Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Journal of Innovative and Creativity Journal of Intelligent Systems and Information Technology Fusion : Journal of Research in Engineering, Technology and Applied Sciences
Albert Riyandi
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Prediksi Harga Penutupan Indeks Harga Gabungan (IHSG) Menggunakan Algoritma Long Short-Term Memory Berbasis Data Historis Muhammad Farhan Fadhila; Rian Septian Anwar; Albert Riyandi
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.4318

Abstract

The dynamic fluctuation of stock prices makes market movement prediction a significant challenge in financial analysis. This study aims to develop and evaluate a Long Short-Term Memory (LSTM) model to predict the daily closing price of the Indonesia Composite Index (IDX Composite) using historical data from 2005 to 2025. The methodology employed includes systematic data pre-processing, such as normalization and the creation of sequential input, as well as the implementation of a two-layer LSTM architecture. Model performance was evaluated through two approaches, namely experimental testing on test datasets and practical validation in short-term prediction scenarios. Experimental results demonstrate very high accuracy, with a Mean Absolute Percentage Error (MAPE) value of 0.69% on the test data. This performance consistency is reinforced by the practical validation, which yielded an overall MAPE of 0.34%, proving the model's capability in predicting previously unseen data. Thus, the hypothesis that the LSTM model can achieve significant prediction accuracy (MAPE<10%) is accepted. Overall, the developed LSTM model is proven to be highly effective and valid, making it a suitable tool to support investment decisions in the stock market.
Detection of Rupiah Nominal Values Based on Computer Vision and OCR for Low Vision Accessibility Doucoure Mohammed Hakeem; Trisna Almuti; Syahbil Afriza Baharaji; Muhammad Iqbal; Albert Riyandi
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 3 No. 1 (2026): Fusion - April
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v3i1.338

Abstract

The ability to recognize banknotes' nominal value is a fundamental skill in daily economic transactions. However, for low-vision individuals, this simple task poses a major challenge, risking transaction errors and fraud. This study aims to build a web-based application capable of detecting Rupiah currency nominals in real-time by integrating computer vision and Optical Character Recognition (OCR) as an independent accessibility feature. The method combines a custom object detection model based on the YOLO architecture via the Roboflow platform and Tesseract OCR for nominal text verification, which is then integrated with the Web Speech API for voice-based output (Text-to-Speech). The system test results indicate that the combined "Roboflow + OCR" approach significantly improves detection reliability compared to using the object model alone. The system achieved a classification accuracy rate of 94.5% under optimal lighting conditions, with an average Text-to-Speech response latency of 1.8 seconds. This implementation proves that the synergy of image processing and OCR can provide an effective and inclusive assistive technology solution for visually impaired groups in Indonesia.
Comparative Analysis of Machine Learning Algorithms in Detecting DDoS Attacks on CICIDS2017 Dataset Dika Kurnia Putra; Chandra Ari Pradana; Muhammad Hilal Gilardin; Albert Riyandi
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.182

Abstract

The rapid escalation of Distributed Denial of Service (DDoS) attacks has posed significant threats to global cybersecurity. This research presents a comparative analysis of three supervised machine learning algorithms—K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF)—in detecting DDoS attacks using the CICIDS2017 dataset. While many studies focus on broader intrusion detection, this study concentrates specifically on binary classification between benign and DDoS traffic. The CICIDS2017 dataset was chosen for its comprehensive and realistic representation of modern network traffic. The methodology involved preprocessing, training, and evaluating the models in Orange Data Mining using 10-fold cross-validation. Evaluation metrics included Accuracy, Precision, Recall, F1-Score, AUC, and Matthews Correlation Coefficient (MCC). Empirical results show that the Random Forest algorithm outperformed both KNN and Decision Tree, achieving perfect scores across all metrics (1.000). These findings highlight the robustness of ensemble learning in intrusion detection. The results have practical implications for the development of more reliable, efficient, and automated Intrusion Detection Systems (IDS), especially in real-world scenarios prone to volumetric DDoS attacks. Future work should explore multiclass classification and real-time implementation.
Co-Authors Chandra Ari Pradana Dika Kurnia Putra Doucoure Mohammed Hakeem Muhammad Farhan Fadhila Muhammad Hilal Gilardin Muhammad Iqbal Rian Septian Anwar Syahbil Afriza Baharaji Trisna Almuti