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Bio plastics are polymers prepared from renewable materials. In this study, maize-derived cornstarch and milled corn husk were used as the base material and filler, respectively. Corn husk powder with two-grain sizes of 150 mesh and 200 mesh, respectively, were used. Chitosan was used at concentrations of 0.02 %, 0.04 %, 0.06 %, 0.08 %, and 0.1 % by weight at a constant ratio of 1:1 to cornhusk powder and maize for improving the mechanical properties of bio plastics. The mixture was diluted using a solution containing 2.5 mL of acetic acid (25 %), 1.75 mL of sorbitol, and 70 mL distilled water. Optimum mechanical properties were observed using a cornhusk grain size of 150 meshes with 0.04% of chitosan by weight. This sample exhibited a tensile strength of 11.7164 MPa, elongation of 10.05 %, a Young’s modulus of 1.1668 MPa, and tear strength of 763.86 mN. A biodegradability of 70–100 % was achieved in 21 days with the evidence of fungal growth after 14 days. In addition, the sample was able to withstand a temperature of 140 °C for 1 h.

Harmful Algal Bloom (HAB) is one of nature's responses to nutrient enrichment in aquatic systems and increasingly occurs in coastal waters, such as in Lampung Bay and Jakarta Bay, Indonesia. HABs present environmental and fisheries management challenges due to their unpredictability, spatial coverage, and detrimental health effects to coastal organisms, including humans. Here, we propose an automated algae species identification system assisted and validated by expert judgment. The system uses ontology as guidance to determine the species of algae and certainty factors to indicate the level of confidence of the experts when providing a statement or judgment for a particular object or event under consideration. We tested the system to identify 60 sample data using 51 predetermined algal characteristics.The tests were narrowed down to the 20 most common HAB-causing algae types found in the study sites and compared with identification by the experts. The results showed that the system has successfully identified the test data with an accuracy of 73.33%. The system also has a high agreement (above 79.75%) with the identification performed by the experts on six algae species. Further improvement on the system's accuracy could facilitate its use as an alternative tool in rapid algal identification or part of an early warning system for HABs.INDEX TERMS Harmful algal bloom, algae identification, ontology, certainty factor, expert system.

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