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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri JISICOM (Journal of Information System, Infomatics and Computing) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Infortech Jurnal Manajemen Informatika Jayakarta Computer Science (CO-SCIENCE) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Accounting Information System
Dwi Yuni Utami
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Comparison of Neural Network Algorithms, Naive Bayes and Logistic Regression to predict diabetes Dwi Yuni Utami; Elah Nurlelah; Fuad Nur Hasan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5201

Abstract

Diabetes is a disease that affects many people with the characteristics of high blood sugar levels. The International Diabetic Federation (IDF) estimates the number of Indonesians aged 20 years and over, suffering from diabetes at 5.6 million people in 2001, and increasing to 8.2 million people in 2020. The problem that occurs is that many people do not know that they suffer from diabetes because they do not have basic knowledge about diabetes and the existing methods to detect diabetes are time consuming. In this study, three data mining methods were compared, namely the neural network algorithm, naïve Bayes, and logistic regression using the rapid miner application by applying the Confusion Matrix Evaluation (Accuracy) and the ROC Curve. The result of this research is that logistic regression method is a fairly good method in predicting early diagnosis of diabetes compared to the naïve Bayes method and the neural network. From the evaluation and validation, it is known that logistic regression has the highest accuracy and AUC values among the comparable methods, namely 75.78% and AUC 0.801, followed by the naïve Bayes algorithm which is 74.87% and AUC 0.799, and the neural network is 69.27% and AUC 0.736. has the lowest accuracy.
Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease Utami, Dwi Yuni; Nurlelah, Elah; Hikmah, Noer
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 4 No. 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i1.3793

Abstract

Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.
Co-Authors Ade Christian, Ade Ade Setiawan Ahmad Fauzi Ari Abdilah Ari Abdillah Badariatul Lailiah Baginda Oloan Lubis Elah Nurlelah Eri Riana, Eri Fauzi Ahmad Muda Firstianty Wahyuhening Fibriany Fuad Nur Hasan Hanggaro Aji Al Kautsar Hasan, Fuad Nur Hayatullah, Mohammad Rizky Hikmah, Noer Leliyanah Leliyanah Noer Hikmah Nurlaelatul Maulidah Nurlelah, Elah Rendi Simon Lesmana Riki Supriyadi Rizka, Muhammad Aditya Sari Dewi Sari Dewi Shidqi, Farsya Lisanush Vara, Regita Santo Wibowo, M.Dwi Saputra Yulianti Yulianti