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Journal : Journal of Technology Research in Information System and Engineering

PERBANDINGAN METODE KLASIFIKASI RANDOM FOREST DAN SUPPORT VECTOR MACHINE TERHADAP DATASET RESIKO KANKER SERVIKS Binanto, Iwan; B, Jesly Putri Kristiani; Leokadja, Louisa
JTRISTE Vol 11 No 1 (2024): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/jtriste.v11i1.507

Abstract

Cervical cancer is a significant global health issue, representing a type of cancer that develops from the cells of the cervix. This research focuses on comparing the effectiveness of two classification methods, namely Random Forest (RF) and Support Vector Machine (SVM), in assessing the risk of cervical cancer. Utilizing relevant datasets, the study aims to identify the strengths and weaknesses of each method and evaluate their ability to provide predictions of cervical cancer risk. Through comparative analysis, it is anticipated that this research will offer valuable insights for the development of more efficient methods for assessing the risk of cervical cancer. The results of this study are expected to contribute to a deeper understanding of the performance comparison between Random Forest and SVM in the context of assessing the risk of cervical cancer, opening opportunities for the optimal application of classification methods in efforts for the prevention and early detection of this disease.
PERBANDINGAN ALGORITMA RANDOM FOREST DAN ARTIFICIAL NEURAL NETWORK UNTUK DATASET WATER POTABILITY Binanto, Iwan; Mali, M. Rizky Fajar; N, Basilius Arilla Dimas; Jaya, Ajitama
JTRISTE Vol 11 No 1 (2024): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/jtriste.v11i1.510

Abstract

Water is an important element that is a basic need for the survival of all living things. Currently, public awareness about the importance of good quality water is increasing. This is due to a wider understanding of the health impacts of unclean water. Water that is clean and safe to use not only has a positive impact on our health, but also on various aspects of daily life. Therefore, research on the quality and suitability of water for consumption is very important. The aim of this research is to determine the best method for comparing water quality for suitability for consumption. This research compares two machine learning methods, namely Random Forest and Artificial Neural Network (ANN) based on attributes for the suitability of drinking water, namely: PH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, turbidity and potability. The research results show that the Random Forest algorithm has an accuracy rate of 67.823%, while the Artificial Neural Network (ANN) algorithm achieves an accuracy of 61.014%. From these results, it can be concluded that the Random Forest algorithm has higher accuracy compared to Artificial Neural Network (ANN).