Rafikha Aliana A Raof
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Rafikha Aliana A Raof
Official Name
Rafikha Aliana, A Raof
Alternative Name
Raof, Rafikha Aliana A.
Raof, R. A.A.
Raof, Rafikha Alaina A.
Main Affiliation
Scopus Author ID
57075005500

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  • Publication
    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.
      21  2
  • Publication
    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.
      1  7
  • Publication
    Hybrid approach for vegetable price forecasting in electronic commerce platform
    (Institute of Advanced Engineering and Science (IAES), 2024-06)
    Kar Yan Choong
    ;
    Suhizaz Sudin
    ;
    Rafikha Aliana A Raof
    ;
    Rhui Jaan Ong
    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%.