Article Per Year (5 Year)
p-Index From 2020 - 2025
3.452
P-Index
This Author published in this journals
All Journal Jurnal Mahasiswa Fakultas Hukum Jurnal Ilmiah Mahasiswa Pendidikan Jasmani, Kesehatan dan Rekreasi Biosfer: Jurnal Pendidikan Biologi Majalah Patologi Indonesia Abdi Dosen : Jurnal Pengabdian Pada Masyarakat JURMA : Jurnal Program Mahasiswa Kreatif Al-Ubudiyah: Jurnal Pendidikan dan Studi Islam Journal of Engineering, Electrical and Informatics (JEEI) Jurnal Teknik Elektro International Journal of Health, Engineering and Technology Kreativitas Pada Pengabdian Masyarakat (Krepa) Triwikrama: Jurnal Ilmu Sosial Jurnal Pajak dan Analisis Ekonomi Syariah ADM : Jurnal Abdi Dosen dan Mahasiswa Moral : Jurnal Kajian Pendidikan Islam As-Sulthan Journal of Education Interelasi Humaniora
AKBAR MAULANA
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Engineering, Electrical and Informatics (JEEI)

 1 
The Implementation of Artificial Neural Networks for Stock Price Prediction Akbar Maulana; Enny Itje Sela
Journal of Engineering, Electrical and Informatics Vol 3 No 3 (2023): Oktober: Journal of Engineering, Electrical and Informatics
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v3i3.2254

Abstract

This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0. Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
The Implementation of Artificial Neural Networks for Stock Price Prediction Akbar Maulana; Enny Itje Sela
Journal of Engineering, Electrical and Informatics Vol. 3 No. 3 (2023): Oktober: Journal of Engineering, Electrical and Informatics
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v3i3.2254

Abstract

This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0. Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
Co-Authors Abdul Muttaqin Ade Suryanda Agus Sarifuddin Agus Widodo Ahmad Tohir Amelia Rahmawati Anas Mas’ud Aravik, Havis Bethy Surjawathy Hernowo Birgitta Maria Dewayani Choiriyah Choiriyah dedi kurniawan Dhea Kisti Mewahni Doly Nofiansyah Dwinda Fatima Ishak Eni Pujiastutik Enny Itje Sela Ernawati Ernawati Fadilla, Fadilla Fina Rosalina Ifwandi Ifwandi Ilham Hudi Lamhot Gabriel Nainggolan Lisa Fitriani Ishak Lutfian Ubaidilah M. Effendi M. Sholeh Awaludin, Muhammad Fahri Ramadhan Muhammad Rimba Alphard Siswaya Muhammad Syukron Abdillah Muhammad Widad Mutasor Muslim Muslim Nasution, Andreanda Novi Wahyu Asmaraningsih Nur Fitriah Nuur Wahidah Nyak Amir Rabbi Fernanda Raufa Aisyah Putri Salsa Ananda Erina Samsuddin Sania Rifqa Anshari SUPRAPTO Swity Retina Harsal Very Andaresta Vicky Allisonia Luckytasari WAHYU FIRMANSYAH Yuniar, Elisa Sabta Yuniar