Islamic Da'wah has grown in Terengganu began before the year 1303 AD missionary in extended periods of time, has managed to adopt Islamic values in Malay heritage. The purpose of writing this article is to detail the findings of research against some artistic practices that have been absorbed by Islamic art. This study uses text analysis method (content analysis) and documentation review and then processed in a descriptive writing. Analysis carried out on the facts of history and current information related to the practice of the arts in the community. The outcomes of this study verify that the application of Islamic values could change the Malay traditional arts heritage. Limitations of this study focuses on Malay language Jawi writing, learning the Quran, architecture, calligraphy, crafts and martial arts in the state of Terengganu. The impact of this study is able to enlighten the community that is very important to know the art heritage from the Islamic perspective and appreciate the art of Malay heritage.
Abstract. In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM-ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting.
Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method.
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