Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal JAIS (Journal of Applied Intelligent System)
Wellia Shinta Sari
Dian Nuswantoro University

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Expert System for Diagnosing Potential Diabetes Attacks Using the Fuzzy Tsukamoto Christy Atika Sari; Wellia Shinta Sari; Andi Danang Krismawan
Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i2.6796

Abstract

Diabetes is one of the top three killers in Indonesia. According to the 2014 sample enrollment survey, the number of people with diabetes is increasing year by year. This is because the diagnosis of the disease is delayed. Also unhealthy lifestyle. In an era of fast and efficient technological advancement, this is a very good thing for advancement in various fields. More and more fields of knowledge are developing, one of which is expert systems. An expert system is a software or computer program that matches the ability of an expert, meaning that it can match humans with special abilities that ordinary people cannot solve. Expert systems aim to solve specific problems, such as in fields such as medicine, education, etc. This expert system takes as inputs several variables consisting of transient blood sugar (GDS), fasting blood sugar (GDP), frequent hunger, thirst, weight loss, and urine (BAK), the method used by the author is Fuzzy Tsukamoto. This Tsukamoto method states that every result of IF-Then must be described as a fuzzy set with an immutable or monotonic membership function, and uses PHP for programming. The results obtained in the study conducted by the authors were in the form of an expert system that detects diabetes and obtains results with 94% accuracy.
The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition Christy Atika Sari; Wellia Shinta Sari; Viki Ari Shelomita; Mohammad Roni Kusuma; Silfi Andriana Puspa; Muhammad Bima Gusta
Journal of Applied Intelligent System Vol 8, No 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.8450

Abstract

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.
Co-Authors Andi Danang Krismawan Christy Atika Sari Mohammad Roni Kusuma Muhammad Bima Gusta Silfi Andriana Puspa Viki Ari Shelomita