Optimasi Algoritma C4.5 dan Naïve Bayes Berbasis Particle Swarm Optimization Untuk Diagnosa Penyakit Peradangan Hati

Amrin, Amrin; Pahlevi, Omar; Satriadi, Irawan

doi:10.31294/instk.v2i1.399

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Sistem pakar untuk mendiagnosa penyakit liver dirancang sebagai alat untuk mendiagnosa penyakit liver dengan basis pengetahuan. Pengetahuan dasar diperoleh dari beberapa sumber, yaitu melalui wawancara dan studi literatur [2]. Penelitian ini menerapkan sistem pakar menggunakan metode Naïve Bayes.…”

Section: Pendahuluanunclassified

Implementasi Naïve Bayes pada Sistem Pakar untuk Deteksi Dini Penyakit Liver

Asri,

Sanglise,

Suhendra

2024

INFORMAL

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Liver health is very important for the human body. The liver as an organ that has the main task of neutralizing toxins in the body makes toxins that have been entering through our bodies from food or the environment can be neutralized by the liver. Based on the results of interviews conducted online to dr. Felix who works at Bhayangkara Hospital, one of the problems that occurs in the medical world is that people who are not medically trained if they experience symptoms of liver disease do not understand the ways to overcome them or the solution. If ignored, liver problems can get worse and more difficult to treat. Therefore, to overcome these problems, development is carried out by creating an expert system that can detect liver disease early by implementing the Naïve Bayes Method on the expert system. The expert system was built using the Rapid Application Development (RAD) development method. By making a design consisting of use cases, activities, class diagrams, database design and user interface design. Implementation in this expert system uses the PHP programming language and MySQL database. After the system is successfully created, the system is tested using the blackbox testing method which shows that the functionality of the expert system is running well and testing the user acceptance test (UAT) by distributing questionnaires to respondents to get the percentage of feasibility of 76.7% with a suitable category for use

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Section: Pendahuluanunclassified

Implementasi Naïve Bayes pada Sistem Pakar untuk Deteksi Dini Penyakit Liver

Asri,

Sanglise,

Suhendra

2024

INFORMAL

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“…From a public health perspective, hepatitis can impose a significant burden on healthcare systems and society as a whole. (Pahlevi & Satriadi, 2021). The consequences of hepatitis can have significant impacts on a range of areas, including socio-economic development, public health, and life expectancy.…”

Section: Introductionmentioning

confidence: 99%

Correlated Naïve Bayes Algorithm to Determine Healing Rate of Hepatitis Patients

Yulhendri

Malabay

Kartini

2023

International Journal of Science, Technology & Management

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The Correlated Naïve Bayes Algorithm is a statistical learning method that has shown promise in predicting the healing rate of hepatitis patients. Hepatitis is a liver disease that can be chronic or acute and affects millions of people worldwide. The healing rate of patients with hepatitis can vary widely depending on various factors such as age, gender, and medical history. The Correlated Naïve Bayes Algorithm takes into account the correlations between the different attributes of patients and their healing rates, unlike the traditional Naïve Bayes Algorithm. This approach has been shown to improve the accuracy of predictions significantly. In this study, the Correlated Naïve Bayes Algorithm was applied to a dataset of hepatitis patients. The dataset contained information about patients' age, gender, medical history, and other attributes that might affect their healing rates. The algorithm was trained on this dataset to predict the healing rate of new patients. The results showed that the Correlated Naïve Bayes Algorithm achieved higher accuracy in predicting the healing rate of hepatitis patients compared to the traditional Naïve Bayes Algorithm. This suggests that the Correlated Naïve Bayes Algorithm could be a useful tool for healthcare professionals in predicting the healing rate of hepatitis patients, and ultimately improving their treatment and care. Furthermore, the study also investigated the importance of different attributes in predicting the healing rate of hepatitis patients. The results showed that age and medical history were the most important factors, followed by gender and other attributes. The findings of this study have significant implications for the medical community, as accurate prediction of healing rates can inform treatment decisions and improve patient outcomes. The Correlated Naïve Bayes Algorithm provides a powerful tool for healthcare professionals in predicting the healing rate of hepatitis patients, and could be extended to other medical conditions. However, it is important to note that the Correlated Naïve Bayes Algorithm has limitations, such as the assumption of independence between attributes. Therefore, future research should investigate alternative methods that can overcome these limitations and improve the accuracy of predictions further.

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Optimasi Algoritma C4.5 dan Naïve Bayes Berbasis Particle Swarm Optimization Untuk Diagnosa Penyakit Peradangan Hati

Cited by 2 publications

References 1 publication

Implementasi Naïve Bayes pada Sistem Pakar untuk Deteksi Dini Penyakit Liver

Implementasi Naïve Bayes pada Sistem Pakar untuk Deteksi Dini Penyakit Liver

Correlated Naïve Bayes Algorithm to Determine Healing Rate of Hepatitis Patients

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