PENGARUH MEDIA PEMBELAJARAN KAMUS ONLINE AL-MA’ĀNY DALAM MENINGKATKAN MAHĀRAH AL-QIRĀ’AH PADA SISWA KELAS X MAN 2 SLEMAN TAHUN AJARAN 2017/2018 (Studi Eksperimen)

This study aims to determine how the application of Al-Ma'any online dictionary media and whether there is a difference between learning that uses Al-Ma'any online dictionary media and those who do not use Al-Ma'any online dictionary media for maharah Al-Qirā'ah in class X MAN 2 Sleman. This study uses a quantitative approach while the type of research is Experimental Research. The sample in this study was 60 students, consisting of class X MIA I which was used as an experimental class with a total of 30 students and class X MIA II which was used as a control class with a total of 30 students. For data collection techniques in this study using conservation, test and documentation. Analysis of the data using T test analysis with analysis requirements Normality Test using Kolmogrof-Smirnov and homogeneity. The results of this study indicate that learning Arabic by using Al-Ma'āny online dictionary learning media can improve the Mahārah of Al-Qirā'ah in the learning of Arabic students. This is demonstrated by using SPSS 19. The output results show that in the Independent Sample Test t-test the posttest value of the experimental class students found that a significant value of 0,000 <0.05 and from the analysis of the T test the value of T counted 5.148> T table 2.045, then H0 is rejected, meaning that there is a difference in the use of Al-Ma'āny's online dictionary learning media for the learning of the Mahārah of Al-Qirā'ah Keywords: Learning Media, Al-Ma’any Dictionary. mahārah Al-Qirā’ah

Online dictionary learning for car recognition using sparse coding and LARS

<p>The bag of feature method coupled with online dictionary learning is the basis of our car make and model recognition algorithm. By using a sparse coding computing technique named LARS (Least Angle Regression) we learn a dictionary of codewords over a dataset of Square Mapped Gradient feature vectors obtained from a densely sampled narrow patch of the front part of vehicles. We then apply SVMs (Support Vector Machines) and KMeans supervised classification to obtain some promising results.</p>