Muhammad SyafrudinSejong University | sejong · Department of Artificial Intelligence and Data Science
Muhammad Syafrudin
Doctor of Engineering
Teach. Learn. Research. Collaborate. Repeat.
About
84
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Introduction
His research interests include Industrial Artificial Intelligence (IAI), Industrial Analytics (IA), Industrial Informatics (II), Industrial IoT (IIoT), Industrial Big Data (IBD), etc. Feel free to contact him if you have any questions and/or are interested in research collaboration in the relevant areas of our shared interest. Visit our team at aintlab.com/team
Additional affiliations
March 2022 - February 2024
March 2019 - February 2022
Publications
Publications (84)
Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the...
With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-t...
Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors...
Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual’s risk factors data. The proposed DPM consis...
Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs)...
In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and re...
Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in...
As the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary data and an extensive bibliographic revie...
In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing...
Dissatisfaction among upper limb prosthetic users is high, reaching over 70%, and 52% of upper limb amputees abandon their prosthetic devices due to limitations such as limited functionality, poor design/aesthetic, and improper fit. The conventional procedure of making prosthetics is time-consuming and expensive. This study was conducted to provide...
Hospitality services play a crucial role in shaping tourist satisfaction and revisiting intention toward destinations. Traditional feedback methods like surveys often fail to capture the nuanced and real-time experiences of tourists. Digital platforms such as TripAdvisor, Yelp, and Google Reviews provide a rich source of user-generated content, but...
This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research i...
To analyze the security of encryption, an effectual encryption scheme based on colored images utilizing the hybrid pseudo-random binary sequence (HPRBS) and substitution boxes, known as S-boxes, is proposed. The presented work aims to design S-boxes using pseudo-random binary numbers acquired by Linear Feedback Shift Registers (LFSRs) in combinatio...
Lower back pain (LBP) is a musculoskeletal condition that affects millions of people worldwide and significantly limits their mobility and daily activities. Appropriate ergonomics and exercise are crucial preventive measures that play a vital role in managing and reducing the risk of LBP. Individuals with LBP often exhibit spinal anomalies, which c...
This study aims to provide a conceptual analysis of the dynamic transformations occurring in an autonomous vehicle (AV), placing a specific emphasis on the safety implications for pedestrians and passengers. AV, also known as self-driving automobiles, are positioned as potential disruptors in the contemporary transportation landscape, offering heig...
Predicting blood glucose levels in the future can help diabetic patients to take preventive action earlier so that they can control their blood glucose levels. This study proposed a blood glucose levels prediction model using linear regression method. Time series data of blood glucose levels from 30 type 1 diabetic patients were used as a single in...
The proposed publication, titled "Artificial Intelligence and Data Science for Sustainability: Applications and Methods," aims to explore the applications of artificial intelligence (AI) and Data Science (DS) techniques and methodologies in addressing various sustainability challenges. The book will bring together cutting-edge research, case studie...
BACKGROUND: An increase in the demand for quality of life following spinal cord injuries (SCIs) is associated with an increase in musculoskeletal (MSK) pain, highlighting the need for preventive measure research. OBJECTIVE: This study aimed to evaluate the incidence and hazards of MSK morbidities among Korean adults with SCIs, as well as the influe...
In this study, we designed a high-performance, compact E-shaped microstrip antenna optimized for intelligent transportation systems, operating at 5.8 GHz. Utilizing simulation tools such as CST Studio Suite 2022 Learning Edition, Ansys HFSS 2022 R1, and MATLAB 2022b PCB Antenna Designer, we ensured consistent physical parameters. Fabricated with a...
The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural network...
In the contemporary era, modern civilization is immersed in a technologically interconnected environment, where numerous applications within the digital ecosystem harness advanced artificial intelligence (AI) techniques [...]
The mangrove ecosystem is crucial for addressing climate change and supporting marine life. To preserve this ecosystem, understanding community awareness is essential. While latent Dirichlet allocation (LDA) is commonly used for this, it has drawbacks such as high resource requirements and an inability to capture semantic nuances. We propose a tech...
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in differ...
