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"The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Readers will find that the second edition includes significant changes that bring the text up to date on current information retrieval methods. For example, the authors have added a completely new chapter on link-structure algorithms used in search engines such as Google, and the chapter on user interface has been rewritten to specifically focus on search engine usability. To reflect updates in the literature on information retrieval, the authors have added new recommendations for further reading and expanded the bibliography. In addition, the index has been updated and streamlined to make it more reader friendly."

"ABSTRAKSalah satu hal yang dibutuhkan user dalam memudahkan melakukan adalah dengan menggunakan mesin pencarian atau yang disebut search engine. Search engine didesain agar dapat membantu pengguna dalam melakukan pencarian data. Fitur yang dapat digunakan dalam membantu pencarian data adalah Text Suggestion dan Text Correction. Text Suggestion dapat membantu pengguna dalam memperkirakan keyword apa yang akan ditulis untuk menemukan data yang paling sesuai. Text Correction adalah fitur untuk memperbaiki kesalahan penulisan, sehingga diharapkan dapat memperbaiki hasil pencarian. Levenshtein Distance, dapat digunakan untuk fitur Text Suggestion dan Correction dengan menghitung maksimum LD dengan variasi range dari satu sampai lima. Tujuan penelitian ini adalah menguji keakuratan Levenshtein Distance dalam membuat sistem Text Suggestion dan Text Correction. Metode yang digunakan adalah dengan menghitung tingkat kemiripan keyword dengan daftar referensi yang ada pada basis data, dan mengambil kata tersebut untuk dijadikan sebagai text suggestion maupun text correction. Dari hasil penelitian ini, akan didapatkan bahwa sebuah batasan maksimum Levenshtein Cost dapat mempengaruhi keakuratan hasil text correction dan text suggestion. Maksimum LD juga berpengaruh pada performa waktu baik pada Text suggestion dan Text Correction, dengan eksekusi waktu Text Correction lebih cepat dibanding Text Suggestion.Nilai maksimum LD yang optimal adalah dua atau tiga.

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ABSTRACTOne of the things required by the user in facilitating the search for data contained on the internet is to use a search engine or so-called search engines. Search engines must also be designed in order to assist users in searching data. Features that can be used in assisting data retrieval are Text Suggestion and Text Correction. Text Suggestion can help users in predicting what keywords will be written to find the most appropriate data. Text Correction is a feature to correct writing errors, so it is expected to improve search results. By utilizing Levenshtein Distance, it can be used for Text Suggestion feature by calculating maximum LD with variation range from one to five. The purpose of this research is to test the accuracy of Levenshtein Distance algorithm in making Text Suggestion and Text Correction system. The method used is to calculate the level of similarity of the keyword with a list of references in the database, and take the word to be used as a text suggestion or text correction. From the results of this study, it will be found that a maximum limit Levenshtein Cost can affect the accuracy of the results of text correction and text suggestion.The optimum of Maximum LD is two or three."

"Perkembangan era digital yang didukung dengan keberadaan internet telah meningkatkan perubahan perilaku masyarakat terhadap konsumsi media. Oleh karena itu, banyak pelaku usaha yang mulai mempertimbangkan strategi komunikasi pemasarannya dengan memproduksi berbagai jenis iklan termasuk iklan di search engine Google. Namun, sampai saat ini faktor-faktor yang paling mempengaruhi terhadap konversi penjualan di Google search engine yang paling efektif belum sepenuhnya dipahami. Adapun penelitian ini bertujuan untuk menganalisis iklan di Google search engine terutama pengaruh headline terhadap konversi. Hipotesis riset eksperimen penelitian ini menyatakan bahwa terdapat hubungan positif antara berbagai bentuk headline dan pengaruhnya terhadap konversi dengan moderating factor tren kata kunci yang diambil dari Google Trend. Pada penelitian ini, dilakukan riset experimen selama 1 bulan penuh (31 hari) dengan membagi variabel yang dikontrol adalah headline pada Google mobile search engine advertising. Sebagai kesimpulan, hasil penelitian ini menemukan bahwa headline iklan baik yang memiliki kata kunci tertentu dan penuhnya karakter dengan moderating factor Google Trend pada Google search engine advertising tidak memiliki hubungan positif terhadap konversi, namun lebih berpengaruh terhadap jumlah klik suatu iklan.

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The digital era, driven by the rapid development of the internet has significantly changed people's behavior towards media consumption. Various industries began to adjust their marketing communication strategies led by this change by using Google search engine advertising. However, to date, the factors that affecting the conversion in Google mobile search engine advertising is not yet fully understood. The paper aims to analyze the ads in the Google search engine advertising more importantly the effect of headline to conversion with keywor trend taken from Google Trend as moderating factor. This experiment research hypothesis shows that there is positive correlation among many types of headline and the influence to the conversion. This research is hold for about 1 full month by dividing control variable to the headline in Google search engine advertising. As a conclusion, the result of this research paper is that the headline of Google search engine advertising both contais certain keywords and having full character with Google Trend as moderating factor do not have positive correlation to the conversion, whereas it has positive correlation with the ads clicks."

"Academic search e ngines, intends to run through the current panorama of the academic search engines through a quantitative approach that analyses the reliability and consistence of these services. The objective is to describe the main characteristics of these engines, to highlight their advantages and drawbacks, and to discuss the implications of these new products in the future of scientific communication and their impact on the research measurement and evaluation. In short, Academic search engines presents a summary view of the new challenges that the Web set to the scientific activity through the most novel and innovative searching services available on the web."

"The book focuses on theoretical foundations of representing natural language texts as well as on concrete operations of automatic text processing. The present volume includes contributions to a wide range of topics in the context of processing of textual data. This relates to the learning of ontologies from natural language texts, the annotation and automatic parsing of texts as well as the detection and tracking of topics in texts and hypertexts. "

"Peningkatan data digital mendorong peningkatan kebutuhan teknik penggalian informasi. Media sosial merupakan salah satu penghasil data digital dalam jumlah besar, berupa aspirasi masyarakat mengenai apa yang terjadi di sekitar mereka. Maka dari itu, penelitian ini menganalisis respon masyarakat melalui akun twitter mengenai harga bahan pokok dan mengklasifikasikan respon tersebut menjadi dua kelompok; respon positif dan negatif. Penelitian ini menggunakan metode text mining, sedangkan asosiasi jenis bahan pokok dengan sentimen respon diukur menggunakan uji Chi Square dan Prosedur Marascuillo. Hasil penelitian menunjukkan bahwa Harga Susu, Harga Telur dan Harga Bawang Merah berasosiasi paling signifikan terhadap munculnya sentimen negatif dibandingkan komoditas lain.

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The increase number of digital data pushes the needs of techniques in mining the information. Social media creates a large pool of data consisting of people’s aspiration on what happen around them. Therefore, this research analyzes people’s responses through their twitter account on staple food prices and classify them into sentiment classes; positive and negative. Research is done using text mining and the association between types of staple foods and sentiments is analyzed using Chi Square Test and Marascuillo Procedure. The result reveals Milk price, Egg Price and Red Onion price associate with negative sentiment tweets most significantly than others."