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

WIJAYA, MUHAMMAD ARI and Yani, Irsyadi (2024) IMPLEMENTASI METODE RANDOM FOREST DALAM KLASIFIKASI KEKASARAN PERMUKAAN BAJA S45C PADA PROSES CNC MILLING. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Random Forest, Klasifikasi, Cnc Milling, Kekasaran Permukaan
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery
Divisions:	03-Faculty of Engineering > 21201-Mechanical Engineering (S1)
Depositing User:	Muhammad Ari Wijaya
Date Deposited:	20 Jun 2024 06:42
Last Modified:	20 Jun 2024 06:44
URI:	http://repository.unsri.ac.id/id/eprint/147277

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