This research investigates consumer reviews of eco-friendly products on Amazon to uncover valuable sustainability insights that can inform design optimization. Using natural language processing (NLP) techniques, including sentiment analysis, key terms extraction, and topic modeling, this research reveals diverse perspectives related to sustainabili...
Companies are beginning to utilize the metaverse to broaden their service network and create new co-creation value for their clients. To better understand how the metaverse phenomena could impact corporate sustainability, investigative research should be conducted. In this chapter, the authors looked at the moral issues raised by businesses using m...
These days, numerous online reviews for restaurants are available on the Internet. People often refer to these reviews to gain insights and make decisions about which restaurants they would like to visit. However, sifting through a large number of reviews can lead to confusion due to the abundance of lengthy texts. To address this issue, our study...
Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learn...
The implementation of Industry 4.0 technology has developed rapidly. Despite its development, Indonesia is still nascent and requires some implementation monitoring in its priority industries. This study aims to design a system for assessing the readiness of implementing Industry 4.0 in priority manufacturing industries in Indonesia. A Fuzzy Infere...
Topic modeling is an important and interesting research area that can assist in discovering patterns and underlying themes in large datasets. This research aims to identify commonly used topics in previous undergraduate thesis research through text mining using Latent Dirichlet Allocation (LDA) as the topic modeling method. The study utilizes abstr...
Flood disasters, a natural hazard throughout human history, have caused significant damage to human safety and infrastructure. This paper presents a systematic study using databases from Springer Link, Science Direct, JSTOR, and Web of Science. The study employs the PRISMA report analysis method to examine 11 flood disaster case studies between 201...
A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in d...
Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are worldwide chronic diseases that have strong relationships with one another and commonly exist together. Type 2 diabetes is considered one of the risk factors for NAFLD, so its occurrence in people with NAFLD is highly likely. As the high and increasing number of T2D and NAFLD,...
Video surveillance and activity monitoring are the practical real-time applications of Human Action Recognition (HAR). A fusion of several Convolutional Neural Network (CNN) architectures has been widely used for effective HAR and achieved impressive results. Feature fusion of multiple pre-trained models also extracts redundant features due to the...
The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate a high cost of energy production....
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of m...
It is expected that the metaverse will be the next generation of the internet and its online activities and will serve as the foundation for a massive online business platform. Furthermore, it will alter people's behavior, which will have an impact on businesses' decisions to choose the metaverse as their primary business platform.
Metaverse Appli...
Recently, the development of a rapid detection approach for glaucoma has been widely proposed to assist medical personnel in detecting glaucoma disease thanks to the outstanding performance of artificial intelligence. In several glaucoma detectors, cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) play roles as the major objects that...
Detecting snow-covered solar panels is crucial as it allows us to remove snow using heating techniques more efficiently and restores the photovoltaic system to proper operation. This paper presents classification and detection performance analyses for snow-covered solar panel images. The classification analysis consists of two cases, and the detect...
The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and discovery. KM necessitates ML for improved organisational experiences, particularly in making knowledge management more discoverable and shareable. Machi...
Companies are beginning to utilize the metaverse to broaden their service network and create new co-creation value for their clients. To better understand how the metaverse phenomena could impact corporate sustainability, investigative research should be conducted. In this chapter, the authors looked at the moral issues raised by businesses using m...
Abstract: Drought analysis via the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) is necessary for effective water resource management in
Sarawak, Malaysia. Rainfall is the best indicator of a drought, but the temperature is also significant
because it controls evaporation and condensation....
Multi-category vessel detection and classification based on satellite imagery attract a lot of attention due to their significant applications in the military and civilian domains. In this study, we generated a new Artificial-SAR-Vessel dataset based on the combination of the FUSAR-Ship dataset and the SimpleCopyPaste method. We further proposed a...
Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry w...
Businesses are starting to use the Metaverse to expand their service network and establish new value co-creation for customers. However, businesses may need to carefully assess the ethical implications of their data collection and utilisation procedures for business sustainability. This paper examines the ethical concerns surrounding the usage of t...
