Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics International Journal of Information Technology and Business
Eryan Ahmad Firdaus
Indonesia Defense University
Computer Science & IT Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Python-based stock price prediction using backpropagation neural networks: a case study on ANTM Prind Triajeng Pungkasanti; Febrian Wahyu Christanto; Fadhilatut Tasyriqul Hajjas Sabat; Christine Dewi; Eryan Ahmad Firdaus
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9760

Abstract

Accurate stock price prediction is critical for informed investment decisions. Today, stock trading has become a popular option as a source of income among people, due to its potential for rapid gains in a short time, but, due to fluctuating stock prices, it can cause great losses in exchange. This study aims to forecast the closing price using the backpropagation neural network algorithm so that it can be used as a decision support for potential investors and traders in this research, the system was built using the Python programming language, and the stock price data used were shares of the company Aneka Tambang Tbk (ANTM). The results of this research are root mean squared error (RMSE) values, additional labels for prediction results, and graphs for comparison of the original data with the predicted data. Based on the testing result, the best value of RMSE is 3.786, the mean absolute percentage error (MAPE) value is 0.001 which indicates that the prediction results are very close to the actual value.
An Adaptive AI-Driven Copywriting Framework: Design, Implementation, and Evaluation of a Web-Based GPT-Integrated Content Generation System April Firman Daru; Febrian Wahyu Christanto; Rastri Prathivi; Dimas Prasetyo; Eryan Ahmad Firdaus
International Journal of Information Technology and Business Vol. 8 No. 2 (2026): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.822026.8-17

Abstract

The increasing demand for scalable and high-quality digital marketing content has exposed limitations in traditional manual copywriting processes, which are time-intensive and difficult to scale. This research proposes an adaptive AI-driven copywriting framework that integrates a full-stack web architecture with optimized prompt engineering strategies for automated content generation. The system is implemented using React.js for the frontend, Node.js with Express for backend services, and a GPT-based API for language generation. Unlike prior implementations, this research introduces a structured prompt optimization mechanism to enhance content relevance and consistency. Experimental evaluation was conducted using multiple datasets of marketing prompts, with comparisons against baseline GPT usage and manual copywriting. Quantitative results show that the proposed system achieves improvements in BLEU (+18.7%) and ROUGE-L (+21.3%) scores over baseline methods. Human evaluation involving 30 participants indicates a significant increase in perceived content quality, coherence, and persuasiveness (p < 0.05). System performance analysis demonstrates an average response time of 1.8–3.0 seconds and a GTmetrix performance score of 82%. The findings confirm that the proposed framework significantly enhances efficiency, scalability, and content quality, contributing to both applied AI systems and intelligent web-based content production.
Co-Authors April Firman Daru Christine Dewi Dimas Prasetyo Fadhilatut Tasyriqul Hajjas Sabat Febrian Wahyu Christanto Prind Triajeng Pungkasanti, Prind Triajeng Rastri Prathivi