Skripsi

IMPLEMENTASI METODE RANDOM FOREST DALAM KLASIFIKASI KEKASARAN PERMUKAAN BAJA S45C PADA PROSES CNC MILLING

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Penilaian

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Product quality is often associated with surface roughness values. Surface roughness can determine whether a product can meet quality standards or not. It is important to determine the right and optimal parameters, so as to achieve good surface roughness quality. Therefore, to improve the effectiveness of machining performance, a method is needed that can optimize the results of roughness quality, namely by predicting surface roughness classification using machine learning. In this research, the random forest method will be used in the classification. The dataset used is 30 data with 3 independent variables namely cutting speed, feeding motion per tooth, depth of cut and Ra as the dependent variable. Then the classification process will use 5 variations of split data and 3 variations of the number of trees. The highest accuracy is found in 75% split data: 25% with 60 trees, with an Accuracy value of 88%, then has a Precision value of 80%, Recall of 100%, and F1-Score of 89%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003772	T147277	T1472772024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1472772024

Publisher

Indralaya : Prodi Teknik Mesin, Fakultas Teknik Universitas Sriwijaya., 2024

Collation

xx, 50 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

621.907

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Mesin
Random Forest

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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