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All Journal BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Mechanical and Manufacture
Muhtar Kosim
Universitas Subang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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BANTUAN TEKNIK DAN PERENCAAAN PEMBIAYAAN REVITALISIASI PEMBANGKIT LISTRIK TENAGA MIKRO HIDRO KAMPUNG BUNIKASIH DESA BUKANAGARA KECAMATAN CISALAK Novandri Tri Setioputro; Muhtar Kosim; Kasda Kasda; Sugeng Sutikno; Ari Ajibekti Masriwilaga
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1063.136 KB) | DOI: 10.31949/jb.v4i1.4097

Abstract

Bunikasih Rural community and the Faculty of Engineering at the University of Subang took the initiative to build the Bunikasih Micro Hydro Power Plant (MHP) in 2013, with financial support from CRS BNI Go Green. The Bunikasih MHP is situated in the Bunikasih area of Cupunagara Village, Cisalak District, Subang Regency, West Java Province, Indonesia. The Bunikasih MHP was initially utilized to supply electricity for the Bunikasih Rural community, which at the time was off grid. In 2018, an earthquake occurred therefore it made MHP Bunikasih malfunctioning. The Engineering Faculty team and the Bunikasih community undertook Focus Group Discussion/FGD activities prior to the MHP Bunikasih damage survey. The results of the FGD activities are the revitalization of the Bunikasih MHP so that it can operate again. It was also proposed that MHP Bunikasih is to be utilized to support the increase in the processing of agricultural production. The survey revealed that Bunikasih MHP suffered heavy damage. The channel was cracked and broken. The penstocks was bent. The roof of the turbine house was collapsed. The generator and electrical controls were fried. Damage to civil buildings can be repaired by making a new building structure in the damaged part. The bent pen stock is repaired by cutting the bent part and replacing it with a new pipe. A new roof is installed to the turbine house. The generator and control system are fixed and new components are installed. In general, all components of the Bunikasih MHP that were damaged can be repaired and revitalized. The cost for the revitalization is Rp. 213,700,000.00.
OPTIMIZATION OF PREDICTION AND PREVENTION OF DEFECTS ON METAL BASED ON AI USING VGG16 ARCHITECTURE muhtar kosim; Ari Wibowo; Novandri Tri Setioputro; Kasda; Dian Susanto
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.6542

Abstract

Manufacturing is one of the most valuable industries in the world, it can be automated without limits but still stuck in traditional manual and slow processes. Industry 4.0 is racing to define a new era in digital manufacturing through the implementation of Machine Learning methods. In this era, Machine learning has been widely applied to various fields and will certainly be very good applied in the manufacturing world. One of them is used to predict and prevent defects in metal. The process of predicting and preventing defects in metal is one of the important efforts in improving and maintaining production quality. Accuracy in predicting and preventing defects in metal can be an innovation and competitiveness in technology, both in production methods, and improving product safety and its users. Human operators and inspectors without digital assistance generally can spend a lot of time researching visual data, especially in high-volume production environments. For this reason, there needs to be research in developing Machine Learning technology in an effort to prevent the occurrence of defects in metal. And one of the development of this technology by using Convolutional Neural Network (CNN) architecture Visual Geometry Group 16 layer (VGG16). As for the metal defect dataset with 10 classes with details for training data as many as 17221, and test dataset as many as 4311, From the use of methods and datasets available, has been done training model used and produce very good accuracy, that is equal to 89% and testing with accuracy equal to 76%. And also done Interpreter process against new input data, to know metal defect type, prediction accuracy and appropriate action to prevent and overcome metal defect type result of Interpreter process application.
Co-Authors Ari Ajibekti Masriwilaga ari Wibowo dian susanto Kasda Kasda, Kasda Novandri Tri Setioputro Sugeng Sutikno