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All Journal JOIV : International Journal on Informatics Visualization Jurnal INFOTEL
Mohd Isa, Mohd Rizal
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Computer Science & IT Electrical & Electronics Engineering

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Breed Lineage Prediction of Small Ruminants Using Deep Learning Kamil, Mohammad Farizshah Ismail; Akmal Jamaludin, Nor Azliana; Mohd Isa, Mohd Rizal
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1168

Abstract

Sheep are a significant food source for humans, besides cattle and poultry. Despite its significance to Malaysian Muslims, who make up approximately 60% of the local population, the sheep supply is limited by the high mortality rate caused by fatal diseases such as foot and mouth disease (FMD) and tetanus. Infected sheep can spread food-borne bacteria, such as Escherichia coli, at various preparation phases, contaminating the meat. The objectives of this study are to identify internal and external factors that influence sheep breed lineage continuity, investigate current practices for collecting and managing data knowledge on sheep breed and hereditary diseases, and propose a sheep breed and disease data knowledge model based on the feedforward artificial neural network (FANN) deep learning method. This study utilized qualitative and quantitative data to obtain in-depth answers to the research questions, which involves collecting all the information required for the system development using the FANN deep learning method. This study found that breeding is the leading data group for tracking each sheep's ADG and BCS. Feed type, sanitization, and medication influence sheep’s daily increase and health. Collaboration, worker knowledge, and climate are recognized as external factors that potentially influence sheep's daily increase. The interview analysis also suggested attributes that could contribute to detecting breed lineage, including breed, category, ADG, and BCS. Therefore, it is recommended that future research adopt this method for other farmed animals.
Predictive Wireless Received Signal Strength Using Friis Transmission Technique Rawi, Roziyani; Mohd Isa, Mohd Rizal; Ismail, Mohd Nazri; Abu Bakar Sajak, Aznida; Yahaya, Yuhanim Hani
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2178

Abstract

A good WLAN performance is crucial in determining the quality of experience (QoE) among the campus community. Proper WLAN planning and design should be done beforehand to ensure good WLAN performance. Various studies have discussed different methods of conducting WLAN planning to predict WLAN's best performance, including using artificial intelligence and mathematical approaches. One of the processes involved in performing WLAN planning is measuring performance parameters. Signal strength is one of the vital parameters to be measured in determining the excellent performance of WLAN in a particular area. When deploying a WLAN design in two different environments, the signal strength outcomes can differ due to various factors, including obstacles and path loss propagation issues within the deployment area. Higher Learning Institutions (HLIs) present a unique challenge as their building designs vary to accommodate student needs. As a result, the selection of materials used will also be different, affecting the WLAN performance. A detailed study should investigate the effect of path loss propagation and the type of obstacle that affects WLAN performance in HLI. Thus, this study focuses on predicting received signal strength using Friis Transmission and studying the effect of path loss propagation on WLAN performance. The simulated model significantly affects signal strength when the signal passes through different types of building material (non-LOS) and line-of-sight (NLOS), where concrete walls substantially affect the received signal strength between transmitters. The proposed model can assist network planners in designing robust WLAN infrastructure by improving signal strength, particularly in the HLI WLAN environment. 
Detecting Distributed Denial-of-Service (DDoS) Attacks Through the Log Consolidation Processing (LCP) Framework Khairuddin, Mohammad Adib; Mohd Isa, Mohd Rizal; Mohd Shukran, Mohd Afizi; Ismail, Mohd Nazri; Maskat, Kamaruzaman
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2184

Abstract

One major problem commonly faced by organizations is a network attack especially if the network is vulnerable due to poor security policies. Network security is vital in protecting not only the infrastructure but most importantly, the data that moves around the network and is stored within the organization. Ensuring a secure network requires a complex combination of hardware including both network and security devices, specialized applications such as web filtering and log management, and a group of well-trained network administrators and highly skilled analysts.  This paper aims to present an alternative to the current log management solution. A hindrance to the current log management solution is the difficulty in amalgamating and correlating a vast number of logs with different formats and variables. This paper uses a novel framework called Log Consolidation Processing (LCP) based on the System Information Event Management (SIEM) technology, to monitor the behavior and the fitness of a network. LCP provides a flexible and complete solution to collect, correlate, and analyze logs from multiple devices as well as applications. An experiment testing the effectiveness of LCP in detecting DDoS attacks in a campus network environment was conducted, demonstrating a highly successful rate of detection. Besides threat detection and avoidance through log monitoring and analysis, other benefits of implementing the LCP framework are also included. This paper concludes by mentioning suggested enhancements for the LCP framework.
A Novel Network Optimization Framework Based on Software-Defined Networking (SDN) and Deep Learning (DL) Approach Osman, Muhammad Fendi; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Mohd Shukran, Mohd ‘Afizi; Mat Razali, Noor Afiza
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2169

