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"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 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."

"Perkembangan teknologi Artificial Intelligence (AI), terkhusus AI text generators (AITGs), telah membawa perubahan signifikan dalam kehidupan manusia di Indonesia. Kehadiran AITGs berhasil mengubah perilaku seseorang mencapai berbagai tujuan, misalnya sebagai sumber belajar dan berpotensi menggantikan popularitas Google SE sebagai penyedia informasi paling populer saat ini. Penelitian ini bertujuan untuk memahami lebih jauh fenomena perpindahan dari Google SE ke AITGs dan memahami faktor-faktor yang memengaruhi terjadinya perilaku ini. Penelitian ini menggunakan kerangka PPM sebagai acuan pembentukan model. Penelitian ini melakukan analisis kualitatif terhadap 11 responden dan analisis kuantitatif terhadap 491 responden. Analisis data dilakukan dengan menggunakan grounded theory dan PLS-SEM modelling menggunakan bantuan aplikasi SmartPLS 4. Hasil penelitian ini mengungkapkan bahwa faktor low searching performance, explainability, inertia, perceived usefulness, social interaction, dan adaptability berpengaruh terhadap intensi berpindah dari Google SE ke AITGs. Sebaliknya, faktor privacy concern, intrusiveness of advertisement, perceived risk, dan perceived ease of use tidak berpengaruh secara signifikan terhadap intensi berpindah dari Google SE ke AITGs. Hasil tersebut diharapkan dapat membuka peluang bagi pengembangan ilmu pengetahuan secara umum dan terkhusus dalam konteks AITGs sebagai sumber belajar. Penelitian ini diharapkan dapat menjadi sumber informasi bagi masyarakat terkait AITGs sebagai sumber belajar, acuan bagi akademisi dan pengajar dalam penyusunan kurikulum dan aturan, serta bermanfaat bagi pelaku bisnis dan pengembang untuk meningkatkan fungsionalitas yang sesuai dengan kebutuhan masyarakat.

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The development of Artificial Intelligence (AI) technology, especially AI text generators (AITGs), has brought significant changes to human life in Indonesia. The presence of AITGs has succeeded in changing a person's behavior to achieve various goals, for example as a learning resource, and has the potential to replace the popularity of Google SE as the most popular information provider today. This research aims to understand further the phenomenon of moving from Google SE to AITGs and understand the factors that influence this behavior. This research uses the PPM framework as a reference for model formation. This research conducted a qualitative analysis of 11 respondents and a quantitative analysis of 491 respondents. Data analysis was carried out using grounded theory and PLS-SEM modeling using the SmartPLS 4 application. The results of this study revealed that the factors of low searching performance, explainability, inertia, perceived usefulness, social interaction, and adaptability influenced the intention to switch from Google SE to AITGs. On the other hand, the factors of privacy concern, intrusiveness of advertisement, perceived risk, and perceived ease of use do not significantly influence the intention to switch from Google SE to AITGs. It is hoped that these results will open up opportunities for the development of knowledge in general and specifically in the context of AITGs as a learning resource. It is hoped that this research can be a source of information for the community regarding AITGs as a learning resource, a reference for academics and teachers in preparing curricula and regulations, as well as being useful for business people and developers to improve functionality by community needs."

"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."