Article Per Year (5 Year)
p-Index From 2020 - 2025
0.92
P-Index
This Author published in this journals
All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdi Insani Multica Science and Technology Parameter: Journal of Statistics Jurnal Statistika dan Komputasi (STATKOM)
Nurmayanti, Wiwit Pura
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Multica Science and Technology

 1 
IMPLEMENTATION OF NEURAL NETWORK IN PREDICTING STOCK PRICE OF PT BANK RAKYAT INDONESIA (PERSERO) TBK Nurmayanti, Wiwit Pura; Ni Luh Desvita Pratiwi; Nariza Wanti Wulan Sari; Desi Yuniarti; Erlyne Nadhilah Widyaningrum; Thesya Atarezcha Pangruruk
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dwkza342

Abstract

Forecasting involves estimating future outcomes by examining patterns in both historical and present data. A commonly used data type in forecasting is time series data, characterized by observations collected at consistent time intervals. One forecasting technique that has gained significant attention is the Neural Network, particularly through the Backpropagation method utilized in this study. In the context of the stock market, price fluctuations are influenced by a variety of factors, including shareholder rights, company performance, and the balance between supply and demand. Typically, a rise in stock prices leads to decreased demand, while a decline tends to stimulate it. Predicting stock prices, such as those of Bank Rakyat Indonesia (BRI), can support investors in making well-informed decisions. This research seeks to identify the optimal number of neurons in the hidden layer for forecasting BRI stock prices by minimizing error metrics such as MAPE, MSE, and MAE. The analysis revealed that forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. using a neural network with one hidden neuron resulted in a MAPE of 1.22248 and an MAE of 61.30548.
Co-Authors Arif, Zainul Ayu Septiani, Ayu Bahriah, Ayu Desi Yuniarti Erlyne Nadhilah Widyaningrum Hasanah, Siti Hadijah Hendrayani, Hendrayani Hidayanty, Nurul Ilma Ika Purnamasari Kosasih, Raditya Arya M. Fathurahman Nariza Wanti Wulan Sari Ni Luh Desvita Pratiwi Ningsih, Eva Lestari Nugraha, Pratama Yuly Pangruruk , Thesya Atarezcha Pangruruk Sifriyani, Sifriyani Siti Hariati Hastuti SITI MAHMUDAH Sri Wahyuningsih Suyitno Suyitno Thesya Atarezcha Pangruruk Yuniarti, Desi