Suhizaz Sudin
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Preferred name
Suhizaz Sudin
Official Name
Suhizaz, Sudin
Alternative Name
Sudin, S.
Main Affiliation
Scopus Author ID
26422542800
Researcher ID
D-2964-2015

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Ergonomic intervention study among woodball players

2020 , M. F. Abd Razak , A. H. Mazelan , Syamir Alihan Showkat Ali , Suhizaz Sudin

This paper proposed a feasibility study on the ergonomics intervention among the Woodball athletes. The objectives of this study are to identify the ergonomics risk factors that may cause harm to Woodball athlete by using the Initial Ergonomic Risk Assessment (ERA) and the Advanced ERA method. The ergonomic assessment of an athlete was focussed on the three different woodball exercises, namely the tee-off comprising of short tee-off, medium tee-off and long tee-off. The advanced ergonomic risk assessments were performed on the upper body part of an athlete such as the neck, shoulder, upper arm, hand or wrist and upper back by using the RULA employee assessment worksheet. Based on the findings and assessment done, the proper body posture is then proposed through the observation from the results of the initial and advanced ERA. A simulation using the Human Digital Modelling (HDM) was also performed to better analyse the ergonomic aspect of the selective body postures. The results of the simulation were then compared to the results of the RULA assessment. The improvised results from the RULA analysis were found to be able to reduce the ergonomics risk of the current and improvised posture. The improvised posture will be useful for future studies in order to obtain a more ideal body posture for the Woodball players.

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A Review of Chatbot development for Dynamic Web-based Knowledge Management System (KMS) in Small Scale Agriculture

2021-03-01 , Ong R.J. , Rafikha Aliana A Raof , Suhizaz Sudin , Choong K.Y.

Market data indicates that the average age of Malaysian farmers to be 50 years old and that the majorities are in the B40 group. Malaysia have so much of land with a small population of over 30 million compared with neighbours country and yet still need to import over 50 billion in food commodities annually to feed the nation. Small-scale farmers are having issues in communicating with each other and usually lack of Standard Operating Procedure (SOP) compare to industrial farming. An information sharing platform is prominent to disseminate information and knowledge between farmers especially for most of the young farmers who having issues when they newly start to involve in agriculture field. This paper is about to design and develop a framework of dynamic web-based knowledge management system with Chatbot application in order to utilize the information sharing platform to disseminate knowledge and build networks among small-scale farmers and related experts. Thus, information sharing and working together with a related expert will effectively improve both the quality and quantity of the product and also against the diseases on the spot.

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Dynamic web-based knowledge management system (KMS) in small scale agriculture

2024-02-08 , Ong R.J. , Suhizaz Sudin , Rafikha Aliana A Raof , Choong K.Y. , Azremi Abdullah Al-Hadi , Yacob Y. , Nasir S.N.B.M.

The shift from paper-based to web-based practice is a significant trend, as paper-based practice consumes time, is prone to error, and is more prone to data loss, all of which lead to inefficiency. Smart farming or agriculture 4.0 defined the new era of agriculture that moving toward digitalization. However, the absence of a centralized information system, which results in information asymmetry among agricultural sectors. One of the keys to success is reaching out to farmers with pertinent agricultural information at the appropriate time and platform. The purpose of this study is to design and create a dynamic web-based knowledge management system for the dissemination of agricultural knowledge in Malaysia. A centralized information system capable of achieving information symmetry, in which all important information is accessible to all participants. Creating an advanced learning environment allowing for virtual engagement between external and internal stakeholders via a web-based knowledge management system.

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Analysis On the Effect of The Length of Training and Test Set to The Accuracy of SARIMA Forecasting

2024-06-07 , Choong K.Y. , Rafikha Aliana A Raof , Suhizaz Sudin , Ong R.J.

The Seasonal Autoregressive Integrated Moving Average (SARIMA) models which built upon the ARIMA models to support seasonality, are used when it encounter the periodic time series data. It has been widely used in infectious disease prediction and other fields where data indicate a seasonal pattern. However, there are lacks of research focuses on the impact of the length of training and test data on the forecasting accuracy. In this study, the data is split into two parts: Training and Test data. The selected SARIMA model will be fitted for different lengths of training data and forecast the observations of the length of test data. This study aims to investigate how the length of training and test data affect the forecasting accuracy. In order to check it, the Mean Absolute Percentage Error (MAPE) for different lengths of training data are calculated and compared to study their relationship.

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Time Series Analysis for Vegetable Price Forecasting in E-Commerce Platform: A Review

2021-06-11 , Choong K.Y. , Rafikha Aliana A Raof , Suhizaz Sudin , Ong R.J.

Vegetables industry plays an important role especially in providing the abundant fresh agricultural products. Forecasting the vegetable price is vital in agriculture sector for effective decision making. In Malaysia, the problems faced by the farmers are not only their age, but also their competitive skill where the wholesale market and the hypermarket/supermarket are prioritized by the consumers in Malaysia for the fresh vegetables and fruits. This review article helps to recognize the current problems faced by the agricultural sector of Malaysia and study the relationship between the agriculture and E-Commerce. Recent researchers have mentioned the growth of the E-Agribusiness and the authors found the potential of an Agricultural E-Commerce platform with price forecasting model in solving the current national issue. This research reviews the existing agricultural E-Commerce platforms in worldwide and try to compare with the local one. After the reviews have been done, the authors bring up an idea in constructing the time analysis model in hybrid approach for veggies price forecasting in an agricultural E-Commerce platform which can be used by the government in deriving their policies.

