Reduksi False Positive Pada Klasifikasi Job Placement dengan Hybrid Random Forest dan Auto Encoder
- M. Riza Pahlevi Universitas Dinamika Bangsa, Jambi, Indonesia
-
Errissya Rasywir
*
Universitas Dinamika Bangsa, Jambi,
Indonesia
- Yovi Pratama Universitas Dinamika Bangsa, Jambi, Indonesia
- Marrylinteri Istoningtyas Universitas Dinamika Bangsa, Jambi, Indonesia
- Fachruddin Fachruddin Universitas Dinamika Bangsa, Jambi, Indonesia
- Muhammad Yaasin Universitas Dinamika Bangsa, Jambi, Indonesia (*) Corresponding Author
Abstract
The False Positive (FP) interpretation shows a negative prediction result and is a type 1 error answer with an incorrect positive prediction result. Based on this, we try to reduce type 1 errors to increase the accuracy value of the classification results. A low FP rate is critical for the use of Computer Aided Detection (CAD) systems. In this research proposal, to reduce FP, we use a Random Forest (RF) evaluation result design which will be reinterpreted by the Auto Encoder (AE) algorithm. The RF algorithm was chosen because it is a type of ensemble learning that can optimize accuracy in parallel. RF was chosen because it performs bagging on all Decision Tree (DT) outputs used. To suppress TP reduction more strongly, we use the Auto Encoder (AE) algorithm to reprocess the class bagging results from RF into input in the AE layer. AE uses reconstruction errors, which in this case is Job Placement classification. From the test results, it was found that combining the use of a random forest using C4.5 as a decision tree with an Autoencoder can increase accuracy in the Job Placement Classification task by a difference of 0.004652 better than without combining it with an autoencoder. Apart from that, in testing using a combination of RF and AE, fewer False Positive (FP) values were produced, namely 11 items in the Cross Validation-5 (CV-5) Test, then 13 items in the Cross Validation-10 (CV-10) test and in testing split training data of 60%, the FP was only 12. This value is less than the false positives produced by testing without Autoencoder, namely 12 items on CV-5, 15 items on CV-10, and 13 on split training data
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Copyright (c) 2024 M. Riza Pahlevi, Errissya Rasywir, Yovi Pratama, Marrylinteri Istoningtyas, Fachruddin Fachruddin, Muhammad Yaasin
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