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"Popularitas telepon pintar dan aplikasi seluler membuat unduhan dan pengguna aplikasi meningkat secara eksponensial. Pengguna dapat memberikan ulasan terkait dengan penggalaman menggunakan aplikasi, ulasan ini dapat berisi keluhan atau saran yang berharga untuk dikaji lebih lanjut. Namun jumlah ulasan yang sangat banyak menyulitkan untuk mencari dan memahami informasi yang terkandung pada teks ulasan. Untuk mengatasi permasalahan tersebut pada penelitian ini mengusulkan model yang dapat menggali informasi serta mengkategorikan konten dan sentimen ulasan dengan menggunakan teknik pembelajaran mesin. Algoritme SentiStrength, Support Vector Machine SVM , Na ve Bayes, Logistic Regresion, Latent Dirichlet Allocation LDA dan Non-negative Matrix Factorization NMF digunakan pada penelitian ini. Hasil dari penelitian didapatkan rerata presisi sentimen ulasan mencapai 85 dan algoritme terbaik untuk klasifikasi konten ulasan didapatkan menggunakan SVM dengan nilai rerata f1-score 84.38 menggunakan fitur unigram sedangkan NMF berkerja lebih baik daripada LDA untuk menemukan topik pada teks ulasan.

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The popularity of smartphones and mobile applications makes app downloads and users of applications rises exponentially. Users can provide reviews related to their experience during using the app, these reviews may contain valuable complaints or suggestions which can be used for further in depth review based on the reviews given before. However, the large number volume of the reviews can make it very difficult to find and understand the information contained in a review. To solve the problem in this study proposes a model that can diging information by categorizing the content and sentiment reviews using machine learning technique. The algorithm SentiStrength, Support Vector Machine SVM , Na ve Bayes, Logistic Regression, Latent Dirichlet Allocation LDA and Non-negative Matrix Factorization NMF are used in this study. The result of the research shows that the average sentiment precision of review is 85 and the best algorithm for the review content classification is obtained using SVM with an average f1-score 84.38 using unigram feature whereas the NMF works better than LDA to find topics in a reviews."

""This book explores the tools and techniques that enable educators to leverage wireless applications and social networks to improve learning outcomes and provide creative ways to increase access to educational resources"--"

"The BER performance of trellis coded (TC) 8PSK with 2-branch selection (SC) and maximal ratio combining (MRC) diversities on mobile satellite communication system, which channel characterized by Nakagami fading channel is investigated. The special case of 2 branch SC and MRC diversities on independent and spatially correlated Nakagami fading are analyzed in detail, It is shown that the BER performance of TC 8PSK with diversity is better than that system without diversity, and the BER performance of system with diversity increases with increasing the Nakagami fading parameter m. Although the correlation between branches causes signal-to-noise ratio (SNR) loss relative to uncorrelated fading case for 2 branches SC and MRC diversities, the SC and MRC diversities can lead the diversity gain, that is, the improvement of BER performance of TC 8PSK with diversity is obtained over the TC 8PSK without diversity. In addition, the effect of antenna separation which causes cross correlation between the fading signals envelops on the performance of TC 8PSK with 2 branch SC and MRC diversities is also considered."

"CONTENTS :- A NOTE FROM OUR SPONSOR - EXECUTIVE SUMMARY - INTRODUCTION - SECTION 1: WHERE ARE WE? - SECTION 2: WHAT?S HOLDING US BACK? - SECTION 3: FROM BLEEDING EDGE TO LEADING EDGE - CONCLUSION & POLICY RECOMMENDATIONS - REFERENCES - ABOUT THE AUTHORS AND CONTRIBUTORS - ABOUT THE CONTRIBUTING ORGANIZATIONS - APPENDIX: MOBILE LEARNING SURVEY OVERVIEW "

"Mobile learning (m-learning) memungkinkan pengaksesan materi elektronic learning (e-learning) melalui perangkat mobile. Hal yang penting untuk dipertimbangkan adalah adanya kemampuan adaptasi presentasi aplikasi Web yang sesuai dengan kebutuhan perangkat yang digunakan oleh client. Tesis ini membahas tentang perancangan dan implementasi m-learning yang adaptive terhadap perangkat client, serta analisa terhadap unjuk kerja aplikasi. Salah satu teknik adaptasi yang dapat dilakukan di server adalah teknik transformasi, yaitu adaptasi yang melibatkan konversi dari suatu markup language ke markup language lainnya. Teknologi eXtensible Markup Language (XML) yang menerapkan pemisahan antara data dengan presentasinya memberikan kemudahan untuk melakukan teknik adaptasi dengan transformasi ini. Keuntungan teknologi XML adalah pengelolaan situs Web yang lebih efisien, karena hanya diperlukan satu format data untuk sebuah konten. Tampilan yang sesuai dengan kapabilitas perangkat dihasilkan oleh stylesheet yang spesifik untuk setiap perangkat (single pipeline). Pembangunan aplikasi ini menggunakan perangkat lunak open source.

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Mobile learning (m-learning) allowed electronic learning materials accessing trough mobile device. The important thing to consider is the Web applications presentation adaptation capability to meet the client's device requirement.

This thesis research about the design and implementation of m-learning adaptaivity towards client's device as well as application performance analysis. transformation. In this design open source software is choosed. One of adaptation techniques runs in server side is transformation technique, the one that involves conversion from one markup language to other markup language.

eXtensible Markup Language (XML) technology that implemented data and presentation separation gives easy way to use this adaptation techique through transformation. The advantage of the XML technology is eficiency in Web sites management, because it only single data format neede for each content. The presentation that meet the device capability is produced by specific stylesheet for each device (single pipeline). The development of this application using open source software."