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Journal : Journal of Applied Science, Engineering and Technology (J. ASET)

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.
Application of Failure Mode, Effect Analysis (FMEA) and Ishikawa Diagram in Determining the Damage Aspects and Maintenance Plan of Screw Feeder of Steam Power Plant Company Riszal, Akhmad; Yohanes, Eko; Risano, A Yudi Eka; Ibrahim, Fauzi; Saputra, Rizal Adi
Journal of Applied Science, Engineering and Technology Vol. 5 No. 1 (2025): June 2025
Publisher : INSTEP Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/jaset.v5i1.76

Abstract

The screw feeder is a commonly used material transporter in various industries due to its ability to move large quantities of material and operate for extended periods. In practice, unexpected damage often occurs, requiring component replacements outside regular maintenance schedules. To improve reliability, Ishikawa diagrams are used to identify root causes of damage, while the FMEA method helps analyze failure risks and schedule preventive maintenance. This study found that the main causes of screw coal feeder damage are human error, mechanical issues, materials, and methods. Recommended actions include regular inspections for wear and proper lubrication to maintain performance. Based on reliability analysis, the screw feeder leaf has a reliability rate of 95.3% with a mean time between failure (MTBF) of 116.66 hours. The casing has a reliability rate of 94.6% and an MTBF of 142.85 hours. Implementing Ishikawa and FMEA methods at PT XYZ’s coal-fired power plant (PLTU) enables more effective and planned maintenance. This approach minimizes unexpected breakdowns, improves component reliability, and ensures smoother operations.