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All Journal Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Vokasi
Radhi, Muhammad
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences

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Journal : Jusikom: Jurnal Sistem Informasi Ilmu Komputer

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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): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM PRIMA)
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.
Classification Of Egg Quality Using The K-Nearest Neighbor Algorithm In Machine Learning Marantika, Windy; Gultom, Putri Romian; Agustine, William; Sinuhaji, Tama Ulina br; Aisyah, Siti; Amalia, Amalia; Radhi, Muhammad
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM PRIMA)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5471

Abstract

In addition to meat, fish, and milk, one of the staple foods consumed by the community is chicken eggs. Egg quality assessment is separated into two categories: exterior (egg shell) and interior (egg contents). However, the evaluation method used in this investigation is focused on evaluating the external quality of eggs. Pre-processing, feature extraction, classification, and evaluation are steps taken in the image processing method used to classify chicken eggs. Classification methods that can be used include the K-Means Clustering and K-Nearest Neighbor (KNN) methods and improved KNN. Based on the findings in the study, the KNN improvisation method can be used to classify chicken egg quality, with a test accuracy value of 91.67%.  
Co-Authors -, Amalia Agustine, William Amalia Amalia Barus, Ertina Sabarita ciptadi, wahyudin Delima Sitanggang, Delima Felix Felix Gultom, Putri Romian Hamzah, Erwin Rizal Harimurti, Puspito Harimurti, R Puspito Marantika, Windy Neva Satyahadewi Sinuhaji, Tama Ulina br Siti Aisyah Yonata Laia, Yonata Yuliono, Agus