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All Journal POSITIF "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika
Sitorus, Dina Suzzete
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Computer Science & IT

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Hepatitis Disease Diagnosis Expert System Using Certainty Factor Method: Hepatitis Disease Diagnosis Expert System Using Certainty Factor Method Sitorus, Dina Suzzete; Desiani, Anita
Jurnal Mahasiswa Teknik Informatika Vol. 3 No. 1 (2024): Jurnal Jamastika Vol.3 No.1 April 2024
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/jamastika.v3i1.3064

Abstract

The liver is the largest visceral organ in the body with important roles such as hormone production, immunity, protein metabolism, and more. This visceral organ can also be affected by various diseases such as hepatitis. Hepatitis is an inflammatory disease of the liver caused by a virus. Hepatitis has five types of disease, namely Hepatitis A, Hepatitis B, Hepatitis C, Hepatitis D, and Hepatitis E. The types of hepatitis that have the most cases in Indonesia are Hepatitis A, Hepatitis B, and Hepatitis C. Hepatitis occurs due to a sedentary lifestyle. Hepatitis occurs due to unhealthy lifestyles and delays in treatment due to the patient's lack of knowledge about hepatitis. If hepatitis is not cured early, it can cause other diseases such as chronic liver and can also result in death, therefore this study aims to design an expert system that can diagnose hepatitis disease. This expert system design uses the certainty factor (CF) method. The certainty factor (CF) method is used because it can help and facilitate diagnosing hepatitis disease with a certainty value. The certainty value can be presented with a range of values from 0 to 100. This research produces an accuracy value of 80%, therefore this expert system is effective for measuring certainty in diagnosing hepatitis disease early.
PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN NAÏVE BAYES DALAM KLASIFIKASI PENYAKIT DIABETES Desiani, Anita; Dewi, Novi Rustiana; Arhami, Muhammad; Sitorus, Dina Suzzete; Rahmadita, Suristhia
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 10 No 1 (2024): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v10i1.2092

Abstract

High levels of sugar in the blood can cause diabetes. The longer people are unable to control glucose in their blood, the more complications it can cause, other diseases and even death. Early detection of diabetes is needed, one way is by carrying out data mining classification. Data mining classification in this research uses two algorithms, namely SVM (Support Vector Machine) and Naïve Bayes. This research compares the two algorithms using two methods, namely training split and k-fold cross validation which aims to get the best classification results in detecting diabetes. The best classification results are determined by calculating the average value of precision, recall and accuracy. Based on this research, the SVM algorithm with split percentage training produces average values for precision, recall and accuracy, namely 77%, 71.5%, 77.27%, while the SVM algorithm with k-fold cross validation produces average values for precision, recall , and accuracy is 77%, 72.5%, 71%. The Naïve Bayes algorithm with the split percentage training method produces average values for precision, recall and accuracy, namely 75.5%, 74.5%, 79%, while the Naïve Bayes algorithm with k-fold cross validation produces average values for precision, recall, and accuracy of 75.5%, 74.5%, 75%. The best classification result in detecting diabetes is the Naïve Bayes algorithm, the split percentage method, which provides the best accuracy, precision and recall values above 74%.
Co-Authors Anita Desiani Arhami, Muhammad Novi Rustiana Dewi Rahmadita, Suristhia