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The Determinant Factors For The Issuance Of Central Bank Digital Currency (CBDC) In Malaysia Using Machine Learning Framework Normi Sham Awang Abu Bakar; Norzariyah Yahya; Norbik Bashah Idris; Engku Rabiah Adawiah Engku Ali; Jasni Mohamad Zain; Erni Eliana Khairuddin; Ahmad Firdaus Zainal Abidin; Sheikh Mohammad Tahsin Murtaj; Siti Sarah Maidin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.176

In order to identify the factors influencing the establishment of the Centre Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were used to identify the determining variables. The accuracy obtained through the Random Forest is 83% and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.

Lora Communication System for Early Detection and Monitoring of Water Toxicity in Floating Net Cages Rahmafadilla Rahmafadilla; Indrarini Dyah Irawati; Mochammad Fahru Rizal; Siti Sarah Maidin
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.787

Floating Net Cages/ Keramba Jaring Apung (KJA) are at risk of polluting the air, which can affect fish farming. Therefore, an early monitoring system is needed that can measure air quality such as temperature, pH, and dissolved oxygen (DO) in real-time. This system utilizes the LoRa RFM95W module to wirelessly transmit environmental data from sensors installed on the cages, which continuously monitor water quality parameters such as temperature, pH, and DO in real-time. The data obtained is then processed to monitor changes in water toxicity in real-time, allowing early detection of potential threats to the ecosystem. Tests were conducted at distances of 50m, 180m, 300m, 340m, and 440m. The results showed that the system worked well up to a distance of 300m with RSSI values between -85 dBm to -120 dBm and SNR more than 2 dB. However, at distances of 340m and 440m, the signal decreased and the delay increased. At a depth of 340m, only one experiment was successful with RSSI -134 dBm and SNR -6 dB, while at a depth of 440m, only a few experiments were successful with RSSI between -122 dBm to -132 dBm and SNR between 1 dB to -6 dB. The prototype system successfully transmitted real-time air quality data to a web-based monitoring center. Data from the sensors were sent via the LoRa network to a central server for further monitoring.

Trust Aware Congestion Control Mechanism for Wireless Sensor Network G. Maria Priscilla; B.L. Shiva Kumar; Siti Sarah Maidin; Zainab S. Attarbashi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.564

Congestion in wireless sensor networks (WSNs) can occur from various factors, including resource limitations and the transmission of packets surpassing the capacity of receiving nodes. This congestion may arise from natural causes or be exacerbated by self-serving nodes. Furthermore, malicious sensor nodes within WSNs have the capability to instigate congestion-like scenarios by either flooding the network with redundant fake packets or maliciously discarding genuine data packets. Relying solely on conventional congestion control techniques proves inadequate for ensuring fair delivery, necessitating a proactive approach to prevent such adversities by segregating these nodes from the network. Existing congestion control strategies often make the unrealistic assumption that all nodes are authentic and behave appropriately. To address these challenges, a proposed Genetic Algorithm based Trust-Aware Congestion Control (GA-TACC) not only manages congestion under natural circumstances but also considers scenarios where hostile nodes deliberately improve packet delivery. The GA evaluates the credibility score (CS), contributing to enhanced performance, and GA-TACC demonstrates superiority over existing state-of-the-art techniques for wireless sensor network.

Intelligent Transportation System's Machine Learning-Based Traffic Prediction S Govindaraju; M Indirani; Siti Sarah Maidin; Jingchuan Wei
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.364

The aim of this study is to develop an accurate and timely traffic flow prediction tool that considers various factors influencing road conditions, such as road repairs, rallies, traffic signals, and other everyday events that can impact traffic movement. By providing drivers with near real-time predictive insights, they can make more informed decisions, enhancing traffic management and potentially supporting future autonomous vehicle technologies. Given the exponential growth in traffic data, this research applies big data principles to the transportation domain, where existing traffic prediction models struggle to handle real-world applications effectively. In this study, we implemented machine learning, genetic algorithms, soft computing, and deep learning techniques, achieving a traffic flow prediction accuracy of 93.5%. The results demonstrate a significant improvement in prediction accuracy compared to conventional models, which typically average around 85%. Additionally, image processing algorithms for traffic sign identification are integrated, achieving 90% accuracy in identifying key traffic signs, further aiding in the training of autonomous vehicles. The proposed approach addresses the challenges posed by large-scale transportation data, offering a solution with improved predictive accuracy and practical utility.

The Determinant Factors For The Issuance Of Central Bank Digital Currency (CBDC) In Malaysia Using Machine Learning Framework Normi Sham Awang Abu Bakar; Norzariyah Yahya; Norbik Bashah Idris; Engku Rabiah Adawiah Engku Ali; Jasni Mohamad Zain; Erni Eliana Khairuddin; Ahmad Firdaus Zainal Abidin; Sheikh Mohammad Tahsin Murtaj; Siti Sarah Maidin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.176

In order to identify the factors influencing the establishment of the Centre Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were used to identify the determining variables. The accuracy obtained through the Random Forest is 83% and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.

CS-based Lung Covid-Affected X-Ray Image Disorders Classification using Convolutional Neural Network Biyantika Emili Triasari; Gelar Budiman; Siti Sarah Maidin; M. Izham Jaya; Yuli Sun Hariyani; Indrarini Dyah Irawati; Zhong Zhao
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.371

Lung diseases, such as pneumonia, significantly affect the respiratory system, especially the lungs. This condition causes various degrees of lung damage in patients of all age groups, including the elderly, adults, and children. Even after treatment and recovery, diagnosing lung damage remains important, which can be done using rapid tests, clinical evaluations, CT scans, or X-rays. This study focuses on the classification of X-ray images of lungs affected by pneumonia and normal lungs, using the Convolutional Neural Network method based on Compressive Sensing (CS) simulated using MatLab. The purpose of the study is to determine the performance by calculating the confusion matrix value. The number of datasets used for normal lungs and those affected by pneumonia is 300 X-ray images from several different sources, with 60% training data, 30% validation, and 10% testing. The addition of the compression process causes a decrease in image quality, expressed in PSNR, as well as a decrease in classification parameters such as accuracy. Compared with previous research, the system without compression produces the highest accuracy. The results of the study can help classify lungs affected by pneumonia.

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