CLASSIFICATION OF PRODUCTION MACHINE SPARE PART STOCK DATA REQUEST NEEDS USING THE K-NEAREST NEIGHBOR METHOD

Yansyah, Hamdi (2024) CLASSIFICATION OF PRODUCTION MACHINE SPARE PART STOCK DATA REQUEST NEEDS USING THE K-NEAREST NEIGHBOR METHOD. Sarjana (S1) thesis, Universitas Pelita Bangsa.

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Official URL: https://doi.org/10.35870/ijsecs.v3i3.1878

Abstract

Spare parts are anything that can be offered, owned, used or consumed so that it can satisfy consumer wants and needs. Implementing the K-Nearest Neighbor algorithm model on testing data of 100 data objects, obtained results that showed new insights in the form of classification. Meanwhile, by using the stages of the model evaluation process with Cross Validation on training and testing data,
namely 1000 dataset records which have 36 critical and 64 non-critical results. Performance evaluation and testing using the RapidMiner Studio application is able to provide optimal results according to the scenario which is modeled. This algorithm model has an accuracy value of 98.00% with a standard deviation of +/- 4.00%

Item Type:	Thesis (TA, Skripsi, Tesis, Disertasi,Jurnal Skripsi dan Laporan KKP/PKL) (Sarjana (S1))
Keywords / Kata Kunci:	Sparepart, Mesin, Produk, Data Mining, K-NN, Klasifikasi, Spare parts, Machines, Products, Data Mining, K-NN, Classification
Subjects:	Teknik Informatika > Analisa dan Perancangan Prediksi Teknik Informatika > Data Mining Teknik Informatika > Sistem Informasi Teknik Informatika
Fakultas / Prodi:	Fakultas Teknik > S1 Teknik Informatika
Depositing User:	Hamdi Yansyah
Date Deposited:	12 Feb 2024 11:55
Last Modified:	12 Feb 2024 11:55
URI:	https://repository.pelitabangsa.ac.id/id/eprint/128

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