التفاصيل البيبلوغرافية
| العنوان: |
Data Mining for Quality Prediction in Software-as-A-Service Concept: A Case Study in Offset Printing Company. |
| المؤلفون: |
Arif, Fahmi1 fahmi.arif@itenas.ac.id, Ramadhan, Fadillah2 f.ramadhan@itenas.ac.id, Sayf, Wildan3 wildansayf@gmail.com |
| المصدر: |
Quality Innovation Prosperity / Kvalita Inovácia Prosperita. 2023, Vol. 27 Issue 3, p109-125. 17p. |
| مصطلحات موضوعية: |
*DATA mining, *SOFTWARE as a service, *MANUFACTURING defects, OFFSET printing, DATA quality, CLASSIFICATION algorithms |
| مستخلص: |
Purpose: This research aims to design a model of a quality prediction system using data mining methods in the software-as-a-service (SaaS) environment in order to facilitate manufacturers' ability to analyse process and product quality without software investment. Methodology/Approach: To develop the quality prediction model, this study utilised a data mining methodology to extract knowledge from historical data collected from the offset printing industry, focusing on manufacturing parameters. Four classification algorithms (Decision Tree, k-NN, Naive Bayes, Random Tree) were employed and compared to identify the most precise model specifically tailored for offset printing. Subsequently, the prediction model was integrated into a web-based system to enable quality prediction. Findings: This study demonstrates the practicality of integrating quality prediction into the SaaS framework, specifically for offset printing. This integration is designed to predict how manufacturing control parameters, such as printing slope angle, engine temperature, paper size, ink density, and roller speed, relate to the occurrence of product defects such as crumpled paper and imprecise printing. Research Limitation/implication: Generalizability is constrained by its focus on the offset printing industry, and prediction accuracy relies on historical data, which can vary across manufacturing sectors, affecting model performance. Originality/Value of paper: This article presents the concept and practical implementation of utilising data mining in a SaaS environment to enhance the quality of manufacturing, with a particular emphasis on the offset printing sector. [ABSTRACT FROM AUTHOR] |
|
Copyright of Quality Innovation Prosperity / Kvalita Inovácia Prosperita is the property of Technical University of Kosice, Faculty of Metallurgy, Department of Integrated Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |
Full Text Finder