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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) Building of Informatics, Technology and Science
Muntari, Siti
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Computer Science & IT Control & Systems Engineering Engineering Other

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Sistem Pakar Diagnosa Penyakit Pada Perokok menggunakan Metode Teorema Naive Bayes Muntari, Siti; Febriansyah, Febriansyah
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (843.522 KB) | DOI: 10.47065/bits.v3i4.1196

Abstract

The purpose of this research is to produce an expert system for diagnosing disease in smokers using the Naïve Bayes Theorem method. The problem that arises in this study is the process of determining the disease in smokers through the diagnosis of experts, patients must come to the hospital and see a doctor during working hours. In the development of this expert system using the waterfall SDLC method with the stages of Analysis, Design, Coding, Testing, and testing methods carried out in this Blackbox research. To determine the type of smoker's disease, this system uses the PHP MySQL Database programming language, and Dreamweaver uses. The results of this study are in the form of a website-based expert system that is able to help users or the public in diagnosing passive smoking and providing formations about smoking diseases. The results of the Blackbox Testing test have an average score of 4.2 with a valid category
PENGELOMPOKAN TINGKAT RESIKO PENYAKIT JANTUNG BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS rahmadayanti, fitria; Muntari, Siti; Putriani, Resti
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 8 No 2 (2023): JUTIM (Jurnal Teknik Informatika Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v8i2.2108

Abstract

Heart disease is one of the non-communicable diseases that can cause death, This disease occurs due to a narrowing of the blood vessels so as to cause impaired heart function Some of the causes of heart disease are one of them based on age, basically heart disease can be prevented by various factors including a healthy lifestyle, besides that early detection of heart disease is also needed to prevent death in sufferers One way to do early detection is to use data mining. The use of the k-means algorithm can be done to cluster the grouping of heart diseases by age to find out someone is exposed to the cause of high and low heart disease. Based on these problems, this study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method with several stages such as Business Understanding, Data Understanding, Data preparation, Modeling, Evaluation, and Deployment. The clustering method with the k-means algorithm in this study shows a new insight, namely grouping the risk level of heart disease based on 3 clusters. Cluster 0 is an age category with a fairly low risk level of heart disease or Low, which is 355 out of 1025 age categories tested, then cluster 1 is an age category with a moderate or Medium heart disease risk level, which is 208 out of 1025 age categories tested, and finally cluster 2 is an age category with a fairly high age category or High, which is 462 out of 1025 age categories tested.
Co-Authors Febriansyah Febriansyah Putriani, Resti Rahmadayanti, Fitria