HUBUNGAN STATUS SOSIAL EKONOMI ORANG TUA DAN PEMANFAATAN MEDIA BELAJAR DENGAN PRESTASI BELAJAR PADA SISWA KELAS XI SMA BATIK 2 SURAKARTA TAHUN AJARAN 2010/2011

Tujuan penelitian ini adalah untuk mengetahui ada tidaknya hubungan yang signifikan antara : (1) Status Sosial Ekonomi Orang Tua dengan Prestasi Belajar, (2) Pemanfaatan Media Belajar dengan Prestasi Belajar, (3) Status Sosial Ekonomi Orang Tua dan Pemanfaatan Media Belajar dengan Prestasi Belajar. Penelitian ini mengambil lokasi di kelas XI SMA Batik 2 Surakarta. Sesuai dengan masalah dan tujuan penelitian, maka penelitian ini menggunakan metode diskriptif korelasional. Populasinya adalah siswa kelas XI SMA Batik 2 Surakarta 2010/2011, sebanyak 230 siswa. Sampel yang digunakan sebanyak 25% dari keseluruhan populasi yaitu sebanyak 60 siswa yang terbagi atas 7 kelas. Teknik sampling yang digunakan adalah Simple Random Sampling. Teknik pengumpulan data menggunakan teknik tes dan angket sebagai teknik pokok, teknik dokumentasi dan wawancara sebagai metode bantu. Teknik analisis data yang dipakai menggunakan analisis statistik dengan teknik regresi ganda dengan bantuan komputer seri program statistik ( SPS-2000 ) edisi Sutrisno Hadi dan Yuni Pamardiningsih. Berdasarkan hasil penelitian dapat disimpulkan bahwa : (1) Ada hubungan yang sangat signifikan antara status sosial ekonomi orang tua dengan prestasi belajar, dapat dilihat dari hasil analisis data yang menunjukkan rx1y = 0,555 dan p = 0,000 dimana p < 0,01 dengan Sumbangan Efektif (SE) sebesar 30,763% dan Sumbangan Relatif (SR) = 99,288% . Dengan demikian hipotesis yang berbunyi “Ada hubungan yang sangat signifikan antara status sosial ekonomi orang tua dengan prestasi belajar” dapat diterima. (2) Ada hubungan yang cukup signifikan antarapemanfaatan media belajar dengan prestasi belajar dapat dilihat dari hasil analisis data yang menunjukkan rx2y = 0,281 dan p = 0,028 dimana p < 0,05 dengan Sumbangan Efektif (SE) sebesar 0,221% dan Sumbangan Relatif (SR) = 0,712%. Dengan demikian hipotesis yang berbunyi “Ada hubungan yang cukup signifikan antara pemanfaatan media belajar dengan prestasi belajar” dapat diterima. (3) Ada hubungan yang sangat signifikan antara status sosial ekonomi orang tua danpemanfaatan media belajar dengan prestasi dengan rx1x2y = 0,557 dan p = 0,000 dimana p < 0,01. Jadi hipotesis yang berbunyi “Ada hubungan yang sangat signifikan antara status sosial ekonomi orang tua dan pemanfaatan media belajar dengan prestasi belajar” dapat diterima

(English, all; Version 2.) Top 60 individual contributions of 1-grams to the JSD between the 1950s and the 1980s.

<p>Each contribution is given as a percentage of the total JSD (see horizontal axis label) between the two given decades. All contributions are positive; bars to the left of center represent words that were more common in the earlier decade, whereas bars to the right represent words that became more common in the later decade.</p

Time series of technical terms from Version 2: (a) English all, (b) English fiction.

<p>In the unfiltered data set, these technical terms appear frequently and increase in usage though the 1980s. In fiction, technical terms show up far less frequently and remain relatively stable in usage with the notable exception of “computer,” which has been gradually gaining popularity since the 1960s.</p

JSD between 1880 and each displayed year for given data set, corresponding to dashed lines from Fig 4.

<p>Contributions are counted for all words appearing above a 10⁻⁵ threshold in a given year; for the dashed curves, the threshold is 10⁻⁴. Typical behavior in each case consists of a relatively large jump between one year and the next with a more gradual rise afterward (in both directions). Exceptions include wartime, particularly the two World Wars, during which the divergence is greater than usual; however, after the conclusion of these periods, the cumulative divergence settles back to the previous trend. Initial spikiness in (D) is likely due to low volume.</p

(Left) <i>k</i>_max,in and (Right) <i>k</i>_max,in for Twitter reply networks.

<p>Each data point represents the observed maximum in- and out-degree, averaged over 100 simulated subsampling experiments. The dashed line extrapolates the predicted number of edges for greater proportions of sampled data.</p

Consecutive year (between each year and the following year) base-10 logarithms of JSD, corresponding to off-diagonals in Fig 4.

<p>For the solid curves, contributions are counted for all words appearing above a 10⁻⁵ threshold in a given year; for the dashed curves, the threshold is 10⁻⁴. Divergences between consecutive years typically decline through the mid-19th century, remain relatively steady until the mid-20th century, then continue to decline gradually over time.</p

Time series for “he” and “she” for Version 2.

<p>The unfiltered normalized frequencies are given by the solid curve. Normalized frequencies in fiction are given by the dashed curve. These personal pronouns are more common in fiction. The pronoun “she” gains popularity through the 1990s in both data sets, with a more pronounced growth in fiction.</p

Charitable gifts of candidates for the United States President from their publicly released federal tax returns.

<p>Again due to finite size bias of maximum likelihood methods, we adopted linear regression for fitting the distribution scaling parameter . The included fit is for President Romney's gifts during the year of 2010. We include the fitted <i>γ</i>'s for each president and the range of their fit in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098876#pone.0098876.s002" target="_blank">File S1</a> as Table S3, and we show comparisons to other distributions in Table S4.</p

Fundraising pyramids customized to an institutions .

<p>Using a power-law model of fundraising, low institutions should plan for and request much higher top level gifts than high institutions.</p

Predicted edge weight and degree distributions for Twitter reply networks.

<p>(Top) The predicted edge weight distribution. (Bottom, left) Predicted <i>Pr</i>(<i>k</i>_in) and (Bottom, right) <i>Pr</i>(<i>k</i>_out) for Twitter reply networks.</p