Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences JOIV : International Journal on Informatics Visualization Indonesian Journal of Data and Science International Conference on Health Science, Green Economics, Educational Review and Technology (IHERT)
Abd Rahman, Aedah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Physics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
AN IMPROVED CYBER SECURITY FRAMEWORK FOR EDUCATION INSTITUTIONS IN INDONESIA Hidayatulloh, Syarif; Abd Rahman, Aedah
International Conference on Health Science, Green Economics, Educational Review and Technology Vol. 1 (2019): International Conference on Health Science, Green Economics, Educational Review and T
Publisher : Universitas Efarina

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (187.887 KB) | DOI: 10.54443/ihert.v1i1.2

Abstract

One of the trends in the world of Education is Education technology. The COVID-19 pandemic forces us to accelerate using educational technology to keep the learning process in educational institutions around the world running. However, in adapting and using educational technology, it turns out that there is a factor of concern, namely cyber security. Because almost all educational technology platforms use the internet, cyber security is something that we inevitably have to deal with. Moreover, it turns out that during this covid19 pandemic, cybersecurity attacks have also increased along with the increase in the use of educational technology. Due to the high number of attacks and a large number of security holes in the Education technology platform adopted by educational institutions, So in this study, the authors will evaluate existing standards, models, and frameworks, identify fundamental and critical cybersecurity problems in several educational institutions in Indonesia, and propose a better security framework to address cybersecurity problems in educational technology in institutions.
The Role of Information Technology in Governance Mechanism for Strategic Business Contribution: A Pilot Study Setyadi, Resad; Abd Rahman, Aedah; Subiyakto, A'ang
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1657

Abstract

Information Technology Governance (ITG) aligns IT and business transformation in schools. Private schools need to implement a method to evaluate the process of aligning IT strategy with business strategy and whether IT investment supports business objectives.  What factors influence the ITG of selected Indonesia High School (HS) aligning IT-Business strategy? Partial Least Squares Structural Equation Modeling (PLS-SEM) is a tool for analyzing the ITG model results in this study. The result is the composition of 9 variables with four independent variables (as structure mechanism variables), four dependent variables (as process and relational mechanism variables), and the ITG variable (as the final variable) shows a significant value of 0.75 at the ITG variable. This considerable value means that the ITG supporting variables of four independent and four dependent variables significantly affect the ITG variable by 75%. This study provides information if the system trust variable is increasing due to the influence of good IT strategy (independent) variables and good business (dependent) variables. The recommendation is that this ITG trust model can be used to evaluate the alignment of IT strategy with business strategy and whether IT investment supports business objectives in HS
A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia Anggreani, Desi; Nurmisba, Nurmisba; Abd Rahman, Aedah
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.330

Abstract

Accurately predicting export values is key for a country in formulating its economic plans. Unfortunately, export data often exhibits complex time series patterns that are difficult to predict, characterized by non-linearity, high volatility, and complex temporal dependencies. This study offers a solution by testing a combined deep learning model, specifically a fusion of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), to address the challenges of export time series forecasting. This study uses this approach to forecast Indonesia's monthly export time series data from 2016 to 2023, covering various sectors ranging from oil and gas, non-oil and gas, agriculture, industry, mining, and others. The core idea is to leverage the CNN's ability to identify hidden features within time series patterns, while the BiLSTM is tasked with understanding the temporal flow of data from both directions to capture the inherent long-term temporal dependencies within economic time series data. As a result, this combined model proved to be far superior to the standard BiLSTM model in handling the complexity of export time series. In the Non-Oil and Gas sector, the proposed model achieved a high level of accuracy with an MSE value of 3,330,239.74, an RMSE of 1,824.89, and an average prediction error (MAPE) of only 8.17%, representing a significant improvement of 69% over the baseline BiLSTM model. Similar success was also found in all other sectors, proving that this hybrid approach is highly promising for complex economic time series analysis
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection Wahid, Abd Rahman; Anggreani, Desi; Hayat, Muhyiddin A. M.; Abd Rahman, Aedah; Faisal, Muhammad
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1263-1280

Abstract

Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
Co-Authors Anggreani, Desi Hayat, Muhyiddin A M Muhammad Faisal Nurmisba, Nurmisba Resad Setyadi Subiyakto, A'ang Syarif Hidayatulloh Wahid, Abd Rahman