Nurmayasari, Tria (2022) Optimasi Algoritma Naive Bayes Berbasis Particle Swarm Optimization untuk Prediksi Proses Persalinan. Undergraduate thesis, UPN Veteran Jawa Timur.
|
Text (Cover)
17081010032-cover.pdf Download (999kB) | Preview |
|
|
Text (Bab 1)
17081010032-bab1.pdf Download (22kB) | Preview |
|
|
Text (Bab 2)
17081010032-bab2.pdf Download (395kB) | Preview |
|
![[img]](http://repository.upnjatim.ac.id/style/images/fileicons/text.png) |
Text (Bab 3)
17081010032-bab3.pdf Restricted to Registered users only until 26 July 2024. Download (685kB) |
|
|
Text (Bab 4)
17081010032-bab4.pdf Restricted to Registered users only until 24 July 2024. Download (1MB) |
|
|
Text (Bab 5)
17081010032-bab5.pdf Download (126kB) | Preview |
|
|
Text (Daftar Pustaka)
17081010032-daftarpustaka.pdf Download (242kB) | Preview |
|
|
Text (Lampiran)
17081010032-lampiran.pdf Restricted to Registered users only until 24 July 2024. Download (523kB) |
Abstract
The birth of a baby is a highly anticipated social event for the mother and family, but during the labor process it often causes risks such as the mother being unable to give birth normally due to mother suffers from certain diseases and so on, with the worst risk being experience death. Although it has taken into account various factors, it is important also in making decisions suddenly so as to avoid the risks that occur in the delivery process, then in the world of health, widely used clinical prediction decision support system. This study aims to predict the labor process using Naïve Bayes algorithm and Particle Swarm Optimization. algorithm Naïve Bayes is used because it can classify in a different way simple and fast and the Particle Swarm Optimization algorithm is used to optimization of particle weights as initial weights for the training process on Naïve Bayes. The data for this study used the maternal patient history dataset gave birth from the Aisyiyah Maternal & Child Hospital Bangkalan. There are 14 attributes in the dataset which will be used for the input particles in the algorithm Naïve Bayes and processed on a prediction system. This study resulted in an average – the average accuracy of the NB algorithm is 84% with an AUC value of 0.842, while the NB-PSO is 86% with an AUC value of 0.860 with the parameter – parameters, namely wmin 0.6, wmax 0.8, number of particles 9, c1 1.5, c2 1.5, r1 0.5, r2 0.5.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Contributors: |
|
||||||||||||
| Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming Q Science > QA Mathematics > QA76.87 Neural computers T Technology > TN Mining engineering. Metallurgy |
||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Tria Nurmayasari Masdayat | ||||||||||||
| Date Deposited: | 26 Jul 2022 07:56 | ||||||||||||
| Last Modified: | 26 Jul 2022 07:56 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/8377 |
Actions (login required)
 |
View Item |