SISTEM REPLANTING KELAPA SAWIT, OPPORTUNITY PENDAPATAN KELAPA SAWIT DAN TINGKAT KESEJAHTERAAN PETANI PADA MASA REPLANTING DI PERKEBUNAN KELAPA SAWIT RAKYAT KABUPATEN LAMPUNG TENGAH

This study aims were to determine the replanting model used by oil palm farmers, to calculate the replanting cost, to know the opportunity lost income, and to analyze the level of welfare of farmers during replanting in Central Lampung Regency. The sampling technique was a survey. The study was conducted in three districts, Anak Tuha District, Bangun Rejo District, and Sendang Agung District. The respondents were 31 oil palm farmers consisting of 6 respondents from Anak Tuha District, 21 respondents from Bangun Rejo District, and 4 respondents from Sendang Agung District. The data collection was carried out in March-May 2019. The results showed tha the replanting model used by oil palm farmers in Central Lampung Regency was the intercropping model with food crops and the underplanting model. The costs of replanting during the first 3 years when the palm trees are not productive yet were IDR45,481,990 per hectare on the intercropping model and IDR 48,146,117 per hectare on the underplanting model. The average value of oil palm opportunity lost income the intercropping model with food crops was IDR7,672,043 per hectare. The average household income of oil palm farmers was IDR19,489,145 per year. The level of welfare of farmers during replanting in Central Lampung Regency in general is in a fairly decent condition. Key words: cost, income, oil palm, opportunity, replanting, welfare

Estimating Human Mass Gathering on a Particular Time and Space Estimation by using Machine Learning

With the expanding populace, evaluating swarm thickness is a typical issue for swarm observation in Computer Vision. This issue stays a difficult assignment because of various varieties in scale issues created by various blocked uproars, changing shapes, and point of view variety. To handles these difficulties and to give a decent condition of precision we, in this way, center to gather a tremendous measure of datasets with shifting thickness levels and manufacture an Allied-CNN model. The assortment of the datasets is done from different sources like YouTube and some genuine recordings. The Allied-CNN model is worked in python and prepared on a named dataset of thousand item pictures from different points of view, for deciding thickness levels (as low thickness, medium thickness, and high thickness). Preparing results for thickness estimation show the preparation set precision arrives at 94.8%, the greatest approval exactness of just 88% is accomplished. Along these lines, this model aids in ordering a picture as low thickness, medium thickness, and high thickness. Estimations on this group datasets show that the proposed Allied-CNN performs modest outcomes contrasted with the cutting-edge strategies.