Browsing by Author "Tamrin, M.I.M."

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Adopting e-hailing Application Among Malaysian Millennials
(The 7th International Conference on Cyber and IT Service Management (CITSM), Jakarta Convention Center – Jakarta, 2019) Razi, M.J.M.; Tamrin, M.I.M.; Nor, R.M.
e-hailing apps dominate the public taxi transport sector all over the globe. Different researchers study this disruptive business model from a different perspective. The current researchers look at this phenomenon from the technology acceptance perspective. Technology Acceptance Model (TAM) make the base for the study. The variables Performance Expectancy (PE) and the Effort Expectancy (EE) and another two variables Trust, and Enjoyment are also considered in this study. Data were collected from 352 university students who are millennials. Out of the four hypotheses proposed, Effort Expectancy (H2), Trust (H3), and Enjoyment (H4) positively influence Intention. The hypothesis related to PE (H1) was not supported. Implications are discussed.
BENCHMARKING OF HALAL FOOD PRODUCTS USING SIMILARITY MEASURES - A CONCEPTUAL RETRIEVAL MODEL
(Journal of Information Systems and Digital Technologies, 2019) Tamrin, M.I.M.; Turaev, S.; Azemin, M.Z.C.; Razi, M.J.M.; Maifiah, M.H.M.
Muslims are concerned with the Halal status of food products sold in the supermarkets. Many products that are imported from overseas are not certified by JAKIM. In this paper, we proposed a conceptual model for benchmarking food products against certified Halal products. Our motivation is to provide similarity measurement between certified and non-certified food products based on their ingredients. This model comprises three main phases: ingredient acquisition, ingredient transformation and similarity measures calculation. In the first phase, web crawlers are employed to retrieve product information from JAKIM online database and supermarket web pages. In the second phase, an index structure will be constructed to allow faster ingredient retrieval which will be used for similarity calculation. In the last phase, Euclidian distance, cosine similarity measure and Jaccard correlation coefficient will be used to measure the similarities between two products. Our proposed model is to complement but not to replace the existing JAKIM procedure to verify food products by empowering Muslim consumers with informed decision making.
Influencing Factors of Social Commerce Behavior in Saudi Arabia
(IEEE Digital Library, 2019) Razi, M.J.M.; Sarabdeen, M.; Tamrin, M.I.M.; Kijas, A.C.M.
Social commerce is getting popular all over the world including in the middle eastern countries. The main objective of this work is to identify the factors that influence the purchasing intention and the behavior among the Y generation and millennials in the Kingdom of Saudi Arabia. For this purpose, a hypothetical conceptual model was developed based on proven theories and well-established literature. To test this model, data were collected from 178 university students using an online questionnaire. Data were analyzed using SPSS 25. The validity and the reliability of the questionnaire items were determined through factors analysis and Cronbach Alpha. All hypotheses were supported in linear regression analysis, however, the stepwise multiple regression analysis which shows the simultaneous effects of the independent variables, resulted in that out of 11 hypotheses 3 were not supported. Based on the findings a discussion was developed at the end of the paper.
Supervised Identification of Acinetobacter Baumanni Strains Using Artificial Neural Network
(Journal of Information Systems and Digital Technologies, Vol. 1, No. 2, 2019) Tamrin, M.I.M.; Maifiah, M.H.M.; Azemin, M.Z.C.; Turaev, S.; Razi, M.J.M.
In hospital environments around the world bacterial contamination is prevalence. One of the most commonly found bacteria is the Acinetobacter Baumannii. It can cause unitary tract, lung, abdominal and central nervous system infection. This bacteria is becoming more resistant to antibiotics. Thus, identification of the non-resistant from the resistant bacteria strain is of important for the correct course of treatments. We propose to use the artificial neural network (ANN) for supervised identification of this bacteria. The mass spectra generated from the liquid chromatography mass spectrometry (LCMS) were used as the features to train the ANN. However, due to the massive number of features, we applied the principle component analysis (PCA) to reduce the dimensions. Less than 1% of the original number of features were utilized. The hand out validation method confirmed that the accuracy, sensitivity and specificity are 0.75 respectively. In order to avoid selection biasness in the sampling, 5-fold cross validation was performed. In comparison, the average accuracy is close to 0.75 but the average sensitivity is slightly higher by 0.50