The population density, growth rate and production of bream, Abramis brama, in Tjeukemeer, the Netherlands

Abstract. Herawati VE, Nailulmuna Z, Rismaningsih N, Hutabarat J, Pinandoyo, Elfitasari T, Riyadi PH, Radjasa OK. 2020. Growth performance and nutritional quality enrichment of Phronima pacifica by Chlorella vulgaris and Chaetoceros calcitrans as natural feed. Biodiversitas 21: 4253-4259. Phronima pacifica as a natural feed has the potential to replace Artemia sp. because of its high nutritional content. The purpose of this study was to determine the effects of different types of feed for P. pacifica during its culture on its population density, growth rate, and nutritional content because of its potential use as a natural feed for fish. The test animals in this study consisted of P. pacifica at a stocking density of 3 ind/L, which were cultured for 18 d. Then, Chlorella vulgaris and Chaetoceros calcitrans were introduced as enrichment feed for P. pacifica. This study used a completely randomized design with three treatments and three replications. The treatments were as follows: A (100% Chlorella vulgaris), B (100% Chaetoceros calcitrans), and C (50% Chlorella vulgaris and 50% Chaetoceros calcitrans). The results showed that treatment increased the population density of P. pacifica by up to 54.67 ± 0.0038 ind/L by the 12th day. Based on the results of proximate analysis for amino acid and fatty acid profiles, the highest values for proteins and fats were in P. pacifica enriched with Chlorella vulgaris (A) and consisted of 45.45% protein, 7.57% fat, 5.95% eicosapentaenoic acid, and 39.23% lysine. Based on an ANOVA, feeding of P. pacifica with Chlorella vulgaris and Chaetoceros calcitrans had a significant effect on population density, relative growth rate, biomass production, and nutrient value of proteins and fats of P. pacifica (P < 0.05). The best results, including population density, growth rate, weight of biomass, and nutrient content of P. pacifica, occurred after feeding with Chlorella vulgaris.

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Prediction Vulnerability Level of Dengue Fever Using KNN and Random Forest

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1926 ◽

2020 ◽

Vol 4 (3) ◽

pp. 531-536 ◽

Cited By ~ 1

Author(s):

Abduh Salam ◽

Sri Suryani Prasetiyowati ◽

Yuliant Sibaroni

Keyword(s):

Growth Rate ◽

Wind Speed ◽

Random Forest ◽

Population Density ◽

Dengue Virus ◽

Dengue Fever ◽

Population Mobility ◽

Density Growth ◽

The Government ◽

Over Time

Indonesia is a country that is prone to Dengue Fever, this happens because Indonesia is a country with a tropical climate. More than 50 years after Indonesia contracted the dengue virus, dengue fever cases have not been resolved, currently the cases that occur are greatly increased over time this happens because of factors that cause dengue fever. By considering this serious problem, the authors created a system that can predict the vulnerability level in Bandung and looks for the factors that most influence from all factors of Dengue Fever using the KNN Algorithm and Random Forest. The results of the system show the results of the best model is KNN algorithm with RMSE 29,26, and from the model shows the most influencing factors are population density, growth rate population mobility, rainfall, wind speed. by utilizing the results of the study, the government can adjust actions to each level of sub-district vulnerability and pay more attention to the factors that most influence dengue fever according to the results of the study.

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