cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Syahroni Hidayat
Contact Email
jtim.sekawan@gmail.com
Phone
-
Journal Mail Official
jtim.sekawan@gmail.com
Editorial Address
Jl. Bandeng No.25, Bintaro, Kec. Ampenan, Kota Mataram, Nusa Tenggara Bar. 83511
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
Arjuna Subject : -
Articles 1 Documents
 1 
Search results for , issue "Vol 6 No 1 (2024): May" : 1 Documents clear
Perbandingan Algoritma Naive Bayes Berbasis Feature Selection Gain Ratio dengan Naive Bayes Kovensional dalam Prediksi Komplikasi Hipertensi Putra, I Made Arya Adinata Dwija; Sunarya, I Made Gede; Gunadi, I Gede Aris
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 6 No 1 (2024): May
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i1.488

Abstract

High blood pressure is a significant public health problem with a high prevalence in the Indo-nesian population. 2018 Riset Kesehatan Dasar (Riskesdas) data shows a prevalence of hyperten-sion of 34.1% in individuals aged 18 years and over, with the highest figure in South Kalimantan and the lowest in Papua. Complications arising from hypertension can have serious impacts on organs such as the brain, eyes, heart and kidneys. The Naive Bayes algorithm is generally used in disease prediction, but the Naive Bayes algorithm has problems when selecting attributes, because Naive Bayes itself is a statistical classification method that is only based on Bayes' Theorem so it can only be used with the aim of predicting the probability of membership in a group or class. So attribute weighting is needed to increase accuracy more effectively. This research introduces Gain Ratio as an attribute weighting method to increase the accuracy of Naive Bayes. The aim of this study was to compare conventional Naive Bayes with Naive Bayes Gain Ratio in predicting complications of high blood pressure. The research results show that feature selection with gain ratio weighting can increase the accuracy of naive Bayes classification, with an average increase in accuracy of 20% compared to naive Bayes without feature selection. The precision value increased by 21% in the naive Bayes gain ratio algorithm for the kidney failure class, an increase of 3% in the heart class, and an increase of 31% in the stroke class, for the recall value the naive Bayes gain ratio increased by 35% in the heart class while in the kidney failure and stroke classes did not increase the recall value.

Page 1 of 1 | Total Record : 1

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): February Vol. 7 No. 4 (2025): November Vol. 7 No. 3 (2025): August Vol. 7 No. 2 (2025): May Vol. 7 No. 1 (2025): February Vol. 6 No. 3 (2024): November Vol. 6 No. 2 (2024): August Vol. 6 No. 1 (2024): May Vol 6 No 1 (2024): May Vol. 5 No. 4 (2024): February Vol 5 No 4 (2024): February Vol. 5 No. 3 (2023): November Vol 5 No 3 (2023): November Vol. 5 No. 2 (2023): August Vol 5 No 1 (2023): May Vol. 5 No. 1 (2023): May Vol 4 No 4 (2023): February Vol. 4 No. 4 (2023): February Vol 4 No 3 (2022): November Vol 4 No 2 (2022): Agustus Vol 4 No 1 (2022): May Vol. 4 No. 1 (2022): May Vol 3 No 4 (2022): February Vol 3 No 3 (2021): November Vol 3 No 2 (2021): August Vol. 3 No. 2 (2021): August Vol 3 No 1 (2021): May Vol 2 No 4 (2021): February Vol. 2 No. 4 (2021): February Vol 2 No 3 (2020): November Vol 2 No 2 (2020): August Vol 2 No 1 (2020): May Vol 1 No 4 (2020): February Vol 1 No 3 (2019): November Vol 1 No 2 (2019): August Vol 1 No 1 (2019): May More Issue