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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JAR'S (Journal of Advanced Research in Informatics)
Malik, Kamil
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health Transportation Other

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Journal : JAR'S (Journal of Advanced Research in Informatics)

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Evaluasi Model Jaringan Saraf Tiruan Berbasis LSTM dalam Memprediksi Fluktuasi Harga Bitcoin Sudriyanto, Sudriyanto; Faid, Mochammad; Malik, Kamil; Supriadi, Ahmad
Journal of Advanced Research in Informatics Vol 2 No 2 (2024): Journal of Advanced Research in Informatics
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/jars.v2i2.3398

Abstract

Amid the highly volatile fluctuations in the cryptocurrency market, the ability to accurately predict Bitcoin prices becomes crucial for investors and financial analysts. This study aims to develop a predictive model using Long Short-Term Memory (LSTM) Neural Networks, a specific form of recurrent neural network, to predict Bitcoin prices. Historical data on daily closing prices of Bitcoin from 2015 to 2023 was used to train and test the model. Following data preprocessing, which included normalization and the creation of a time series dataset, the LSTM model was constructed with two LSTM layers and two dense layers to enhance the predictive analysis. The model was trained with the data split into 80% for training and 20% for testing. Results show that the LSTM model was able to produce fairly accurate predictions with a low loss value on the test data. Further evaluation through comparison with baseline models showed significant improvements in predictive accuracy. This research demonstrates the potential application of advanced machine learning techniques in financial analysis, particularly in predicting the prices of highly volatile assets like Bitcoin. With continuous improvements to the model architecture and parameter optimization, Bitcoin price predictions could become more reliable, helping stakeholders make more informed investment decisions.
AI CHATBOT IMPLEMENTATION FOR NURUL JADID UNIVERSITY WEBSITE USING LSTM ALGORITHM Sudriyanto, Sudriyanto; Malik, Kamil; Jamal, Jamal
Journal of Advanced Research in Informatics Vol 3 No 2 (2025): Journal of Advanced Research in Informatics
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/jars.v3i2.4163

Abstract

The rapid advancement of technology has brought significant changes in various aspects of life, including the education sector. As an educational institution, Nurul Jadid University must adopt the latest technology to enhance efficiency and service, particularly in responding to the increasing volume of inquiries and information needs from the public and parents before enrolling their children. A chatbot, as part of Natural Language Processing (NLP) based on Artificial Intelligence (AI), is designed to interact with users through text or voice, providing fast, accurate, and continuous service. The Long Short-Term Memory (LSTM) algorithm in deep learning is utilized for text data prediction and classification. In this research, the data consists of tags, patterns, and responses obtained manually from the official Nurul Jadid University website and then preprocessed to develop the chatbot model. The core component of this model is the embedding layer, which assigns vector values to each word in the processed text data. The model training results indicate an accuracy of 99.32% and a loss of 12.57%, demonstrating that the model performs well without overfitting or underfitting, making it suitable for testing and deployment. Thus, the LSTM-based chatbot serves as an effective virtual assistant to help the public, prospective students, and current students access information more easily and efficiently.
Co-Authors Eko Purnomo Hasyim, Fuadz Hudaya, Kharisman Kholid Jamal Jamal Jasri, Moh. Mashuri, Ahmad Sanusi Mochammad Faid Nisa', Khoirun Rizal, Fathur Supriadi, Ahmad Sya'roni, Wahab Widad, Zeda Al Yoga Pratama Yudistira Yudistira Zainal Arifin