PENINGKATAN KETERAMPILAN MENULIS TEKS EKSPOSISI MELALUI PENGGUNAAN STRATEGI PEMBELAJARAN THINK TALK WRITE DAN MEDIA AUDIO VISUAL ( Penelitian Tindakan Kelas pada Siswa Kelas X IPS 2 SMA N 1 Surakarta Tahun Pelajaran 2017/2018)

Yustina Dwinuryati.2017. Peningkatan Keterampilan menulis Teks Eksposisi melalui Penggunaan Strategi Pembelajaran Think, Talk, Write dan Media Audio Visual (Penelitian Tindakan Kelas pada Siswa Kelas X IPS 2 SMA N 1 Surakarta Tahun Pelajaran 2017/2018). Tesis. Pembimbing: Prof. Dr. Andayani, M.Pd. Kopembimbing: Prof. Dr. Retno Winarni, M.Pd. Program Studi Magister Pendidikan Bahasa Indonesia, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sebelas Maret Surakarta. ABSTRAK Keterampilan menulis merupakan keterampilan berbahasa tingkat tinggi dan harus diajarkan kepada siswa di Sekolah Menengah Atas. Salah satu keterampilan menulis yang harus diajarkan kepada siswa kelas X sesuai Kurikulum 2013 Revisi adalah menulis teks eksposisi. Pembelajaran keterampilan menulis teks eksposisi di kelas X IPS2 SMA N 1 Surakarta mengalami permasalahan baik dari sisi motivasi belajar maupun keterampilan menulis siswa. Penelitian ini bertujuan untuk meningkatkan: (1) motivasi belajar menulis teks eksposisi siswa kelas X IPS 2 SMA Negeri 1 Surakarta dengan strategi pembelajaran think talk write dan penggunaan media audio visual dan (2) keterampilan menulis teks eksposisi siswa kelas X IPS 2 SMA Negeri 1 Surakarta dengan strategi pembelajaran think talk write dan media audio visual. Strategi penelitian berupa Penelitian Tindakan Kelas. Data penelitian bersumber dari proses pembelajaran, informan, hasil tes menulis teks eksposisi, dan dokumen. Teknik pengumpulan data dengan pengamatan, kajian dokumen, wawancara, dan tes. Uji validitas data menggunakan teknik triangulasi sumber data dan triangulasi metode. Teknik analisis data menggunakan teknik deskriptif komparatif dan analisis kritis. Hasil penelitian menunjukkan bahwa penerapan strategi pembelajaran think, talk, write dan penggunaan media audio visual pada siswa kelas X IPS2 SMA N I Surakarta dapat meningkatkan motivasi belajar dan keterampilan menulis teks eksposisi dari siklus 1 ke siklus 2. Hal itu dibuktikan adanya perubahan dan peningkatan motivasi belajar dan keterampilan menulis teks eksposisi siswa: (1) motivasi siswa meningkat dari siklus 1 sebesar 68% menjadi 82% pada siklus 2 dan (2) keterampilan menulis teks eksposisi meningkat dari siklus 1 sebesar 76% meningkat menjadi 88% pada siklus 2. Kata kunci: teks eksposisi, motivasi, strategi think talk write, audio visua

Results of training the same architecture on data seriated via different distance metrics, as well as using unsorted data (i.e. individuals within each population are arranged in the arbitrary order produced by the simulator), for the ghost-introgression problem (top row, panels A and B) and the <i>Drosophila</i> demographic model (bottom row, panels C and D).

These plots show the values of training (A and C) and validation (B and D) loss over the course of training. Validation loss is usually lower than training in the case of Drosophila because label smoothing was applied to the training data for the purposes of regularization, but not to the validation data. (PDF)</p

Diagram of the ghost introgression demographic model from [40], in which an unsampled archaic population splits off from the main population, before a pulse introgression event introduces alleles from this population into a sampled “Target” population, not an unsampled “Reference” population.

Diagram of the ghost introgression demographic model from [40], in which an unsampled archaic population splits off from the main population, before a pulse introgression event introduces alleles from this population into a sampled “Target” population, not an unsampled “Reference” population.</p

Calibration curves showing the impact of Platt scaling on the accuracy of the introgression probability estimates produced by IntroUNET, calculated on the validation set from the <i>Drosophila</i> simulations (Methods).

