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-, Amalia
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Decision Sciences, Operations Research & Management

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APPLICATION OF DATA MINING USING THE RANDOM FOREST METHOD TO PREDICT HEART DISEASE Felix, Felix; Sitanggang, Delima; Laia, Yonata; -, Amalia; Radhi, Muhammad; Barus, Ertina Sabarita
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4801

Abstract

A heart attack is when fatty deposits block the arteries. This causes symptoms such as shortness of breath and chest pain. In addition, obstructed blood flow to the heart can cause damage to the heart muscle. Heart attacks are still the highest cause of death in Indonesia to date. The problem today is that it is tough to predict and identify heart disease. The appropriate method needed to predict heart disease is the Random Forest method. This research aims to calculate the level of accuracy in predicting heart attacks. Based on research and data processing carried out by previous study by comparing two K-Neighbor algorithms, which produced an accuracy value of 83% and the Logistic Regression algorithm produced an accuracy value of 88% and it was found that the Random Forest algorithm had an accuracy of 86.88%. Thus, other algorithms are better at predicting heart attacks than the Random Forest algorithm. Keywords: Heart Attack, Random Forest, Prediction.
ANALYSIS OF CLASSIFICATION OF LUNG CANCER USING THE DECISION TREE CLASSIFIER METHOD Setiawan, Wendy; Banjarnahor, Jepri; Shandika , Muhammad Faja; -, Amalia; Radhi, Muhammad
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4136

Abstract

The International Agency for Research on Cancer (IARC) revealed staggering figures, with 19.3 million global cancer cases and 10 million related deaths in that year. Cancer, characterized by abnormal cell growth, can potentially be dangerous with the ability to metastasize. Notably, lung cancer is often detected in an advanced stage due to a lack of awareness and comprehensive medical assessment. Lung cancer usually presents with a late-stage diagnosis. From 60% to 85% of individuals diagnosed with lung cancer show a lack of awareness about their condition. Early diagnosis using an accurate classification method can significantly increase the success of lung cancer diagnosis. To improve predictions, Decision Tree Classifier method was used in lung cancer classification, resulting in a significant increase in accuracy. This study achieved a good level of accuracy, with an accuracy value of 95.16% at a max_depth model depth of 15, and tested in 40 experimental iterations. These results are expected to provide hope for progress in the classification of lung cancer.   Keywords: Lung, Cancer, Classification, Decision Tree
Co-Authors Banjarnahor, Jepri Barus, Ertina Sabarita Delima Sitanggang, Delima Felix Felix Setiawan, Wendy Shandika , Muhammad Faja Solly Aryza Yonata Laia