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All Journal International Journal of Artificial Intelligence International Journal of Education, Science, Technology, and Engineering (IJESTE) International Journal of Recent Technology and Applied Science (IJORTAS) Journal of Engineering, Technology, and Applied Science (JETAS)
Saad, Ahmad Fadli
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics

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Data Visualization Based On Sentiment Analysis to Identify the Quality of Internet Service Providers in Malaysia Abdul Latiff, Muhammad Nazirul Mubin; Saad, Ahmad Fadli; Yani, Achmad
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 5 No 2: September 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0502.572

Abstract

Nowadays, most of the people have subscribed to their own internet service providers. However, due to slow of speed, connectivity, quality, and customer satisfaction need a proper evaluation. In addition, the rate of internet adoption is still slow. This project aims to develop a web-based system of data visualization based on sentiment analysis of internet service providers in Malaysia. For the project methodology is conducted in three phases, which include studying the existing problem of the internet service providers in Malaysia, developing a web-based system of data visualization based on sentiment analysis, and the evaluation of the usability of the system. The System Development Life Cycle (SDLC) has been used in this project which are analysis, design, implementation, testing, maintenance, and usability. The quality of internet services is a crucial factor in determining the satisfaction of customers in the telecommunications industry. The data visualization system based on sentiment analysis can assist customers in making informed decisions when choosing the right Internet Service Provider (ISP) by providing a better understanding of the quality of internet services offered by different ISPs. The sentiment analysis approach also done by using the corpus-based approach. The evaluation of the usability result was done by 3 experts through questionnaires using the linear scale indicator to evaluate the usability of the system. The visualization of data is important which can help the user understand the information in detail.
Covid Classification System for Covid Detection Haris, Muhammad Akmal Husaini; Saad, Ahmad Fadli
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 1: April 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0601.610

Abstract

The ongoing global pandemic, which has now become an endemic has had a significant impact on the educational sector. Despite advancements in technology, there is no real- time prevention of COVID-19 transmission especially UiTM Tapah students that must go through crowd to reach health unit and have high possibility of spreading the disease. This research aims to develop a mobile application by utilizing machine learning for UiTM Tapah students to classify their COVID-19 status. To ensure a systematic and efficient development process, the research adopted the Mobile Application Development Life Cycle (MADLC) methodology. Within the application, the core of the machine learning functionality lies in the implementation of an Artificial Neural Network (ANN). By using 5,434 samples of data that had previously been classified by previous studies that analyzed student data such as symptoms to identify potential cases of the virus. The ANN approach performed greatly with the accuracy of 98%. Feedback from 32 respondents helped identify students' difficulties during the pandemic, which results in majority of the respondents agreeing that MCO affecting them adapting to online learning, and access to healthcare facilities. Furthermore, the usability testing conducted using the System Usability Scale (SUS) provided valuable insights into the system's user-friendliness and effectiveness. The results indicated a high level of usability, with a SUS score of 88.3 from 30 UiTM Tapah students while 76.25 from UiTM Tapah lecturers. This system was usable to classify the risk of infection among students who had not yet been diagnosed, while having some room for improvements. The system's ability to identify infected individuals promptly and accurately aided in controlling the spread of the virus, thereby protecting both students and staff. Thus, the classification system by using ANN was a valuable tool for public health organizations in their efforts to combat the COVID-19 pandemic.
Bird Species Classification System Using Transfer Learning Saad, Ahmad Fadli; Muhaiyuddin, Arina Syakirah; Iksan, Nur
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 2: August 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0602.651

Abstract

Manual recognition is limited by the observer's expertise and knowledge, which can lead to errors when observed by non-experts. The objective of this research is to create a machine learning algorithm that can accurately classify bird species based on physical features and develop a software system that includes the machine learning algorithm, allowing for efficient classification of different bird species. This research also wants to evaluate the accuracy of the approach in real-world scenarios. The methodology research uses the machine learning life cycle model and software development life cycle model. The research aims to provide a user-friendly interface that recommends bird species classifications based on uploaded images, ultimately contributing to a more accessible and informative bird identification experience. In this research, the F1-score with fine-tuning is reported as 0.8889. It is close to 1, it suggests a well-balanced performance in terms of correctly identifying positive instances which is precision, and capturing relevant positive instances which are recalled. Based on the result, the proposed system can enhance users' ability to accurately identify and classify various bird species through the utilization of a pre-trained convolutional neural network model.
Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School Mohamad Ghazali, Nor Diyana Syafiqah; Saad, Ahmad Fadli
International Journal of Artificial Intelligence Vol 9 No 2: December 2022
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-0902.457