Abstract

Access to networks and the Internet has multiplied, and data traffic is growing exponentially and quickly. High network utilization, along with varied traffic types in the network, poses a considerable challenge and impact on the ICT Infrastructure, particularly affecting the performance and responsiveness of real-time application users who will experience slowness and poor performance. Conventional/traditional Quality of Service (QoS) mechanisms, designed to ensure reliable and efficient data transmission, are increasingly insufficient due to their static nature and inability to adapt to the dynamic demands of modern networks.  As such, this study introduces a Novel Network Optimization Framework leveraging the combined strengths of Software-Defined Networking (SDN) and Deep Learning (DL) to dynamically manage multiple QoS of network devices in enterprise and campus network environments. The proposed system is a dynamic QoS that utilizes SDN's global monitoring and centralized management control capabilities to programmatically control network devices, ensuring that sensitive traffic is allocated with appropriate bandwidth and minimized latency. Concurrently, DL algorithms enhance the framework's decision-making process by proposing an accurate preferred configuration for the best adequate bandwidth for sensitive traffic transmission. This integration facilitates real-time adjustments to network conditions and improves overall network performance by ensuring high-priority applications receive the bandwidth they require without manual/human intervention. By providing a dynamic, intelligent solution to QoS management, this framework represents a significant step forward in developing more adaptable, resilient, and efficient networks capable of supporting the demands of contemporary and future digital ecosystems.
Enhancing The Server-Side Internet Proxy Detection Technique in Network Infrastructure Based on Apriori Algorithm of Machine Learning Technique Maskat, Kamaruzaman; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Kamarudin, Nur Diyana; Ismail, Mohd Nazri
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3410

Abstract

The widespread use of proxy servers has introduced challenges in managing and securing internet connections, particularly in detecting non-transparent proxies that obscure the originating IP address. Proxy servers, while beneficial for bandwidth management and anonymity, can be exploited for malicious purposes, such as bypassing geo-restrictions or concealing cyberattacks. This study aims to address the gap in identifying proxy usage by providing an organized review of existing detection techniques and proposing a hybrid server-side detection framework. The objectives of the research include identifying and comparing proxy detection methods, developing a hybrid approach using machine learning, and evaluating its effectiveness in enhancing network security. The methodology involves collecting primary data through controlled environments simulating direct and proxy-based connections. A machine learning model, based on the Apriori algorithm, is employed to analyze network traffic patterns and identify characteristics indicative of proxy usage. Attributes such as IP addresses, port numbers, and round-trip times are used to train the model. The proposed framework is tested for its robustness, accuracy, and speed against existing detection methods. The results demonstrate the feasibility of the hybrid approach in improving the detection of non-transparent proxies, particularly those not easily identifiable using conventional techniques. The findings have significant implications for securing server-side infrastructure, aiding in cyber threat mitigation, and enforcing organizational policies. Future research can expand on this framework by testing it against broader proxy types and integrating real-world data to enhance its reliability and scope. This study contributes to advancing cybersecurity practices by addressing a critical challenge in proxy detection.
A Case Study on Energy Efficiency at Universiti Pertahanan Nasional Malaysia (UPNM) Building for Smart Campus Initiatives Thanakodi, Suresh; Ismail, Nur Hidayah; Moh Nazar, Nazatul Shiema; Mukhtaruddin, Azharudin; Hidayat, Hendra; Mohd Isa, Mohd Rizal
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3409

Abstract

In efforts to minimize harmful greenhouse gas (GHG) emissions and mitigate climate change, energy efficiency has become a global priority. Commercial buildings, including university facilities such as Universiti Pertahanan Nasional Malaysia (UPNM), play a significant role in this effort, particularly in becoming a smart campus. Commercial buildings have been identified as major contributors to greenhouse gas (GHG) emissions, primarily due to their high energy consumption for air conditioning, lighting, and other operational needs. In alignment with the Paris Agreement 2016, the Malaysian government has implemented various energy-saving initiatives to reduce carbon emissions and achieve Energy Efficient Building Star Ratings. This study analyses five years of energy consumption at UPNM using the desktop audit method. Data collected from January 2018 to December 2022 includes the Nett Floor Area (NFA), energy consumption, and maximum demand of the UPNM buildings. The analysis encompasses energy consumption and expenditure, maximum demand, Building Energy Intensity (BEI), carbon footprint, and building energy labeling under the National Building Energy Labelling Standard. The findings indicate that UPNM has achieved a 5-star rating in 2020 and 2021, compared to a 4-star rating in 2018, 2019, and 2022. The BEI of UPNM buildings from 2018 to 2022 met the Energy Commission (EC) requirement of being below 135 kWh/m²/year. This study has also identified recommendations for further enhancing the energy efficiency of the UPNM Building, including regular maintenance of electrical appliances and conducting energy efficiency awareness campaigns.
Evaluation of Extreme Rainfall Occurrences Using Short-term and Long-term Standard Precipitation Index (SPI) Razuki, Nurul Dayana; Abdul Rauf, Ummul Fahri; Zainol, Zuraini; Mohd Isa, Mohd Rizal; binti Jamaludin, Nor Azliana Akmal
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2213

