Analisis Proses Seleksi Tenaga Kerja Di De Boliva Café Surabaya Town Square

Penelitian ini dilakukan di De Boliva Café Surabaya Town Square. Tujuan dalam penelitian ini adalah untuk mengetahui proses seleksi tenaga kerja. Teknik analisis yang digunakan dalam penelitian ini adalah analisis kualitatif deksriptif. Hasil analisis menunjukkan bahwa proses seleksi tenaga kerja di De Boliva adalah seleksi curriculum vitae (CV) beserta surat lamaran, tes tulis, wawancara video, dan wawancara akhir

Comparisons of CDC, M_f, M_l, and GFT peak ILI values.

<p>ILI: Influenza-like illness, CDC: Centers for Disease Control and Prevention.</p><p>M_f: Full model, M_l: Lasso model, GFT: Google Flu Trends.</p><p>*Referent values are CDC ILI values for the corresponding week of the estimated ILI peak for M_f, M_l, and GFT.</p

Time series plot of CDC ILI data versus estimated ILI data.

<p>(A) Wikipedia Full Model (Mf) accurately estimated 3 out of 6 ILI activity peaks and had a mean absolute difference of 0.27% compared to CDC ILI data. (B) Wikipedia Lasso Model (Ml) accurately estimated 2 out of 6 ILI activity peaks and had a mean absolute difference of 0.29% compared to CDC ILI data,. (C) Google Flue Trends (GFT) model accurately estimated 2 of 6 ILI activity peaks and had a mean absolute difference of 0.42% compared to CDC ILI data.</p

Rates of OxyContin print news media mentions in the United States, 1999–2006, by state.

<p>Print news media articles showed a progression from localized reporting, to national attention and eventual regression between 1999 and 2006, based on an analysis where articles were geotagged based on the US state which the article was describing or was published in, see text for methodology. OxyContin problems were first described in 2001 with particular focus on the Appalachian region and New England. Between 2001 and 2003, there was a sharp increase in articles about OxyContin published about Florida, Pennsylvania and New Jersey. The highest state-level rates of OxyContin news articles were in 2001. After 2003, reporting became more homogenous with substantial reporting throughout the country. Media attention on OxyContin started to subside soon afterwards, with a return to localized reporting in 2005. By 2006, New Jersey had the highest rates, as articles about abuse- and tamper-resistant formulations in development become prominent; New Jersey has a large concentration of pharmaceutical manufacturers and many of these articles were from financial news sources describing attempts to develop formulations that would be less prone to tampering. The geo-temporal spread of news reports about OxyContin mirror the experiences of previous cycles of drug abuse panics in the United States.</p

Monthly rates of news volume mentioning prescription opioids, 1998–2006, United States.

<p>Monthly print news volume mentioning prescription opioids was driven by different factors over time. Government and industry actions and celebrity involvement tended to produce peaks in the time series. A general pattern was observed of regional reports of abuse problems followed by national coverage, for OxyContin. By comparison, fentanyl was reported on less frequently than OxyContin. Two peaks in fentanyl articles were due to issues not related to pharmaceutical fentanyl formulations (weaponized gas and illicitly manufactured powder), suggesting the need for categorization of articles based on content. Abbreviations: Apr, April; DC, District of Columbia; Feb, February; FDA, Food and Drug Administration; Jan, January; Jul, July; KY, Kentucky; MA, Massachusetts; Mar, March; NY, New York; Oct, October; OH, Ohio; US, United States; VA, Virginia.</p

Range of proportions of users by interest category and geography.

<p>Proportion of Facebook users across neighborhoods in NYC and metropolitans or micropolitans in USA with activity-related interests or interest in television.</p

Prevalence of television interests and obesity in New York City.

<p>Neighborhoods in NYC are color-coded based on the (a) population prevalence of obese and/or overweight people or (b) the proportion of the population with television-related interests. Neighborhoods in grey are in the middle 25% based on proportion of individuals. Proportion of obese or overweight is color-coded from red to green (more to less) (a), and proportion of television-related interests from red to green (more to less) (b). For the data from Facebook (b), the neighborhood with the minimum proportion of people with activity-related interests is demarcated by “+”, and neighborhood with maximum proportion by “*”.</p

Prevalence of activity-related interests and obesity in the USA.

<p>Squares for each metropolitan or micropolitan used in the study, color-coded by (a) population prevalence of obese and/or overweight people or (b) the proportion of the population with activity-related interests. Metropolitans in grey are in the middle 25% based on proportion of individuals. Proportion of obese or overweight is color-coded from red to green (more to less) (a), and proportion of activity-related interests from red to green (less to more) (b). For the data from Facebook (b), the place with the minimum proportion of people with activity-related interests is demarcated by “+”, and place with maximum proportion by “*”.</p

Scatter plot of distance between authors and citation for high resolution data.

<p>High resolution data is only available for Harvard affiliated authors. Harvard authors are in 4 major geographical locations: Longwood Medical Area, Massachusetts General Hospital (MGH) main campus, MGH Navy Yard campus, and McLean Hospital. Distances between authors are aggregated in discrete values because authors are not uniformly distributed but in one of those 4 locations. Maximum value (12 km) are for authors in MGH campuses and McLean Hospital.</p

Distance and mean citation in different author relationships.

<p>There are four author relationships within an article: first-last, first-middle, last-middle, middle-middle. While there is only one FL in any article, there are (n-2) FMs or LMs in an article with n authors, and (n-2)x(n- 3)/2 MMs. As you can see in FM/LM/MM graphs, the ALL graphs are dominated by > = 5 data because there are way more same relationship pairs as n increases. If there is an article with 100 authors/50 citations, for example, the citation 50 gets counted 98 times in FM/LM mean citation and ∼10000 times in MM. There is one safe case, though. If there are 4 authors, there is only 1 MM. If there are less than 4 authors, there is no MM. Therefore, graphs for FL and MM < = 4 are safe for interpretation. Other graphs should be interpreted carefully.</p