Model Prediksi Kebangkrutan Fullmer H-score dan Springate: Mana yang Lebih Kuat?

Penelitian ini bertujuan untuk menganalisis metode yang berbeda antara metode Fulmer h-nilaidan metode Springate untuk prediksi kebangkrutan pada Perusahaan pertambangan. Variabelyang digunakan dalam penelitian ini adalah metode Fulmer h-nilai, metode Springate dan prediksikebangkrutan. Sampel dalam penelitian ini 38 Perusahaan pertambangan yang terdaftar di BursaEfek Indonesia sejak 2011 hingga 2014. Penelitian ini menggunakan analisis metode Fulmer hnilai,metode Springate dengan membantu microsoft excel dan paket statistik ilmu sosial (SPSS)16.0. Dari model Fulmer menemukan 47 Perusahaan pertambangan di kategori dalam potensikebangkrutan sampai 2011-2014 dan model Springate menemukan pada tahun 2011, ada tujuhbelas Perusahaan. delapan belas Perusahaan ditemukan pada tahun 2012, dua puluh tigaperusahaan yang ditemukan pada tahun 2013, dan dua puluh tiga Perusahaan yang ditemukanpada tahun 2014 Model Springate berada didalam posisi kebangkrutan

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.Comment: 13 page

Pengaruh Penerapan E-Government Terhadap Pelaksanaan Good Governance Di Dinas Komunikasi Informatika Dan Statistik Kabupaten Wajo

The objectives of this research is to find out the effect of controlling to the employee work professionalism at Statistic Center Agency in Pinrang Regency. The type of research used is the measurement of quantitative and objective statistics. The population in this research were 23 people and used as a sample. The file collection techniques are using observation, documentation and cousionary techniques against the response. The identity of respondents was seen from gender, age, religion, and level of education. The result of this research indicated that the influence of the controlling to the employee work professionalism at Statistic Center Agency in Pinrang Regency means that it has a positive and significant effect on employee work professionalism. The result of this research have been tested using a simple regression tool with a positive result of 1,948 and a significant level of 0,000 smaller than 0,05 with the R square value of 0,997 or 99,7 % which means the controlling contribution to employee work professionalism at Statistic Center Agency in Pinrang Regency, while 3%  is affected by other variables not examined in this research such as motivation, work experience, etc

Application of Machine Learning Using Decision Trees for Prognosis of Deep Brain Stimulation of Globus Pallidus Internus for Children With Dystonia

Background: While Deep Brain Stimulation (DBS) of the Globus pallidus internus is a well-established therapy for idiopathic/genetic dystonia, benefits for acquired dystonia are varied, ranging from modest improvement to deterioration. Predictive biomarkers to aid DBS prognosis for children are lacking, especially in acquired dystonias, such as dystonic Cerebral Palsy. We explored the potential role of machine learning techniques to identify parameters that could help predict DBS outcome. Methods: We conducted a retrospective study of 244 children attending King's College Hospital between September 2007 and June 2018 for neurophysiological tests as part of their assessment for possible DBS at Evelina London Children's Hospital. For the 133 individuals who underwent DBS and had 1-year outcome data available, we assessed the potential predictive value of six patient parameters: sex, etiology (including cerebral palsy), baseline severity (Burke-Fahn-Marsden Dystonia Rating Scale-motor score), cranial MRI and two neurophysiological tests, Central Motor Conduction Time (CMCT) and Somatosensory Evoked Potential (SEP). We applied machine learning analysis to determine the best combination of these features to aid DBS prognosis. We developed a classification algorithm based on Decision Trees (DTs) with k-fold cross validation for independent testing. We analyzed all possible combinations of the six features and focused on acquired dystonias. Results: Several trees resulted in better accuracy than the majority class classifier. However, the two features that consistently appeared in top 10 DTs were CMCT and baseline dystonia severity. A decision tree based on CMCT and baseline severity provided a range of sensitivity and specificity, depending on the threshold chosen for baseline dystonia severity. In situations where CMCT was not available, a DT using SEP alone provided better than the majority class classifier accuracy. Conclusion: The results suggest that neurophysiological parameters can help predict DBS outcomes, and DTs provide a data-driven, highly interpretable decision support tool that lends itself to being used in clinical practice to help predict potential benefit of DBS in dystonic children. Our results encourage the introduction of neurophysiological parameters in assessment pathways, and data collection to facilitate multi-center evaluation and validation of these potential predictive markers and of the illustrative decision support tools presented here