Abstract

The main objective of this study is to investigate the Standard Precipitation Index (SPI), a method commonly used to determine extreme rainfall occurrences. It is also used to gauge the severity and duration of drought in meteorological studies. To highlight exceptional extreme rainfall events in selected areas, a methodology for calculating the SPI was provided in this paper using a range period and thresholds. The Standard Precipitation Index (SPI) is used to analyze monthly precipitation data from several selected rain gauge stations between 1970 and 2014. The goal of this study is to monitor the extremely moist conditions that may eventually lead to flooding. Precipitation index data from several rain gauge sites in the selected region are used to calculate the SPI time series. Additionally, SPI readings for 3 months or less may usually be used for basic drought monitoring, values for 6 months or less may be useful for monitoring agricultural impacts, and values for 12 months or more may be useful for monitoring hydrological impacts. In this study, two states affected by the monsoon season were selected: Johor and Kelantan. Two rain gauge stations were selected from these two states to calculate the SPI results. From this study, statistics on the occurrence of dry and wet events in specified areas were determined based on the SPI readings for 3-month, 9-month, 12-month, and 24-month periods. To summarize, this research demonstrates the potential of SPI to enhance our understanding of extreme rainfall events in Peninsular Malaysia.
Switching On/Off Air Conditioner and Fan Alternately based on IoT Motion Detection and Room Temperature Abdul Shukora, Nurul Hidayah; Ahmad Baidowi, Zaid Mujaiyid Putra; Mohd Isa, Mohd Rizal; Darus, Mohamad Yusof; Abdullah, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2721

Abstract

The Internet of Things (IoT) connects electrical appliances that enable data transfer for communication without human intervention. IoT has evolved, and its implementation has been extended to residential areas. It can be said that all residents use fans as cooling appliances. In Malaysia, having a fan is not sufficient due to its hot temperature throughout the year. Therefore, most of the residents use air conditioners as an additional cooling appliance. Using air conditioners regularly could contribute to high energy consumption. Furthermore, excessive energy consumption occurs when an occupant of a residential building forgets to switch off electrical appliances such as fans and air conditioners. In addition, leaving electrical appliances turning on when nobody is at home just wastes energy. This work aims to develop an IoT-based smart home controlling system for minimizing energy consumption. This system enables automatic control that depends on room temperature and motion detection. Various types of sensors, such as temperature sensor, humidity sensor, and motion sensor, are used to switch on/off the air conditioner and fan. The air conditioner and fan will be alternately switched on and off depending on the ideal room temperature. The testing results show a significant reduction in energy consumption and a promising decrease in the electricity bill. Future works should be focusing on determining the over limit energy consumption. On top of that, this research would be best to try on simulation to get better results.
Brain Tumor Detection Through Image Enhancement Methods and Transfer Learning Techniques Thohari, Afandi Nur Aziz; Mountaines, Patricia Evericho; Mohd Isa, Mohd Rizal
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1262

Abstract

A brain tumor is dangerous and must be treated immediately to prevent worsening. The identification of brain tumors can be performed by a more in-depth examination by specialists or by using artificial intelligence technology through MRI datasets. Several studies have examined how artificial intelligence could be used to find brain cancer in MRI images. The algorithm usually used is CNN with the addition of transfer learning. Previous studies have produced very high accuracy, but the accuracy value can still be improved. In this study, MRI image quality is improved as a new input for modeling. The test results show that the proposed CNN Model produces an accuracy of 98.50% on the test data. This result is higher than the baseline method of 98.45%. Analysis of other metrics, such as precision, recall, and F1-score, indicates consistent performance across classes. These findings suggest that using preprocessing to improve image quality can improve Model performance. Using CLAHE and median blur to improve image quality can improve accuracy by 14.5%. This study contributes to identifying an effective combination of Model optimization techniques for image classification tasks.
Co-Authors Abdul Rauf, Ummul Fahri Abdul Shukora, Nurul Hidayah Abu Bakar Sajak, Aznida Afandi Nur Aziz Thohari, Afandi Nur Aziz Ahmad Baidowi, Zaid Mujaiyid Putra Akmal Jamaludin, Nor Azliana binti Jamaludin, Nor Azliana Akmal Darus, Mohamad Yusof Hendra Hidayat Ismail, Nur Hidayah Kamarudin, Nur Diyana Kamil, Mohammad Farizshah Ismail Khairuddin, Mohammad Adib Maskat, Kamaruzaman Mat Razali, Noor Afiza Moh Nazar, Nazatul Shiema Mohd Nazri Ismail, Mohd Nazri Mohd Shukran, Mohd Afizi Mohd Shukran, Mohd ‘Afizi Muhammad Abdullah Mukhtaruddin, Azharudin Osman, Muhammad Fendi Patricia Evericho Mountaines Rawi, Roziyani Razuki, Nurul Dayana Suresh Thanakodi, Suresh Yahaya, Yuhanim Hani Zainol, Zuraini