Predicting future glycemic events such as hypoglycemia, hyperglycemia, and normal for type 1 diabetes (T1D) remains a significant and challenging issue. In this study, an artificial neural network (ANN)-based model is proposed to predict the future glycemic events of T1D patients. We utilized five T1D patient datasets to build the models and predic...
Risk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian popula...
This research project aimed to provide an environmentally friendly method for the decolorization and biosorption of synthetic dye by utilizing fungi as biosorbents. The study was carried out by first growing the fungi in solid medium and then using the fungi as biosorbent to absorb dye in aqueous solution. In the first stage, screening experiments...
The analysis of influential machine parameters can be useful to plan and design a plastic injection molding process. However, current research in parameter analysis is mostly based on computer-aided engineering (CAE) or simulation which have been demonstrated to be inadequate for analyzing complex behavioral changes in the real injection molding pr...
Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a...
Water is a vital resource to every living thing on the earth. Once the water is contaminated (physically, chemically, biologically, or radiologically), it brought negative impacts to the living thing. This paper provides a brief review of the characterization of biological pollutants in drinking water and their effects on human health. Some biologi...
Predicting future blood glucose (BG) values for diabetic patients, particularly for type 1 diabetes (T1D), remains an important and challenging issue. To overcome it, several well-known machine learning models have been used in recent years. Thus, a personalized model based by using random forest (RF) regression is implemented to forecast the futur...
Prediction of blood glucose (BG) values in type 1 diabetes (T1D) remains an essential and challenging issue. Recently, machine learning methods have been used to solve this problem. We present a forecasting model based on extreme gradient boosting (XGBoost) regression to estimate the BG value of T1D patients in this study. We developed the models u...
Accuracy improvement of classification model becomes main research objective in various fields. Selecting important features and removing outliers of a dataset are two effective solutions for improving model accuracy. Information Gain is one of the feature selection methods that can be considered as a solution for selecting important features of a...
The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous robots,...
An essay was used as an assessment to illustrate a certain way of thinking and attitude of a student. The essay demonstrates the student's utmost degree as an author with a cognitive and affective style. The study aimed to provide a simple description of the essay as an assessment and critical function in the Groundwater Contamination course of the...
The Baram River is one of the largest rivers in Sarawak, where many large industries, such as plywood, sawmills, shipyards, interisland ports, and other wood-based industries are located along the river.
Microplastic contamination has become a widespread and growing concern worldwide because of the small sizes of microplastics and their presence in...
The ubiquitous problem of pesticide in aquatic environment are receiving worldwide concern as pesticide tends to accumulate in the body of the aquatic organism and sediment soil, posing health risks to the human. Many pesticide formulations had introduced due to the rapid growth in the global pesticide market result from the wide use of pesticides...
The Internet of Things (IoT)–based sensors together with smartphone can be utilized as personal health devices to gather vital signs data, so that current health condition of patient can be presented. In this study, we propose a health-care monitoring system by utilizing an IoT-based sensor device and prediction model, so that diabetes patients can...
Self-care classification for children with physical disability remains an important and challenging issue. It needs the support from occupational therapists to make decision. Data-driven decision making have been widely adopted to make decision based on the data with help of expert systems and machine learning algorithms. In this study, we develope...
Diabetes is the number one of major causes of death globally. Undetected and untreated diabetes causes serious issues and the individuals with diabetes are at high risk for complication. Thus, an early diabetes prediction is necessary to help the individuals preventing dangerous conditions at the early stage. This study proposed a prediction model...
Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domai...
Nowadays, customer's health awareness is of extreme significance. Food can become contaminated at any point during production, preparation and distribution. Therefore, it is of key importance for the perishable food supply chain to monitor the food quality and safety. Traceability system offers complete food information and therefore, it guarantees...
Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a dee...
Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects’ heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clus...
Understanding customer shopping behavior in retail store is important to improve the customers' relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavi...
Predicting future blood glucose (BG) level for diabetic patients will help them to avoid critical conditions in the future. This study proposed Extreme Gradient Boosting (XGBoost), an ensemble learning model to predict the future blood glucose value of diabetic patients. The clinical dataset of Type 1 Diabetes (T1D) patients was utilized and the pr...