A MEDICAL PRICE PREDICTION SYSTEM

The health care costs constitute a significant fraction of the U.S. economy. Nearly 20% of the Gross Domestic Product (GDP) is spent on health care. The health spending in the US is the highest among all developed nations in absolute numbers as well as a percentage of the economy. The U.S. government bears a large portion of seniors’ health expenditure through its Medicare program. The growing health related expenses combined with the fact that the baby-boomer generation is retiring, and hence they will be eligible for Medicare, puts a great burden on the U.S. exchequer. Therefore, it is essential to contain health related payments through all means possible. In this work, we will develop a medical price prediction system using machine learning algorithms which will aid in steering patients to cost effective providers and thereby curb health spending. The policymakers can also use the tool to better understand which providers are relatively expensive and take punitive actions if necessary. The prediction of the medical price will be done using implementing Random Forest Regression algorithm in machine learning. Additionally, we plan to include the experiments on the same data with other machine learning models such as Gradient Boosted Trees and Linear Regression and compare results. The findings from these experiments will also be included

The productivity of 4th Generation KUB-2 Chicken

KUB-2 line of chicken has improved local chicken selected from the KUB-1 chicken line. KUB-2 was selected for more egg production and yellow shank. KUB-1 chicken has 64% various of black feather color, which sometimes tends to have unpreferred dark carcass. Yellow shank color has a positive correlation with the skin color of carcass. As many as 517 pullets of KUB-2 at 4th generation were divided into two groups of 194 pullets of KUB-2kk (yellow shank) and 323 pullets of KUB-2nk non-yellow shank). The chickens were raised intensively in the individual cages for the 24 weeks observation. Variables measured were age at first egg (AFE) bodyweight at first egg (BWFE), egg weight at first egg (EWFE), average egg weight (AEW), average egg production (AEP) during 24 weeks, feed conversion ratio (FCR) of 25-43 weeks of age, and mortality. The result showed that there was no statistically significant different (p>0.05) between KUB-2nk and KUB-2kk respectively for AFE of 156.2 d and 158.1 d, for BWFE of 1788 g and 1808 g, for EWFE of 31.32 g and 31.34 g, for AEP24 of 103.3 eggs or 61.5% and 101.9 eggs or 60.7%, and for FCR25-43 of 3.53 and 3.54. AEW increased with increasing age of hen, the mortality of the whole population was 0.98%

Non-Tax State Revenue (PNBP) In Research Institute For Inland Fisheries And Extension (BRPPUPP)

This study aims to analyze Non-Tax State Revenue (PNBP) at Research Institute For Inland Fisheries And Extension (BRPPUPP) Tax in terms of recording accuracy, management compliance, and effectiveness. then analyzed using descriptive qualitative analysis method. The results of this study indicate that the percentage of PNBP for Research Institute For Inland Fisheries And Extension in 2019 the realization of PNBP receipts does not reach the target which shows that the performance of achieving the target is 89.5%, while the amount of revenue in 2020 illustrates the effectiveness of the PNBP policy exceeding the target of 198.9 %. Keywords: PNBP, BRPPUPP   Abstrak Penelitian ini bertujuan untuk menganalisa Penerimaan Negara Bukan Pajak (PNBP) di Balai Riset Perikanan Perairan Umum dan Penyuluhan Perikanan (BRPPUPP) Pajak yang ditinjau dari sisi keakuratan pencatatan, kepatuhan pengelolaan, serta tingkat keefektifan. kemudian dianalisis dengan menggunakan Metode analisis deskriptif kualitatif. Hasil penelitian ini menjukan bahwa persentase PNBP untuk di Balai Riset Perikanan Perairan Umum dan Penyuluhan Perikanan di tahun 2019 realisasi penerimaan PNBP tidak mencapai target yang menunjukan bahwa kinerja pencapaian target sebesar  89.5%, Sedangkan jumlah penerimaan pada tahun 2020 menggambarkan keefektifan kebijakan PNBP  melebihi target sebesar 198.9%. Kata kunci: PNBP,BRPPUPP &nbsp