Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Big Data Analytic and Artificial Intelligence
Yusni Amaliah
STMIK PPKIA Tarakanita Rahmawati Jl. Yos Sudarso 8 Tarakan, Indonesia
Computer Science & IT

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

Found 2 Documents
Search
Journal : journal of big data analytic and artificial intelligence

 1 
Studi Efektivitas Metode Sistem Pakar untuk Mendiagnosa Penyakit pada Hewan Ternak Sapi Damayanti, Nabela; Amaliah, Yusni; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 2 (2024): JBIDAI Desember 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.48

Abstract

Cattle often face the risk of diseases that can lead to significant losses for farmers. Limited knowledge about diseases in cattle often makes farmers rely on livestock experts or seek assistance from veterinarians. Therefore, this research aims to compare the effectiveness of two expert system methods, namely Euclidean Probability and Bayes' Theorem, in detecting diseases in cattle. Euclidean Probability is used as a case-based approach technique to measure the likelihood or certainty of conclusions based on the causes that occur. On the other hand, Bayes' Theorem is a method for calculating the probability of hypotheses based on previous data. Both methods have similar goals, which are to determine the presentation of diseases based on the symptoms experienced by cattle, with the main difference lying in the calculation processes they employ. The application of the expert system resulting from this research can assist clinic personnel in detecting diseases in cattle. The effectiveness of the method from the program trial for six different diseases resulted in an accuracy of 83% for the Euclidean Probability method, while the Bayes' Theorem method resulted in 50% accuracy. This concludes that the Euclidean Probability method is more effective than the Bayes' Theorem method in diagnosing diseases in cattle.
Sistem Pakar Dalam Penentuan Minat dan Bakat Anak Usia Taman Kanak-Kanak Menggunakan Metode Dempster Shafer Devaus, Lea; Amaliah, Yusni; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.53

Abstract

Early childhood education relates to the teaching of children from birth up to the age of six, which prepares children for further education by providing educational stimulation to aid physical and spiritual growth. Thus, it is necessary a system to help parents learn their children's interests and talents to direct and develop their potential, especially in the arts. Dempster Shafer is a mathematical theory of evidence based on belief function and plausible reasoning to combine discrete pieces of information to calculate the probability of an event. The results show that children's interest and talent in art at Paud Pelangi Desa Sedulun consists of 5 types: Mosaic Art, Acting Art, Music Art, Montage Art, and Painting Art. Manual calculations and computerized tests produce the highest percentage value and the final conclusion. Determining children's interests and talents from the input of behavioral criteria generates a final assessment that helps children's development in the arts.
Co-Authors Damayanti, Nabela Devaus, Lea Gusmana, Roman