Abstract

Today's technology has improved to the point that it can be utilized to execute many activities in daily life with minimum effort, and the world has acknowledged the worth of education in one's life. The schools have to analyze student performance manually, which requires a lot of time and effort from teachers to work on. However, the increasing amount of student data becomes difficult to analyze using traditional statistical techniques and database data management tools. The objective of this project is to study the current problems in the online student performance system. A preliminary survey of 30 respondents was conducted in order to gather information based on previous user experiences with the online student performance system. The next objective is to develop an Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School using Web Development Life Cycle that can visualize student performance systems to assist teachers and parents. Following that, this project employed a tool based on Multidimensional Data Visualization techniques. Google Charts and Dialogflow were used in this project to visualize the dashboard and construct a chatbot for the system. The last objective is to evaluate the usability of the system. There are three experts to test the project usability using the Post-Study System Usability Questionnaire (PSSUQ). The findings of the project can be used as a guideline to improve the system in the future. Overall, this project will assist teachers and parents in obtaining information about their students’ academic performance. The data about the students' performance can be displayed in the dashboard as a chart, graph, or diagram, and they can also communicate with the chatbot if they require assistance or guidance in using the system and obtaining their students' performance.
Dietry Monitoring System Using Decision Tree to Control Human Obesity Mat Baseri, Mohamad Faiz; Saad, Ahmad Fadli
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.485

Abstract

Nowadays, obesity is one of the dangerous diseases in the world. Lack of dietary monitoring system will make it difficult for people with obesity to reduce their weight problems. The main objective of this project is to develop a dietary monitoring system that can be used by everybody especially for obesity’s people. The method used in this study is to identify the strength and weaknesses of the existing system which involves reviewing some articles, journals, magazines, and books. The survey was conducted which involves 10 people answering the questionnaire. Respondent answered was used to improve the quality of the system. Next method is utilizing a waterfall model as a method to develop a dietary monitoring system. The system applied the decision tree technique to a classified food calorie. Therefore, the decision tree technique was used in developing this system. The last method used in this study involves the participation of three respondents to evaluate the usability of the system. Respondents need to answer whether they satisfied with the system or they can give suggestions for future improvement. The results of this study show that obesity is a public health issue that is rapidly increasing and must be addressed seriously by developing the system. Significant by developed this system such as helping obesity’s people to diet by giving them the guideline. In conclusion, this system will help people to diet using the decision tree technique for classifying food calories.
Career Finder System using Rule-Based Filtering for University Student Candidates Amir Hamzah, Fikri Nur Izzudin; Saad, Ahmad Fadli; Panessai, Ismail Yusuf
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.539

Abstract

As a current reality, students are frequently questioned about a suitable career path for the future, but they are unaware of the jobs offered by current industries. Moreover, students seeking university admission frequently encounter difficulties selecting courses and educational programs, and they are confronted with a variety of available courses. This research aims to make a mobile application for students to obtain employment career options appropriate to their educational qualifications because student is often asked about a suitable career for their future but have no idea about the available career path that appropriate. The methodology that implements in this research is Mobile Application Development Life Cycle (MADLC) that have four phases which is identification, design, development, and testing. The Visual Studio Code with Flutter plugin is used to develop the mobile application and its function. Firebase is used to get the database to store all the data and works as backend function of the application. The finished system was tested accordingly based on the functionality that listed all available function of the system. The system considers students' educational qualifications and academic achievements to provide personalized recommendations. This system can assist students in making career decisions and pursuing the right career path, saving them time, and reducing the risk of making wrong choices. This research indicates understanding the importance of career decision-making for students before continuing their university studies. In conclusion, this research seeks to enhance the ability of students to make decision of the available career path provided through recommendation system.
Point of Sale System Using Convolutional Neural Network for Image Recognition in Grocery Store Roslan, Naim Najmi; Saad, Ahmad Fadli
International Journal of Artificial Intelligence Vol 10 No 2: December 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01002.553

Abstract

The history of point of sale already has been told from a long time ago. The business nowadays is opting for the point-of-sale transactions because it was easy to sell the item to people face to face. This will build some trust between the cashier and the customer. The popular store that always customer need was the grocery store. However, the grocery store nowadays still not has a good feature for the point-of-sale system. The cashier still needs to scan the item through barcode scanner. This idea was led to make the point-of-sale transactions easier in the grocery store by applying the machine learning to the system. The problem for this project is the customer wait for a long time for their point-of-sale transactions to finish when bought the grocery items. The aim of this project is to detect the grocery items with convolutional neural network model for image recognition through camera within the main user interface. The Agile Development Life Cycle (ADLC) method is used in the development of Point-of-Sale System using Machine Learning for Image Recognition in Grocery Store. Moreover, this project is to evaluate the usability of the system using Post-Study System Usability Questionnaire (PSSUQ) approach. The PSSUQ evaluation is evaluated by the users of the system. The results of PSSUQ stated that the users satisfied with the system. The future research for this project is to make the point-of-sale system with a better model in the future. In conclusion, the system is works well and machine learning image recognition model also can detect the grocery item clearly.
Drone Based Fire Detection System Based on Convolutional Neural Network Rahman, Hanif Ikmal; Saad, Ahmad Fadli; Yani, Achmad
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.669

