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All Journal Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Governance IT Adoption and Technology Advance
Aedah Abd Rahman
Asia e University
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach Yaddarabullah Yaddarabullah; Aedah Abd Rahman; Amna Saad
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp233-244

Abstract

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.
A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia Desi Anggreani; Nurmisba Nurmisba; Aedah Abd Rahman
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
Pengukuran Kualitas Layanan Website Jurnal GovITA Untuk Mendukung Tata Kelola Publikasi Ilmiah Digital Modern Aedah Abd Rahman; Angelie Faatihah Susanto; Dwi Nur Karin; Tsaqif Arkan Samaizar
Governance IT Adoption and Technology Advance Vol. 1 No. 1 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i1.10312

Abstract

This study aims to evaluate the quality of digital services on the GovITA Journal Website Portal using the  WebQual 4.0 model, which encompasses three main dimensions: usability, information quality, and service interaction quality, as well as to analyze users’ satisfaction levels with the portal services. The GovITA Journal Portal plays a strategic role in supporting the scientific publication process; therefore, assessing service quality based on user experience is essential to ensure the effectiveness and credibility of the digital publication system. The study employed a quantitative approach by distributing questionnaires to 50 respondents consisting of authors, reviewers, editors, and journal readers. The research instrument was tested for validity and reliability, and all items were found to be valid and reliable. Descriptive analysis revealed that all three WebQual 4.0 dimensions were classified as high, with mean scores of 4.02 for usability, 4.00 for information quality, and 3.88 for service interaction quality. User satisfaction was also categorized as high, with a mean score of 4.04, indicating that the portal has met users’ needs and expectations. The discussion highlights that information quality and ease of use are the dominant factors contributing to user satisfaction. The study recommends improving aspects of service interaction, particularly system reliability and portal response speed, as well as developing a user experience–based interface to enhance overall website performance. These findings provide important contributions for the management of the GovITA Journal in continuously improving digital service quality and strengthening the portal’s position as a modern and user- friendly scientific publication platform.
Co-Authors Amna Saad Angelie Faatihah Susanto Desi Anggreani Dwi Nur Karin Nurmisba Nurmisba Tsaqif Arkan Samaizar Yaddarabullah, Yaddarabullah