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

Research Output

Filters

6
4
6
Reset filters

Settings
Now showing 1 - 6 of 6
  • Publication
    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.
  • Publication
    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.
  • Publication
    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%.
  • Publication
    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.
  • Publication
    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.
  • Publication
    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.