Article Per Year (5 Year)
p-Index From 2021 - 2026
0.877
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) EMITTER International Journal of Engineering Technology Indonesian Journal of Electrical Engineering and Computer Science
Nurulaqilla Khamis
Universiti Teknologi Malaysia
Computer Science & IT

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Optimization of RF signal detection and alert system for restricted area Ili Najaa Aimi Mohd Nordin; Najla Aiman Nazari; Muhammad Rusydi Muhammad Razif; Nurulaqilla Khamis; Noraishikin Zulkarnain; Farkhana Muchtar; Nor Aira Zambri
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp325-332

Abstract

This paper presents study on optimizing RF signal detection and alert system for restricted area. Nowadays, mobile phone utilization is very common among youngsters. Despite of being very useful for educational purpose, it turns into a noteworthy issue while being smuggled into restricted area, such as examination hall and used as a cheating tool. To overcome this problem, an advancement on the detection and alert system of active mobile phone was proposed by implementing RF signal detection system optimization using multi-band dipole antenna and alert system utilizing LED and camera. The system demonstrated higher sensitivity to 3G signal followed by GSM and Wi-Fi with the decrease in voltage value across the LED when distinguishing 3G, GSM and Wi-Fi signal with the estimation of 2.316 V, 1.162 V and 1.054 V respectively. The alert system was improved by activating LED and camera instead of buzzer in displaying the region of the active mobile phone. The camera was able to effectively capture the conceivable cheating area when active mobile phone is present. The images of the potential cheating region can be recovered from the computer. This framework can assist the invigilators with being cautioned to the potential regions of cheating using mobile phone.
IMU sensor-based data glove for finger joint measurement Muhammad Ajwad Wa’ie Hazman; Ili Najaa Aimi Mohd Nordin; Faridah Hanim Mohd Noh; Nurulaqilla Khamis; M. R. M. Razif; Ahmad Athif Faudzi; Asyikin Sasha Mohd Hanif
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp82-88

Abstract

The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents an inertial measurement unit sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors, MPU-6050 and two flexible bend sensors which are capable to detect angle displacement were attached to the distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joint points on the glove. The data taken from inertial measurement unit sensors and flexible bend sensors were acquired using Arduino and MATLAB software interface. The data obtained were compared with the reference data measured from goniometer to allow for accurate comparative measurement. The percentage of error resulted from MPU-6050 sensor unit were ranged from 0.81 % to 5.41 % were very low which indicates high accuracy when compared with the measurements obtained using goniometer. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor-based data glove using MPU-6050 sensors can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating  MPU-6050 sensors on all five fingers.
Imbalanced data handling in multiclass distributed denial of service attack detection using deep learning Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Hasanatul Fu’adah Amran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26005

Abstract

In data analysis, imbalanced datasets are a frequent issue, where classes in a dataset have an uneven distribution, which can lead to poor performance in machine learning (ML) and predictive modeling. In this study, we analyze distributed denial of service (DDoS) attacks at the application layer. Three primary strategies are studied in this study to address the issue of data imbalance in multiclass techniques: random oversampling (ROS), random undersampling (RUS), and the use of class weights. A model using a deep learning (DL) technique has been proposed in this paper to be trained and tested for DDoS attack detection. Based on the results obtained and presented in this paper, it is observed that RUS outperforms class-weight and ROS in multiclass settings in terms of resolving imbalanced data when implemented with the deep learning-based DDoS attack detection model.
Deep learning approach to DDoS attack with imbalanced data at the application layer Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Januar Al Amien; Edi Ismanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24857

Abstract

A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
Towards Robust Recognition of Handwritten Arabic Characters with Diacritics Using an Incremental Learning Approach Based on CNNs Fatima Aliyu Shugaba; Usman Ullah Sheikh; Mohd Afzan Othman; Nurulaqilla Khamis; Muhammad Habibullah Abdulfattah
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.982

Abstract

Handwritten Arabic text recognition (HATR) presents unique challenges due to complex character shapes, contextual variations, cursive connections, and the presence of diacritical marks. This study introduces AHAD (Arabic Handwritten Alphabet with Diacritics), a novel benchmark dataset of 71,061 handwritten Arabic character images annotated with five primary vowel diacritics; Fathah, Kasrah, Dammah, Shaddah, and Sukoon, covering 492 distinct classes that combine character identity, contextual form, and diacritic. Leveraging this dataset, we propose an incremental learning framework based on Convolutional Neural Networks (CNNs) to address fine-grained recognition of handwritten Arabic characters with its corresponding diacritics. The model was initially trained on a 114-class dataset of handwritten Arabic characters (in all contextual forms) of non-diacritic characters and fine-tuned in two phases using the AHAD dataset. The two-phase strategy includes output layer expansion, learning rate adjustment, and gradual unfreezing of deeper layers to enhance knowledge retention and prevent catastrophic forgetting. The proposed method achieved a validation accuracy of 92.96% and a test accuracy of 93.26%. Our findings demonstrate the effectiveness of incremental learning for diacritic-aware Arabic handwriting recognition and establish AHAD as a strong baseline for future research in this field.
Co-Authors Ahmad Athif Faudzi Asyikin Sasha Mohd Hanif Edi Ismanto Faridah Hanim Mohd Noh Farkhana Muchtar Fatima Aliyu Shugaba Gunawan, Rahmad Hadhrami Ab Ghani Hasanatul Fu’adah Amran Ili Najaa Aimi Mohd Nordin Ili Najaa Aimi Mohd Nordin Januar Al Amien M. R. M. Razif Mohd Afzan Othman Muhammad Ajwad Wa’ie Hazman Muhammad Habibullah Abdulfattah Muhammad Rusydi Muhammad Razif Najla Aiman Nazari Nor Aira Zambri Noraishikin Zulkarnain Usman Ullah Sheikh