Article Per Year (5 Year)
p-Index From 2021 - 2026
1.953
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah KOMPUTASI Journal of Information System, Applied, Management, Accounting and Research Jurnal Restikom : Riset Teknik Informatika dan Komputer Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB IT For Society : Journal of Information Technology SAINTEK
Muhammad Makmun Effendi
Universitas Pelita Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Analisa Prediksi Harga Saham Blue Chip Lq45 Dengan Metode Data Mining Backpropagation Neural Network (Studi Kasus Di Bursa Efek Indonesia) Puguh Ariyadi; M.Makmun Effendi; Sugeng Budi Raharjo
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366.SAINTEK0101.6876

Abstract

This research is a research on predictive analysis of LQ45 blue chip stock price with backpropagation neural network data mining method. This study aims to determine the stock price prediction process using the backpropagation neural network method on the LQ45 blue chip stock price. This research is in training and testing data using RapidMiner tools with 80% data sharing for training data and 20% for testing data. The parameters used are training cycle of 500, learning rate of 0.01 and momentum of 0.9. The results of the training and testing of the stock prices of 5 companies in LQ45 obtained the RMSE (Root Mean Square Error) value with the best result of 11.296 and the largest error of 61.925 which indicates the backpropagation neural network method is quite good in the process of predicting stock prices. The results of this prediction can be used as a reference for stock investors in determining the right strategy to minimize mistakes in making decisions to buy or sell the desired stock. Keywords: Data Mining, Backpropagation Algorithm, Neural Network, Rapidminer, Stock Price
Co-Authors A Yudi Permana A. Yudi Permana Agung Wijayadhi Ahmad Turmudi Ahmad Turmudi Zy Aida Ratnasari2 Arie Setiawan2 Arif Siswandi Arji Prasetio Diah Rahmawati Ermanto Ermanto Farid Wajdillah Farish Al Khairi Hengki Anthony Juliyanto M. Najamuddin Dwi M Mochammad Rahmat Faisal Moh Khamim Muhammad Adipa Puguh Ariyadi Sugeng Budi Rahardjo Sugeng Budi Raharjo Suherman Suherman Zaenal Mustofa