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Examining the trend of the research on the internet of things (IoT): A bibliometric analysis of the journal articles as indexed in the Scopus database

2020-06-17 , Raja Mohd Ali R.H. , Ahmi A. , Suhizaz Sudin

Studies related to the Internet of Things (IoT) has grown tremendously since 2010 even though this topic has been found published in Scopus since 1970. Several studies have been published in the Scopus database to analyse the trend in this topic. However, it does not account for the whole years (since 1970 up to 2019). This study employs a similar bibliometrics technique to study the trend in IoT by taking into account the first year it was published until today. Results show that this topic has grown extensively since 2010 until today. The top two journals are IEEE Internet of Things Journal and IEEE Access with more than 500 articles. This topic has been studied quite extensively in computer science and engineering discipline. Apart from IoT or "Internet of Things", the top three keywords used are "wireless sensor networks", "network security", and "energy efficiency". While most of the publications were written in English, the majority of the publications were contributed by Asian countries of which the English language is not their native language. The results of this study might contribute to the body of knowledge on providing comprehensive trend analysis of the studies related to IoT, which was published in the Scopus database from the year 1970 to 2019.

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Hybrid approach for vegetable price forecasting in electronic commerce platform

2024-06-01 , Choong K.Y. , Suhizaz Sudin , Rafikha Aliana A Raof , Ong R.J.

The significance of the agriculture sector in Malaysia is often overlooked, and there is a notable deficiency in the advancement of digitalization within the country’s agricultural domain. The integration of a price forecasting model in the platform enables the relevant parties, including farmers, to make informed decisions and plan their crop selection based on projected future prices. In this research, the authors proposed the hybrid approach with the combination of a linear model and a non-linear model in doing the vegetable price forecasting model. The hybrid model combining seasonal autoregressive integrated moving average (SARIMA)-discrete wavelet transform (DWT)-genetic algorithm neural network (GANN), referred to SARIMA-DWT-GANN, was used to forecast monthly vegetable prices in Malaysia. The historical vegetable price data is collected from the federal agricultural marketing authority Malaysia and split into training/test sets for modeling. The performance of the models is evaluated on the accuracy metrics including mean absolute error (MAE), mean absolute percentage error, and root mean square error (RMSE). The forecasted results using the proposed hybrid model are compared to those using the single SARIMA model. In conclusion, the hybrid SARIMA-DWT-GANN model is superior to the individual model, which obtained the smaller MAE and RMSE, and got the forecast accuracy of at least 95%.

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Chatbot Application Training Using Natural Language Processing Techniques: Case of Small-Scale Agriculture

2024-06-07 , Ong R.J. , Rafikha Aliana A Raof , Suhizaz Sudin , Choong K.Y.

Tacit knowledge, which is based on first-hand experience and is more difficult to articulate, has evolved alongside natural languages as they are passed down through the years. In computing, Natural Language Processing (or NLP) refers to a set of methods for studying and modelling human languages that may be studied and represented automatically. Extracting or searching through vast bodies of unregulated text for specific information can be a complex and time-consuming process. Knowledge comes in several shapes and sizes, but can usually be differentiated into two types: structured or unstructured. Using NLP techniques, unstructured text data can be translated into a structured and well-organized database and then used for question-answering purposes. This paper is about the implementation of NLP techniques to convert unstructured text data into a structured database for Chatbot application training.

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A framework design of web-based knowledge management system with NoSQL database

2023 , Rhui Jaan Ong , Rafikha Aliana A Raof , Suhizaz Sudin , Kar Yan Choong

In today's dynamic and information-rich environment, effective knowledge management has emerged as a critical determinant for organizations seeking to take advantage of intellectual resources. Nevertheless, it is unrealistic to expect experts who lack a background in Information and Communications Technology (ICT) to possess an advanced level of technological proficiency or linguistic analytical skills. This study provides a comprehensive framework targeted at developing a Web-based Knowledge Management System (WKMS) coupled with chatbot application that is precisely tuned to meet the complex challenges of organizing, disseminating, and utilizing knowledge related to agriculture sector. This paper elaborates on a framework from both functional and technical perspectives, including the identification of knowledge origins, the establishment of mechanisms for capturing and sharing knowledge, and the facilitation of collaborative knowledge generation. This study employing a methodological approach and adopts a three-tier architecture for framework construction, coupled with text processing for data preparation. The findings emphasize that a successful WKMS should prioritize not only technology infrastructure but also strategies for building a culture favorable to information exchange. This paradigm lays the groundwork for enhanced discovery, innovation, and decision-making in a variety of professional fields by fusing theoretical insights and pragmatic considerations.

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Enhancing vegetable price forecasting accuracy: a hybrid SARIMADWT-GANN approach

2023 , Kar Yan Choong , Suhizaz Sudin , Rafikha Aliana A Raof , Rhui Jaan Ong

In the context of Malaysia's agricultural sector, the development of a robust vegetable price forecasting model holds paramount importance. The pricing dynamics of vegetables can significantly affect various stakeholders, including farmers, distributors, and consumers. The agricultural sector in Malaysia encounters persistent challenges such as supply-demand imbalances, seasonal variations, and market uncertainties, which can lead to income disparities for farmers and disruption in supply chains. Addressing these issues requires accurate and timely predictions of vegetable prices to enhance planning, resource allocation, and decision-making. This study introduces an innovative SARIMA-DWT-GANN hybrid model for vegetable price forecasting. By fusing the strengths of traditional time series modeling with the capabilities of neural networks, the proposed model offers a comprehensive solution to capture both linear and non-linear price patterns. The results demonstrate the superiority of the SARIMA-DWT-GANN model over the individual SARIMA model, as evident from correlation coefficients that closely approach unity and p-values confirming statistical significance. The model's ability to predict price changes has significant implications for making informed decisions throughout the agricultural supply chain. This research provides a robust forecasting tool that not only enhances market efficiency and profitability but also offers a promising solution to address the challenges in Malaysia's agricultural sector.

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