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All Journal Jurnal Matematika Sains dan Teknologi Jurnal Pendidikan Terbuka Dan Jarak Jauh
Aprijani, Dwi Astuti
LPPM Universitas Terbuka
Agriculture, Biological Sciences & Forestry Education Mathematics Other

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PEMILIHAN TEKNOLOGI AUDIO YANG TEPAT SEBAGAI MEDIA PEMBELAJARAN UNTUK MAHASISWA UNIVERSITAS TERBUKA Kurniati, Sri; Sinar, Tengku Eduard Azwar; Aprijani, Dwi Astuti
Jurnal Pendidikan Terbuka Dan Jarak Jauh Vol 10 No 1 (2009)
Publisher : LPPM Universitas Terbuka

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The kind of audio technology makes it possible to choose the technology that can make students easy and convenient to access the audio program. Audio streaming technology is technology used to put the audio programs on the website. Available in various formats audio programs that can be used for audio streaming technology. The aim of the research is to make the selection of audio technologies available for use in e-Learning system at the Open University (UT). Data taken with the questionnaire and download audio programs in various formats on the UT website. Data analysis to see the frequency calculations are presented descriptively.The results showed that the 4 (four) format used on audio technology, which is MP3 (MPEG layer 3), WMA (Windows Media Audio), OGG Vorbis, and AAC (Advanced Audio Codec) can be concluded that: most audio format easy to use are MP3, MP3 audio format is the fastest format can be downloaded by students, as well as audio technology that produces the best sound quality is MP3.
APLIKASI JARINGAN SYARAF TIRUAN UNTUK MENGENALI TULISAN TANGAN HURUF A, B, C, DAN D PADA JAWABAN SOAL PILIHAN GANDA (Studi Eksplorasi Pengembangan Pengolahan Lembar Jawaban Ujian Soal Pilihan Ganda di Universitas Terbuka) Aprijani, Dwi Astuti; Sufandi, Unggul Utan
Jurnal Matematika Sains dan Teknologi Vol 12 No 1 (2011)
Publisher : LPPM Universitas Terbuka

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Artificial Neural Network (ANN) can be applied to recognice pattern, particularly at the stage of data classification. This study used a multilayer perceptron backpropagation ANN, an unsupervised learning algorithm, to recognize the pattern of uppercase handwriting on the answer sheet of multiple-choice exams. The application of this network involves mapping a set of input against a reference set of outputs. In this research, ANN was trained using 8000 handwritten uppercase characters (A, B, C, and D) consisting of 6000 training data characters (1500 characters for each letter) and 2000 testing data characters (500 characters for each letter). The result showed that for the most optimal performance, the architecture and network parameters were 10 neurons in hidden layer, learning rate of 0.1 and 3000 iteration times. The accuracies of the result using the optimal network architecture and parameters were 90.28% for training data and 87.35% for testing data.
Co-Authors Sri Kurniati, Sri T. Eduard Azwar Sinar Unggul Utan Sufandi