Skripsi

PENERAPAN NAÏVE BAYES UNTUK KLASIFIKASI KEKASARAN PERMUKAAN BAJA S45C PADA PROSES CNC MILLING

---

Penilaian

---

Surface roughness is one of the key quality parameters in machining that affects the performance and longevity of the produced components. This study examines the application of the Naïve Bayes method to classify or group the surface roughness of S45C steel in the CNC milling process. The Naïve Bayes method, known for its simplicity and effectiveness in solving classification problems, is used to predict surface roughness levels based on various machining parameters such as cutting speed, feed rate, and depth of cut. The Naïve Bayes model is trained using training data and evaluated with test data to measure the accuracy and consistency of the classification or grouping. The results indicate that the Naïve Bayes method can produce an accurate and reliable classification model for predicting the surface roughness of workpieces, thereby helping to improve quality control and process efficiency in CNC milling operations. Keywords: surface roughness, naïve bayes, face milling CNC

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003759	T147026	T1470262024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1470262024

Publisher

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

Collation

xii, 51 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

621.907

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Machine Learning
Prodi Teknik Mesin

Specific Detail Info

-

Statement of Responsibility

KA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment