Junior Class Preparedness Classification Faces A National Exam Using A C.45 Algorithm With A Particle Swarm Optimization Approach

Abstract

These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage

Performance Analysis of Tree-Based Algorithms in Predicting Employee Attrition

“…C45 can also achieve an accuracy value of 88.75% with an AUC value of 0.744 and testing on applications made in BCA customer satisfaction [24] . The application of the C45 algorithm can be optimized, such as the addition of swarm particle optimization which achieves an accuracy of 97.13% in measuring the readiness of junior high school students to face the national exam [25] . Another result of the C45 algorithm in predicting customer or customer loyalty is reflected in 2 similar results, namely the C.45 algorithm has good accuracy [26] compared to other algorithms such as Naïve Bayes [27] , although in certain cases the results are sometimes contradictory, the resulting accuracy is still relatively good [28] .…”