Abstract

Open fires are happening more and more throughout Malaysia. It is either intentional or accidental fire. The most dangerous is an accidental fire because it may not be detected by anyone until it becomes large. Detecting a fire is not an easy task. People may not see an ongoing fire because it may be too far away, or the fire may be too small. The objective of this project is to build a fire detection system. Fire detecting systems are developed to ensure more accurate fire detection. To ensure accurate fire detection, this project uses a waterfall methodology. This project uses drones as a tool to help with fire detection. Using a Convolutional Neural Network (CNN), this project implements the use of the PyTorch framework in detecting fires. The testing was done with a distance of 2 meters from the fire and a height of 2 meter from the ground. Edited images were used and uploaded to the system. Accuracy results of 80% can ensure accurate fire detection. To evaluate the system, edited fire images are used to ensure the accuracy of the system. Therefore, CNN is a good tool for detecting fires.
Online Convenience Store System using Multidimensional Data Visualization Zainal, Nisa Athirah; Saad, Ahmad Fadli
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 6 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0601.486

Abstract

The explosion of e-commerce businesses has seen the growth number of the online businesses worldwide. Retailers nowadays are opting with e-commerce platform to manage and monitor the business activities which is considerably convenience such as visualizing the sales data. However, Serbaneka is still using traditional way to store the sales data such as in spreadsheet papers which lead to data loss or misplaced the sales record. The objective of this research is to study the current usage of e-commerce system among hostel students in UiTM Tapah. A preliminary survey was carried out by eight selected hostel students in order to generate a list of criteria based on students' previous experiences with e- commerce systems. The following objective is to develop a web- based convenience store system using multidimensional data visualization for Serbaneka UiTM Tapah. The System Development Life Cycle (SDLC) method is used in the development of Online Convenience Store System for Serbaneka UiTM Tapah. Besides, multidimensional data visualization is used as method in visualizing the sales data of Serbaneka UiTM Tapah. Other than that, this research is to evaluate the usability of the system using Post-Study System Usability Questionnaire (PSSUQ) approach. The PSSUQ evaluation is evaluated by three selected experts. The results of PSSUQ evaluation stated that the experts satisfied with the development of Online Convenience Store System using Multidimensional Data Visualization for Serbaneka UiTM Tapah. This is due to the system's ability to assist retailers Serbaneka UiTM Tapah in managing and monitoring the business activities.
Brand Logos Recognition System Using Image Processing for Food and Beverage Brands Mohamad Roslan, Aini Khadijah; Saad, Ahmad Fadli
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 3: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0603.768

Abstract

This study investigates the development of a Brand Logo Recognition (BLR) system employing Convolutional Neural Networks (CNNs), specifically designed for the food and beverage industry in Ipoh. Accurate logo recognition is vital for businesses to strengthen brand identity, monitor consumer engagement, and mitigate the misuse of counterfeit logos. Existing systems often encounter challenges related to variations in logo design, image quality, and lighting conditions. To address these issues, the research adopts a hybrid methodology that integrates the Machine Learning Life Cycle and the Software Development Life Cycle (SDLC), utilizing an iterative Agile development framework. The system incorporates CNN models for feature extraction and classification, complemented by Single Shot Detector (SSD) algorithms for object detection. A curated dataset of food and beverage logos underwent preprocessing techniques, including resizing, normalization, and augmentation, to enhance the model’s generalization capabilities. Empirical results demonstrate high accuracy in detecting and classifying logos across diverse conditions, underscoring the effectiveness of the CNN-SSD architecture. The proposed system offers practical applications for marketing analytics and consumer research, empowering local businesses to refine branding strategies and improve customer engagement. Future research directions include the exploration of multi-label classification, real-time processing, and the integration of advanced methodologies, such as generative adversarial networks (GANs), for counterfeit logo detection. This study emphasizes the transformative potential of AI-driven logo recognition systems in revolutionizing marketing practices and supporting small and medium-sized enterprises (SMEs).
Co-Authors Abdul Latiff, Muhammad Nazirul Mubin Amir Hamzah, Fikri Nur Izzudin Haris, Muhammad Akmal Husaini Manaf, Abdi Mat Baseri, Mohamad Faiz Mohamad Ghazali, Nor Diyana Syafiqah Mohamad Roslan, Aini Khadijah Muhaiyuddin, Arina Syakirah Nur Iksan Panessai, Ismail Yusuf Rahman, Hanif Ikmal Roslan, Naim Najmi Shaharudin, Muhammad Hairil Yani, Achmad Zainal, Nisa Athirah