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All Journal Journal of Computer Networks, Architecture and High Performance Computing KLIK: Kajian Ilmiah Informatika dan Komputer
Agung Prabowo
Universitas Prima Indonesia
Computer Science & IT Education

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Komparasi Tingkat Akurasi Random Forest dan Decision Tree C4.5 Pada Klasifikasi Data Penyakit Infertilitas Agung Prabowo; Sumita Wardani; Rico Wijaya Dewantoro; Wilfredo Wesly; Leonardo
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1115

Abstract

Male fertility has declined over the past two decades. The decrease is due to environmental factors, such as lifestyle habits that can affect the quality of a man's sperm. Artificial intelligence technology is currently developing as a methodology for health decision support systems. In the process of predicting infertility can be done by applying Machine Learning technology. This study focuses on comparing the Random Forest classification method with Decision Tree C4.5 to see the level of accuracy in predicting the success of infertility data classification. Data for the Fertility Dataset was obtained from the UCI Machine Learning Repository with a total of 100 data records, 10 attributes and 2 attribute classes, namely Normal and Altered. The parameters used are age, childhood diseases, accidents or trauma, surgical operations, alcohol consumption and smoking habits. Then evaluate the testing of the two methods, namely by using 10fold Cross Validation. Based on the results of Random Forest and Decision Tree C4.5 testing, the average accuracy of Random Forest is 87.20% and Decision Tree C4.5 with an accuracy rate of 85.90%. From the results obtained, it can be concluded that Random Forest is a superior method by 1.3% when compared to Decision Tree C4.5 in predicting accuracy in the Fertility Dataset.
Diagnosis and Prediction of Chronic Kidney Disease Using a Stacked Generalization Approach Agung Prabowo; Sumita Wardani; Abdul Muis; Radiman Gea; Nathanael Atan Baskita Tarigan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3611

Abstract

Chronic Kidney Disease (CKD) is. In the past, several learners have been applied for prediction of CKD but there is still enough space to develop classi?ers with higher accuracy. The study utilizes chronic kidney disease dataset from UCI Machine Learning Repository. In this paper, individual approaches, viz., linear-SVM, kernel methods including polynomial, radial basis function, and sigmoid have been used while among ensembles majority voting and stacking strategies have been applied. Stacked Ensemble is based on various types of meta-learners such as C4.5, NB, k-NN, SMO, and logit-boost. The stacking approach with meta-learner Logit-Boost (ST-LB) achieves accuracy 98,50%, sensitivity 98,50%, false positive rate 20,00%, precision 98,50%, and F-measure 98,50% demonstrating that it is the best classi?er as compared to any of the individual and ensemble approaches
Co-Authors Abdul Muis Leonardo Nathanael Atan Baskita Tarigan Radiman Gea Rico Wijaya Dewantoro SUMITA WARDANI Sumita Wardani Wilfredo Wesly