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A common problem in regression analysis using ordinary least squares (OLS) is the effect of outliers or contaminated data on the estimates of the parameters. A robust method that is not sensitive to outliers and can handle contaminated data is needed. In this study, the objective is to determine the significant parameters that determine the moisture content of the seaweed after drying and develop a hybrid model to reduce the outliers. The data were collected with sensors from the v-Groove Hybrid Solar Drier (v-GHSD) at Semporna, South-Eastern Coast of Sabah, Malaysia. After the second order interaction, we have 435 drying parameters, each parameter has 1914 observations. First, we used four machine learning algorithms, such as random forest, support vector machine, bagging and boosting to determine the significant parameters by selecting 15, 25, 35 and 45 parameters. Second, we developed the hybrid model using robust methods such as M. Bi-Square, M. Hampel and M. Huber. The results show that there is a significant improvement in the reduction of the number of outliers and better prediction using hybrid model for the contaminated seaweed big data. For the highest variable importance of 45 significant drying parameters of seaweed, the hybrid model bagging M Bi-square performs better because it has the lowest percentage of outliers of 4.08 %.

This paper presents detection of climate change in Penang Island by using precipitation data based on interpolation technique. Climate change brings about vast and everlasting effects on all living creatures on the Earth. These effects are especially detrimental towards heritage sites, landscapes and businesses based in Penang Island, Malaysia. This study focuses mainly on investigating the indication of climate change in Penang Island over the period of 2003-2018 by utilising sound application procedures of proven analysis methods. Two deterministic interpolation methods are used to produce new estimation points based on the precipitation data to enrich the monitoring network of rainfall stations in Penang Island. Monthly and monthly-average precipitation maps for Penang Island are produced by using inverse distance weighting interpolation method. Results reveal that seven out of twelve months of a year show increasing precipitation trends over the period of study and March is the only month that shows a decreasing trend in precipitation. Monthly-average precipitation in Penang Island also displays a gradual trend of precipitation increase over the period of study, further conforming the finding of monthly precipitation increase over the period of study. The finding of this study provides insight for local agriculturists and ministry to make better decision in response to climate change in Penang.

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