Rafikha Aliana A Raof
Thumbnail Image
Preferred name
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

Research Output

Filters

19 1
19
Reset filters

Settings
Now showing 1 - 10 of 19
  • 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
    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.
  • Publication
    Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
    ( 2020-08-01)
    Vijayasarveswari V.
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    R Badlishah Ahmad
    ;
    Khatun S.
    ;
    Hasliza A Rahim @ Samsuddin
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multistage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
  • Publication
    Reliable Early Breast Cancer Detection using Artificial Neural Network for Small Data Set
    ( 2021-03-01)
    Vijayasarveswari V.
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Khatun S.
    ;
    Iszaidy I.
    This paper proposes a breast cancer detection module using Artificial Neural Network for small data set. The developed system consists of hardware and software. Hardware included UWB transceiver and a pair of home-made directional sensor/antenna. The software included a Graphical User Interface (GUI) and k-fold based feed-forward back propagation Neural Network module to detect the tumor existence, size and location along with soft interface between software and hardware. Forward scattering technique is used by placing two sensors diagonally opposite sides of a breast phantom. UWB pulses are transmitted from one side of phantom and received from other side, controlled by the software interface in PC environment. Firstly feed forward backpropagation neural network (FFBNN) is developed. Then, k-fold is combined with developed FFBNN for testing purpose. Four data sets are created where contains 125, 95, 65 and 30 data samples in 1st,2nd,3rd and 4th data set respectively. Collected received signals were then fed into the NN module for training, testing and validation. The process is done for all data sets separately. The system exhibits detection efficiency of tumor existence, location (x, y, z), and size were approximately 87.72%, 87.24%, 83.93% and 80.51% for 1st, 2nd, 3rd and 4th data set respectively. The proposed module is very practical with low-cost and user friendly. The developed breast cancer detection module can be used for large data samples as well as for minimum data samples.
  • 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
    An introduction to double stain normalization technique for brain tumour histopathological images
    ( 2024)
    Fahmi Akmal Dzulkifli
    ;
    Mohd Yusoff Mashor
    ;
    Rafikha Aliana A Raof
    ;
    Hasnan Jaafar
    Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.
  • 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.
  • 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
    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
    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.