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Indonesia is the largest palm plantation that reaches 32 million tonnes palm oil production per year with 84 million tones Palm Oil Mill Effluent (POME) as liquid waste. POME contains many organic substances. The quality of POME for its utilization is generally measured in COD which has range 30000 -100.000 ppm. Microbial convertion for biogas especially for bio-H2 enrichment, the active sludge was pretreated physically to suppress methanogenesis microbes. H2 Biogas production was conducted at pH 5-6. Additional 10% phosphate buffer was done in the beginning only. The production of H2 biogas was influenced by hydrostatic pressure in closed batch system. Inoculumsmedium ratio also influenced the H2 biogas productivity, reached 0.7 ml/ml POME with more than 50% H2. Scaling up anaerobic in 2.5 L working volume bioreactor, H2 biogas productivity reached 0.86 ml/ml POME by 10% inoculums because of no hydrostatic pressure. In bio-reactor, H2-CO2 in H2 biogas was affected by the amount of active sludge. In the beginning of H2 biogas, H2 reached 80%. However, at subsequence process, fed batch, with retention time 2.5 day and 3 days H2 biogas production, the active sludge was accumulated and caused the decreasing H2, finally only 46% at the 3rd day. The consortium tended to produce more CO2 as the result of primary metabolite rather than H2. Raising inoculums to level 15% improved productivity only in the beginning but H2 content was getting less, only 59%. Additional feeding would cause more accumulation sludge and more decreasing H2 content to 31% on the 3rd day. Thus, the ratio of active sludge and substrate availability must be controlled to gain optimum H2. Limited substrate will cause the direction of bio-conversion more in CO2 rather than H2.

In recent years, biodiversity has emerged as a prominent and pressing topic due to the urgent need to address biodiversity loss and the recognition of its connections to climate change and sustainable development. Additionally, increased public awareness and the consideration of economic factors have further underscored the significance of biodiversity conservation. To investigate the sentiment of the Indonesian people towards biodiversity, we conducted a comprehensive data collection on Twitter, focusing on keywords we have set. We amassed a substantial dataset of 500,000 Indonesian tweets from January 2020 to March 2023. These tweets encompassed a wide range of discussions on biodiversity, including its subdomains such as food security, health, and environmental management. Three annotators labeled each tweet with a sentiment class (positive, negative, neutral), or label none for unrelated tweet. The final label was determined using the majority voting method. The tweets with the final label none and those with undecided sentiment class were considered invalid and excluded in the subsequent process. Before labeling, a team of 18 experts jointly developed a labeling guide. This document served as a reference in labeling. After going through a series of processes, including cleaning (removing duplications, irrelevant tweets, and tweets written other than in Indonesian) and preprocessing, we prepared a dataset containing 13,435 tweets. We measured the inter-annotator agreement level, made several models using different algorithms and the K-Fold cross-validation method, and evaluated the models. The Fleiss' Kappa value of the dataset was 0.62187 as the value of the inter-annotator agreement level, and the F1-score value with the best model using the pre-trained IndoBERT model was 0.7959. The Fleiss' Kappa and F1-score values suggest that the annotators have a substantial comprehension and agreement of how to label a tweet, thus ensuring consistency and reliability of our dataset, and the reusability of our dataset is quite suitable for further research on sentiment analysis on biodiversity, respectively. This dataset will benefit various research, including topic modeling, sentiment analysis, public opinion analysis on Twitter, etc., especially biodiversity-related policies., (© 2023 The Author(s).)