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All Journal Journal of Tropical Soils Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Ternak Veteriner ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Teknobiz : Jurnal Ilmiah Program Studi Magister Teknik Mesin Jurnal Pengabdian Masyarakat Teknik Indonesian Journal of Aerospace
Bambang Setiadi
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Veterinary

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High Pressure Transfer Pump Efficiency Calculation Before And After Reverse Engineering at PLTGU XYZ Setiadi, Bambang; Rahardja, Istianto Budhi; Sitompul, Josua Uli Syahputra; Sulistiyo, Eko
Teknobiz : Jurnal Ilmiah Program Studi Magister Teknik Mesin Vol. 15 No. 1 (2025): Teknobiz
Publisher : Magister Teknik Mesin Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/teknobiz.v15i1.8549

Abstract

Steam Gas Power Plant (PLTGU) is a power plant that uses gas as fuel and reuses exhaust gas from the gas turbine to heat the pipes in the HRSG (Heat Recovery Steam Generator) to produce steam and rotate the steam turbine. PLTGU XYZ is a Gas and Steam Power Plant which has 2,154 MW. One of the important components used in PLTGU is the high-pressure transfer pump. A high-pressure transfer pump is a pump that functions to move water from the low-pressure drum through the high pressure economizer to the high pressure drum, using an automatic system. The type of high-pressure transfer pump at PLTGU XYZ is Type MC100-300/9. To maintain the efficiency of the high-pressure transfer pump, components need to be maintained or replaced. high pressure transfer pump components have been replaced on the pump shaft using the reverse engineering method. Before reverse engineering the large pump shaft the efficiency was 66%, and after reverse engineering the large pump shaft the efficiency was 63%, so the comparison of pump efficiency before and after reverse engineering the pump shaft was 3%.
Implementation of Backprojection Algorithm for Synthetic Aperture Radar Image Processing on Low-Cost Hardware Platform Agus Wiyono; Chasanah, Nurul; Abner Hamonangan, Jefri; Ruhiyat, Abdurrasyid; Rohman, Abdul; Kurniawan, Farohaji; Muksin; Arief Aditya, Satria; Hendra Wahyudi, Agus; Rahayu, Novelita; Arisal, Andria; Setiadi, Bambang
Indonesian Journal of Aerospace Vol. 21 No. 2 (2023): Indonesian Journal Of Aerospace
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/ijoa.2023.2516

Abstract

This work presents the implementation of back-projection algorithm for Synthetic-Aperture Radar (SAR) signals on a low-cost, small, lightweight, and low-power consumption platform: Raspberry Pi. The algorithm is implemented with GNU Octave open-source software and the performance was tested on Raspberry Pi 3B and 4 hardware. For performance comparison, a single-threaded baseline implementation of back-projection is created and then modified to run on several threads on an available multicore processor. Executing a single-threaded code Raspberry PI is too slow for real-time imaging. However, the parallelized version shows computation improvement over the baseline version. We include a discussion of parallel implementation on a single Pi using Octave’s parallel package. This study contributes to the understanding of implementing SAR image processing on affordable single-board platforms with constrained computing resources.
Comparative Performance of U-Net CNN in Multi-Class Aircraft Segmentation and Classification Using Polygon and Bounding Box Annotations Sitanggang, Rivilyo Mangolat Rizky; Dani, Wa Ode Dianita Putri Suaiba; Setiadi, Bambang; Kuntjoro, Yanif Dwi
Indonesian Journal of Aerospace Vol. 23 No. 1 (2025): Indonesian Journal Of Aerospace
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/ijoa.2025.8155

Abstract

Recent advancements in deep learning have revolutionized image processingtasks such as segmentation and classification. This study investigates theperformance of U- Net-CNN models in multi-class aircraft segmentation andclassification using polygon and bounding box annotations. Military aircraftclassification is crucial for defense applications, as it aids in rapid and accuratedecision-making during critical missions. This study investigates howthese annotation methods affect training time, segmentation accuracy, andclassification performance in multi-class segmentation and classification tasksinvolving military aircraft. The research compares polygon and bounding boxmethods to evaluate their effectiveness in capturing object details and computationalefficiency. While polygon annotations achieved superior precision witha mean test accuracy of 0.987 and lower loss of 0.041, bounding boxes excelledin computational efficiency. Future research should expand datasets and exploreadditional annotation techniques to further generalize these findings.
Co-Authors Abdul Rohman Abner Hamonangan, Jefri ABNER, JEFRI Agus Suparyanto Agus Wiyono Andria Arisal, Andria Arief Aditya, Satria Basyir Rantawi , Azhar Dadang Gunawan Dani, Wa Ode Dianita Putri Suaiba Didi Budi Wiyono Eko Handiwirawan Endang Romjali Hendra Wahyudi, Agus K Diwyanto Kumbarasari, Shanti Kuntjoro, Yanif Dwi KURNIAWAN, FAROHAJI Kusuma Diwyanto Kuswandi . Leksi Siregar , Ahdiat Leo P Batubara M Doloksaribu M Rangkuti Muchji Martawidjaja Muksin Muslim, Ichwanul Nurul Chasanah Rahardja, Istianto Budhi Rahayu, Novelita RUHIYAT, ABDURRASYID Saparso Saparso Saputra , Hendra Sedu Kemie, Silvester Shiddieq, Dja’far Simo Eliaser Sitanggang, Rivilyo Mangolat Rizky Sitompul, Josua Uli Syahputra Subandriyo . Sulistiyo, Eko Tohari Tohari Zulbardi Muhammad