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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem Informasi dan Informatika (SIMIKA)
Sumiati Sumiati
Serang Raya University
Computer Science & IT Control & Systems Engineering Engineering

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Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm Sumiati Sumiati; Viktor Vekky Ronald Repi; Penny Hendriyati; Anharudin Anharudin; Afrasim Yusta; Agung Triayudi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1128-1138

Abstract

The limitations of medical personnel, especially heart disease, cause difficulties in diagnosing heart disorders, so diagnosing heart disorders is not easy, it takes the ability and experience of a cardiologist who has the expertise and experience to be able to accurately diagnose heart disorders. Several studies in the field of computing have been carried out in diagnosing cardiac abnormalities in patients. This study was conducted to accurately test the results of the classification of heart disorders using electrocardiogram medical record data with a C.45 decision tree approach. The results showed that the classification of heart defects obtained a mean squared error (MSE) value of 0.24, a root mean squared error (RMSE) value of 0.49, and an accuracy value of 75.33% with the C4.5 algorithm.
DATA MINING APRIORI ALGORITHM TO ANALYZE BOOK RECOMMENDATIONS Sumiati Sumiati; Shella Wasilatul Hasanah
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4327

Abstract

This study focuses on the development of a book recommendation system in the Library using the Apriori algorithm. By utilizing this algorithm, the library can analyze book borrowing transaction data to find patterns of student interest, which will help librarians in determining which books need to be recommended. Through the application of the Apriori algorithm, the system successfully identified 17 association rules that show the relationship between books that are often borrowed together. These rules have a minimum support of 5.5% and a confidence of 100%, indicating that this pattern is very strong and reliable. With the results of this study, it is hoped that librarians can be more efficient in recommending books to students, improving the reading experience, and maximizing the use of library collections. In the future, this system can be expanded by integrating user data for more personalized recommendations.  
Co-Authors Afrasim Yusta Agung Triayudi Anharudin Anharudin Penny Hendriyati Repi, Viktor Vekky Ronald Shella Wasilatul Hasanah