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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Statistika Jurnal Pendidikan Matematika RAFA Science and Technology Indonesia Jurnal Penelitian Sains Jurnal Pengabdian Pada Masyarakat Seminar Nasional Lahan Suboptimal M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Journal of Engineering and Scientific Research (JESR) Indonesian Journal of Engineering and Science (IJES) Inovasi Matematika (Inomatika) Bulletin of Applied Mathematics and Mathematics Education Jurnal Pelita Sriwijaya Prosiding Seminar Nasional Unimus Mathematics Education Journal Journal on Mathematics Education
Yulia Resti
Jurusan Matematika Fakultas MIPA Universitas Sriwijaya,Jl. Raya Palembang-Prabumulih Km.32 Inderalaya 30662, Ogan Ilir, SumateraSelatan
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Journal : Inovasi Matematika (Inomatika)

 1 
Coronary Artery Disease Prediction Using Decision Trees and Multinomial Naïve Bayes with k-Fold Cross Validation Endang S Kresnawati; Yulia Resti; Bambang Suprihatin; M. Rendy Kurniawan; Widya Ayu Amanda
Jurnal Inovasi Matematika Vol 3 No 2 (2021): Inovasi Matematika (Inomatika)
Publisher : Pendidikan Matematika Universitas Muhammadiyah Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1226.478 KB) | DOI: 10.35438/inomatika.v3i2.266

Abstract

Coronary artery disease has been the leading cause of death in the world population for at least two decades (2000-2019) and has experienced the largest increase in mortality in that time span compared to other causes of death. The success of predicting coronary artery disease early based on medical data is not only beneficial for patients, but also beneficial for the stability of the country's economy. This paper discusses the prediction of coronary artery disease risk by implementing two statistical learning methods, namely Multinomial Naïve Bayes and Decision Tree with 10-fold cross validation, where numerical variables are discretized to obtain categorical variables. The results showed that the Decision Tree method has better performance than the Multinomial Naïve Bayes method in predicting coronary artery disease. The performance measure of the Decision Tree method obtained an accuracy rate of 99.63%, 100% sensitivity, 99.33% specificity, 99.23% precision, and 100% Negative Prediction Value. These measures indicate that the Decision Tree method is appropriate for predicting coronary artery disease, including independent data (other coronary artery disease data with the same predictor variables). The results of this study also show that the different references to previous studies in discretizing numerical variables can improve the performance of the method in predicting coronary artery disease.
Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality Winoto Chandra; Resti, Yulia; Bambang Suprihatin
Jurnal Inovasi Matematika Vol 4 No 1 (2022): Inovasi Matematika (Inomatika)
Publisher : Pendidikan Matematika Universitas Muhammadiyah Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (696.13 KB) | DOI: 10.35438/inomatika.v4i1.310

Abstract

Despite the pandemic, Jakarta is one of the most polluted cities in the world. Knowing the daily air quality forecast aids the community, particularly Jakarta residents. Among these is the ability to protect oneself from dangerous air. The multinomial naive Bayes and the decision tree-ID3 methods are popular and perform well. Both of these strategies, however, require categorical variables. This need necessitates the implementation of a discretization technique for numerical variables. The purpose of this study is to predict Jakarta's air quality using the multinomial naive Bayes and decision tree method based on Particulate Matter 10 µg (PM10), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3), and Carbon Monoxide (CO). These continuous variables are discretized in two ways: using all midway breakpoints or halfway mixture breakpoints. The results indicated that the decision tree method with the mixture breakpoints halfway approach performed better than the multinomial nave Bayes method, with an accuracy of 98.90%, a specificity of 98.92%, a sensitivity of 75.00%, a precision of 75.00%, and an F1 score of 97.81%.
Co-Authors A. S. Mohruni Akbar Teguh Prakoso Ali Amran Ali Syahbana Alwine Zayanti, Des Amrifan Saladin Mohruni Andi Eka Putra Aneka Firdaus Anita Desiani Ansyori Ansyori Ansyori Ansyori Ansyori Yani Anthony Costa Arhami, Muhammad Astuti Astuti Bambang Suprihatin Bambang Suprihatin Bambang Suprihatin Burlian, F. Chandra Irsan Chandra Irsan Chandra Irsan Dendy Adanta Des A. Zayanti Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti, Des Alwine Des Alwine Zayantii Desi Herlina Saraswati Dewi Puspita Sari Dewi Puspitasari Dewi Puspitasari Dewi, Novi R. Endang S Kresnawati Endang S. Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati Endang Sri Kresnawati F Nasution F. A. Alhamdini Firmansyah Burlian Firmansyah Burlian Firmansyah Burlian Fitri Puspasari Fusito Fusito Hasan Basri Hoiri, Sajiril I Yani Indah Meiliana Sari Irsyadi Yani Ismail Thamrin Jeremy Firdaus Latif Kresnawati, Endang S. M. Hasbi Ramadhan, M. Hasbi M. Rendy Kurniawan MARWANI Marwani Marwani, Marwani Mauizzatil Rahmayani Mega Tiara Putri Muflika Amini Muhammad Nawawi Muhammad Yanis Ning Eliyati Ning Eliyati Ning Eliyati Ning Eliyati Ning Eliyati Ning Elyati Noriszura Ismail Nova Yuliasari Novi R. Dewi Novi Rustiana Dewi Novi Rustiana Dewi Novi Rustiana Dewi Rahmayani, Mau’izatil Ratu Ilma Indra Putri Resnawati Rossi Passarella Saiful Hafizah Jaaman Saputra, M. A. Ade Saputra, M.A. Ade Setyo Cahyono, Endro Shohif Wijaya Sri Kresnawati, Endang Sugandi Yahdin Teguh Prakoso, Akbar Titania Jeanni Charissa Widya Ayu Amanda Winoto Chandra Yani, I. Yuli Andriani Zayanti, Des A. Zulkardi Zulkardi Zulkardi Zulkarnain Zulkarnain