Government Universities Board (Forum Teknik, BKS-PTN Wilayah Barat) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors Arifianto, Ony DJAROT WIDAGDO, DJAROT Huda, Zulmiftah Javensius Sembiring, Javensius Khairul Ummah Mahardi Sadono, Mahardi Riszal, Akhmad Rizal Adi Saputra Setiawan, Antonius Darma Ummah , Khairul Wasiran, Wasiran Wibowo, Pasha R A Widyosekti, M. Dhiku