Insect pathogens that are often used to control insect pests are fungi Beauveria bassiana and Metarhizium anisopliae. One of the agar media, which is often used in the entomopathogenic function of propagation in the laboratory, is PDA (potato dextrose agar). The price of instant PDA is quite high, starting from Rp. 750,000 per 250 g doing the research costs quite large. Cheap and easily obtained abundant natural resources can be used as an alternative medium for microorganism growth to reduce the overall costs to be incurred in research. Carbohydrate sources used in this study were potatoes, cassava, sweet potatoes, corn, and rice, which were made into the agar media. Observation parameters were viability rates, colony growth rates, and sporulation of B. bassiana and M. anisopliae fungi isolates. The experiments are used in a completely randomized design (CRD) with two treatment factors. The data obtained were processed with variance and continued with the DNMRT test at 5% significance level. The results showed that cassava agar caused the highest germination at 24 hours. The highest diameter was fungi grown on potato dextrose agar. The highest sporulation was fungi grown on corn agar.

As a metropolitan city with high mobility, public transportation plays an important role in facilitating economic, business and government activities in DKI Jakarta. DKI Jakarta provincial government launched the JakLingko program to create an integrated, convenient, efficient, and affordable public transportation system. Knowledge of public opinion can help improve the service quality of the JakLingko program. The use of social media is becoming very popular nowadays. Through social media, anyone can easily express their opinion about an issue. It is used to obtain objective and latest public opinion. Sentiment analysis is a method that can be used to analyze public opinion. Through sentiment analysis whose data was collected from Twitter, it can be seen how the public opinion toward JakLingko program. In this study, public sentiment will be classified into positive sentiment or negative sentiment. As for the classification, the Support Vector Machine (SVM) algorithm is used.