KLASIFIKASI TINGKAT KEKASARAN PERMUKAAN BAJA MENGGUNAKAN REGRESI LOGISTIK MULTINOMIAL DENGAN DISKRITISASI FUZZY

AMMALIA, ATIKA PUTERI and Zayanti, Des Alwine and Resti, Yulia (2025) KLASIFIKASI TINGKAT KEKASARAN PERMUKAAN BAJA MENGGUNAKAN REGRESI LOGISTIK MULTINOMIAL DENGAN DISKRITISASI FUZZY. Undergraduate thesis, Sriwijaya University.

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Abstract

The manufacturing sector largely depends on machining operations, such as milling, which produces workpiece surfaces with a certain degree of roughness. Surface roughness is an important parameter for evaluating the quality of machining results. The purpose of this study is to classify the degree of steel surface roughness using the multinomial logistic regression method. The initial data in this study consists of numerical data comprising nine independent variables, namely cutting speed, feed rate, axial cutting depth, and surface roughness from point 1 to point 6, which were discretised into categorical data based on a combination of fuzzy membership curves (linear increasing, linear decreasing, and beta bell) and one dependent variable in the form of a label. A confusion matrix was used to evaluate the model's performance based on accuracy, precision, recall, specificity, and F1 score values. The multinomial logistic regression model with fuzzy discretisation produced an accuracy value of 88.33%, precision of 40.26%, recall of 41.68%, specificity of 91.27%, and an F1 score of 40.96%. This study concludes that the multinomial logistic regression method is quite effective in classifying the overall surface roughness of steel.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Regresi Logistik Multinomial, diskritisasi fuzzy, kekasaran permukaan baja, klasifikasi
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.M94 Experimental design, Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Atika Puteri Ammalia
Date Deposited:	24 Jul 2025 07:49
Last Modified:	24 Jul 2025 07:49
URI:	http://repository.unsri.ac.id/id/eprint/180353

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