APLIKASI METODE RANDOM FOREST CLASSIFIER PADA NILAI KEKASARAN PERMUKAAN BAJA S45C PROSES MILLING CNC

ERSI, SULTAN ISMAIL and Yani, Irsyadi (2024) APLIKASI METODE RANDOM FOREST CLASSIFIER PADA NILAI KEKASARAN PERMUKAAN BAJA S45C PROSES MILLING CNC. Undergraduate thesis, Sriwijaya University.

	Text RAMA_21201_03051282025042.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_21201_03051282025042_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_21201_03051282025042_0025127104_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (781kB)
	Text RAMA_21201_03051282025042_0025127104_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (539kB) | Request a copy
	Text RAMA_21201_03051282025042_0025127104_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (404kB) | Request a copy
	Text RAMA_21201_03051282025042_0025127104_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (897kB) | Request a copy
	Text RAMA_21201_03051282025042_0025127104_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (72kB) | Request a copy
	Text RAMA_21201_03051282025042_0025127104_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (82kB) | Request a copy
	Text RAMA_21201_03051282025042_0025127104_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy

Abstract

Artificial Intelligence (AI) is a program designed to enable machines to think like humans. The utilization of AI by industry is not limited to the telecommunications sector but extends to the manufacturing field, where machine learning is employed in milling machines to classify surface roughness classes. This is particularly done for the Ra value, which is the industry standard for surface roughness used to determine the quality of a product. This research aims to apply the random forest machine learning method to classify surface roughness values obtained through the CNC milling process on S45C steel. In this study, the Random Forest method is used to facilitate the determination of surface roughness value groups based on Ra values. The accuracy calculation is conducted by comparing the real class groups with the predicted ones to determine the correctness of the class group predictions. The research utilizes the milling process by varying several parameters such as cutting speed, feed rate, and axial depth. After the workpiece is milled, the surface roughness value is measured. The classification will be carried out using Python software, where the classification is based on surface roughness value classes that have been grouped into four categories. Classification using Python is conducted to create training and test data, aiming to visualize the comparison graph between real and predicted values and to display the classification results based on coding. xx This research also employs manual calculations in the classification of surface roughness class groups, which yield accuracy values that meet standards. However, for Vc "75", entropy and information gain need to be recalculated to establish an accurate node in explaining the decision tree. The Vc "75" node cannot determine the Ra class group classification due to insufficient data, resulting in a calculation of 0 or equal values between the Fz and Ax attributes. The lack of data in the four class groups is a result of the inherent limitation of the random forest method, which requires a dataset rich in information. The calculations show that parameters with variable Vc 75 and Fz 0.05 have two decision outcomes in Ra1 and Ra4 classes, whereas variable Vc 75 and Ax 1.25 have one decision tree in Ra1 class. When all three variables are considered, the classification decision is Ra1, thus a decision tree can be constructed as shown below. Finally, accuracy comparison is conducted by printing the prediction results to determine whether the predicted class groups match the real class groups. This comparison uses variations in the number of trees: 10, 20, 30, 80, and 100, with a data train-test split of 60% and 40% respectively. The results indicate that the best prediction accuracy is 9 out of 12 test data, yielding an accuracy rate of 83.3% with 20 trees. Keywords: random forest, classification, cnc milling, surface roughness

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	random forest, classification, CNC milling, surface roughness
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:	Sultan Ismail Ersi
Date Deposited:	24 Jun 2024 04:28
Last Modified:	24 Jun 2024 04:28
URI:	http://repository.unsri.ac.id/id/eprint/147710

Actions (login required)

View Item