A) The fraction of alleles falling within a given bin of IntroUNET’s predicted probability of introgression that were in fact truly introgressed, prior to Platt recalibration. B) Same as (A), after recalibration. (PDF)</p

Parameters of the simple simulated test case.

We begin with a single population of size N which is allowed to “burn in” for 20N generations so that the populations reach, or at least approach, equilibrium. Then, a split occurs tS generations ago. Next, after some amount of time of complete isolation, which follows the described uniform distribution, a pulse migration event occurs with individuals migrating with a probability also drawn from a uniform distribution. This migration event can occur in either direction or in both directions, and both unidirectional and bidirectional introgression is examined the Results. Note that in the case of bidirectional migration a separate rate is drawn for both directions, and the maximum value of this rate is one half that for unidirectional migration. Migration rates specify backward probabilities (i.e. the expected fraction of the recipient population that migrates from the source population during the introgression event).</p

Bootstrap parameter estimates for the <i>D. simulans</i> and <i>D. sechellia</i> joint demographic model obtained via ∂a∂i.

The parameters of the model are the ancestral population size (Nref), the final population sizes of D. sechellia and D. simulans (N0−sech and N0−sim), the initial population sizes (Nsech and Nsim), the population split time (ts), and the backwards migration rates (msim → sech and msech → sim). Note that parameter estimates are shown for each bootstrap replicate for which our optimization procedure succeeded (Methods), but only those with log-likelihood scores greater than −1750 were used to simulate training data. (PDF)</p

IntroUNET’s accuracy when the split and migration times may be misspecified.

IntroUNET was trained on 25 different combinations of the population split time and the upper bound of the range of possible introgression times (with the lower bound always set to zero). These simulations were performed in the same manner as described for the simple bidirectional model in the Methods, with the exception of these two parameters. Each heatmap in this grid shows the accuracy of one version of IntroUNET on each of the 25 test sets, and the parameter combination used to train that network is marked by a circle. For example, if the true split time is 2N generations ago and the true split time is 0.1 times the split time, one can observe the impact of misspecification on accuracy by comparing the top-left value in the top-left heatmap (i.e. no misspecification in this case) to the top-left value of all other heatmaps in the figure, which experience varying degrees of misspecification. (PDF)</p

Example inputs and outputs (both true and inferred) for each of the three problems we used to assess IntroUNET’s effectiveness.

(A) A simulated example of the simple test scenario of a two-population split followed by recent single-pulse introgression event (bidirectional, in this case). The first column shows the population genetic alignments for this example, with the two panels corresponding to the two input channels (population 1 and population 2). The second shows the true histories of introgression for this example (again, with white pixels representing introgressed alleles); note that both population 1 and population 2 have introgressed alleles. The third and fourth columns show IntroUNET’s inference on this simulation, with the former showing the most probable class (i.e. introgression or no introgression) for each individual at each polymorphism, and the latter showing the inferred probability of introgression (i.e. the raw softmax output for the introgression class). The color bar for these plots is shown in panel (A), and the scaling is the same for the panels below as well. (B) A simulated example of the archaic ghost introgression scenario. The four columns are the same as in panel (A), but here we are examining a recipient population and a reference population, with the goal of identifying introgression only in the former. Thus, our output has only one population/channel. (C) A simulated example of our Drosophila introgression scenario. The four columns are the same as in (A) and (B), and here we are concerned with identifying introgression from D. simulans to D. sechellia, so again our output has only one channel (i.e. introgressed alleles in D. sechellia).</p

Ten example segmentations on simulations from our archaic introgression scenario: 5 from IntroUNET (left) and 5 from ArchIE (right).

For each example we show the true and inferred introgressed alleles in the recipient population. For each method, both examples with and without introgression are shown. (PDF)</p

Confusion matrix showing performance of a classifier that detects genomic windows that have experienced introgression, trained and evaluated on data simulated under the <i>D. simulans</i>-<i>D. sechellia</i> scenario as described in the Methods.

Confusion matrix showing performance of a classifier that detects genomic windows that have experienced introgression, trained and evaluated on data simulated under the D. simulans-D. sechellia scenario as described in the Methods.</p