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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications International Journal of Artificial Intelligence Research Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control J I M P - Jurnal Informatika Merdeka Pasuruan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Multimedia DedikasiMU: Journal of Community Service
Farid Sukmana, Farid
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Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences

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Journal : International Journal of Artificial Intelligence Research

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Unsupervised Machine Learning Using Fp-Growth in Service and Maintenance Of Asset Management Rozi, Fahrur; Sukmana, Farid
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.436

Abstract

Preventive maintenance is one of effort in manufacturer industry to maintain an infrastructure that has an important thing in the industry. One of module that was provided from this system, like service and maintenance or Work Order (WO). This module has behavior data like brain jobs in human beings. Where is the data that record in memory used to learn to get a solution in the next experienced because the data WO be saved like data in the market basket analysis. The transaction data may be repeated in the next problem. So this data is interesting to be processed to get the best solution by involved it as machine learning like the recommended solution in the brain of human beings. This research will be focused on using fp-growth association rule as unsupervised machine learning to process data as a recommended solution for the technician. A different method like previous research using apriori algorithm. This research has a goal to prove the effect of minimum support with the result of decision support in fp-growth algorithm. The study shows the best condition of the result in this method is between 0.002 until 0.004 for minimum support, because the best precision ,recall, and accuration value more than 50% in that range of minimum support.
Co-Authors Adryan Firdaus Ardana, Savira Auliya Ayunda Kristari Bagus Firmansyah Bakti, Henny Dwi Desiyarotur Risykiyana Dwi Junianto Ervin Setyawan Ezra Amelia Fahrur Rozi Gading Tri Prameswari Hartono, Fabyo Aryasena Harunur Rosyid Khoirul Wafiq Mega Arisia Dewi Muhammad Iqbal Waliyuddiin Muhammad Kevin Bhaktiar Muhammad Nabil Adani Muhammad Yusril Bahtiar Muhyiddin, Sulthon Noviarani, Khurin In Pamungkas, Bian Dwi Pangki Suseno Puji Restiawan Rezal Ridlo Kurniawan Rochdianingrum, Widhi Ariestianti Rozi, Fahrur Sari, Yayak Kartika Wardatus Yudha Arya Prasaja