STEEL BOX GIRDER BRIDGE COMPONENT TRACEABILITY SYSTEM USING TREE STRUCTURE DIAGRAM AT PT BUKAKA TEKNIK UTAMA

The International Organization for Standardization (ISO) through ISO 9001:2015requires every product to have product traceability. In response to these challenges, PT Bukaka Teknik Utama developes the Traceability System in the Steel Box Girder Bridge products. Traceability System built by adopting Tree Structure Diagram Concept to describe production system process currently runs. The production process start from identify raw material, cutting process, sub-assembly process, and assembly process. This concept is then translated into Relational Database by applying Parent-Child Concept. The result of this Traceability System is the system able to issue a list of product traceability including raw material information, sub-contractor/employee who work on them, etc, quickly and accurately. System testing was carried out using the black box method, where of the 37 items tested all functioned properly. Tests were also carried out to determine the accuracy and speed of the system compared to the manual method. Of the 10 tests carried out, the system traceability is exactly the same as the manual method with an average processing time of 3 seconds, compared to the manual method, which is 97.6 seconds

The Combination of C4.5 with Particle Swarm Optimization in Classification of Class for Mental Retardation Students

Mental retardation or brain weakness is a condition of children who experience mental disorders. There are several characteristics to know the child has mental retardation. When entering a school, teachers are expected to be able to determine the right class for mental retardation students according to their category. Data mining is the process of finding patterns in selected data using artificial intelligence and machine learning. Algorithm C4.5 is one of the classification techniques in data mining. C4.5 can be used to create decision trees and classify data that has numeric, continuous, and categorical attributes. But C4.5 has the disadvantage of reading large amounts of data and cannot rank every alternative. PSO is an optimization algorithm for feature selection that can improve performance in data classification. Therefore, this study proposes an algorithm that can overcome the weaknesses of C4.5 by combining PSO. This study aims to classify a class of new mental retardation students using a combination of C4.5 as a classification and PSO as a feature selection to determine the attributes that affect the level of accuracy. The contribution of this research is to make it easier for the school to determine the new class of mental retardation students so that it is appropriate and according to their needs. The classification process in this study uses a combination of C4.5 and PSO. The validation used in this model is 10-fold cross-validation, and the evaluation uses a confusion matrix. This study resulted in an accuracy of C4.5 before using PSO of 91%. While the accuracy of C4.5 uses a PSO of 93%. Of the 20 attributes, there are 6 attributes that affect the level of accuracy. This study shows that PSO can be used to implement feature selection and increase the accuracy value of C4.5 by 2%

System software model based on Learning Analytics techniques as a tool to support decision making at public educational institutions

Modelo De Un Sistema De Software Basado En Las Técnicas De Learning Analytics Como Herramienta De Apoyo En La Toma De Decisiones Académico-Administrativas En Las Instituciones Públicas De Educación Superior.The latest attention that Learning Analytics has received, in part is because it encounters hidden patterns in educational data that once is processed and integrated into a specific case of use, offers to stakeholders assistance to improve their task into educational context. Due to it highly application, Learning Analytics techniques are integrated into a Decision Support System proposed to help administrators to develop decision making process and collaterally enhance student performance. In this paper, is defined a Decision Support System and how it has been use in educational institutions; is presented the functionalities of Learning Analytics and presented a survey of how can be integrated to produce a Learning Decision Support System to help faculty or department administrators to improve their decision impact in the academic community.cid