“It’s like heaven over there”: Medicine as discipline and the production of the carceral body

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Background Correctional systems in several U.S. states have entered into partnerships with Academic Medical Centers (AMCs) to provide healthcare for people who are incarcerated. This project was initiated to better understand medical trainee perspectives on training and providing healthcare services to prison populations at one AMC specializing in the care of incarcerated patients: The University of Texas Medical Branch at Galveston (UTMB). We set out to characterize the attitudes and perceptions of medical trainees from the start of their training until the final year of Internal Medicine residency. Our goal was to analyze medical trainee perspectives on caring for incarcerated patients and to determine what specialized education and training is needed, if any, for the provision of ethical and appropriate healthcare to incarcerated patients. Results We found that medical trainees grapple with being beneficiaries of a state and institutional power structure that exploits the neglected health of incarcerated patients for the benefit of medical education and research. The benefits include the training opportunities afforded by the advanced pathologies suffered by persons who are incarcerated, an institutional culture that generally allowed students more freedom to practice their skills on incarcerated patients as compared to free-world patients, and an easy compliance of incarcerated patients likely conditioned by their neglect. Most trainees failed to recognize the extreme power differential between provider and patient that facilitates such freedom. Conclusions Using a critical prison studies/Foucauldian theoretical framework, we identified how the provision/withholding of healthcare to and from persons who are incarcerated plays a major role in disciplining incarcerated bodies into becoming compliant medical patients and research subjects, complacent with and even grateful for delayed care, delivered sometimes below the standard best practices. Specialized vulnerable-population training is sorely needed for both medical trainees and attending physicians in order to not further contribute to this exploitation of incarcerated patients.The University of Kansas (KU) One University Open Access Author Fun

Pemodelan Pertumbuhan Ekonomi Provinsi Sulawesi Selatan dengan Menggunakan Regresi Data Panel

Economic growth is a process for change the economic condition a country or regional by continuously for the better condition as long as definite period. Economic growth in South Sulawesi for 2013-2016 have up and down because many factors have influence it. Like jobless, human capital index, regional revenue, expenditure, and total population. This research was conducted to determine the factors that influence economic growth in South Sulawesi by using data panel regression methods. Panel data regression is a regression by using panel data. Panel data is a statistics analysis method that combines between time series data and cross section data. The result indicates that the result if the regression analysis on the α = 5% show that the best panel data regression model is random effect model and human capital index variable have significant effect on economic growth with probability value about 0,0227. Meanwhile, jobless, regional revenue, expenditure, and total population no significant

The Cyclooxygenase-2 and Nuclear Factor-kappa B Expressions in Colorectal Polyps

Background: Cyclooxygenase-2 (COX-2) is the rate-limiting enzyme in prostaglandin synthesis, while nuclear factor kappa B (NF-kB) is a family of transcription factors. Both play an important role in tumorigenesis. In the present study, we examined NF-kB and COX-2 expressions pattern, and their association in neoplastic and non-neoplastic colorectal polyps (CP). Method: Formalin-fixed and paraffin embedded tissue blocks from 77 patients with CP were immunostained with anti-NF-kB (p 65) and anti-COX-2. Expressions of NF-kB, and COX-2 were detected immunohistochemically. The relationship between these expressions and the two types of CP, and other clinicopathological findings were evaluated Results: The expressions of NF-kB and COX-2 in patients with neoplastic and non-neoplastic CP were high. The results of this study indicated that generally in CP, NF-kB was associated with COX-2 and the association was also seen in neoplastic and non-neoplastic polyps. There was no significant difference of NF-kB and COX-2 expressions in terms of patient's age, sex, histologic type, and location of the CP. Neoplastic CPs were more common in the distal colon, female patients and older patients (> 60 years) compared with non-neoplastic CPs. Neoplastic CP were located more at the distal colon, more in female, and more in older (> 60 years) patients as compared with the non-neoplastic CP. Further studies are needed to elaborate the role of inflammation in sporadic colorectal carcinogenesis. Conclusion: The expressions of NF-kB and COX-2 in patients with CP were high, and strong correlated each other. There were no significant differences between expression of NF-kB and COX-2 in neoplastic and non-neoplastic polyps