Effect of Organic and Inorganic Fertilizers on Yield and Quality of Synedrella nodiflora (Tropical Weed)

This study aimed to determine the effect of organic and inorganic fertilizers on the cultivation of tropical weed Synedrella nodiflora as forage. The study was conducted from May to July 2018. The treatments of the research were control (C), organic fertilizer (D) and organic fertilizer + urea (DU), with 3 replications that were arranged on experimental design with a completely randomized design, in a unidirectional pattern and continued with least significant different (LSD). Organic fertilizer dosage in this study was 5 tons/ha, while urea fertilizer was 350 kg/ha, with plant spacing was 45x60 cm. The observed parameters were plant height, forage production and chemical composition (5 weeks after planting). Plant height of C, D and DU were 41.59, 47,42, and 50.59 cm respectively. Forage production of dry matter after 5 weeks planting at C, D and DU were 1.69, 1.70 and 2.91ton/ha, with in vitro digestibility values ranging from 51.68 to 57.70% (IVDMD) and 51.71 to 61.98% (OMD) respectively. The chemical composition of native S. Nodiflora were 12.32% of dry matter (DM), 62.45% TDN count for cattle and 67.42% TDN count for sheep. Based on DM, The organic matter was 84.46%, crude protein 20,11%, crude fiber 13.26%, extract ether 7.77%, and nitrogen free extract 37.08%. The combination of organic fertilizer and urea increased the height and fresh and dry matter production S. nodiflora

Implementation of Machine Learning for Text Classification Using the Naive Bayes Algorithm in Academic Information Systems at Sebelas Maret University Indonesia

This study implements machine learning using the Naive Bayes algorithm to create a text classification in an engineering professional program information system. The methods used include text data collection, preprocessing, feature extraction, Naive Bayes model training, and evaluation using data testing. This study made a classification model to predict text categories with a test accuracy rate of 0.975 and a training accuracy of 0.967. This research contributes to the development of text classification in information systems and can be used as a basis for further study