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"Novel processing and searching tools for the management of new multimedia documents have developed. Multimedia Information Retrieval (MIR) is an organic system made up of Text Retrieval (TR); Visual Retrieval (VR); Video Retrieval (VDR); and Audio Retrieval (AR) systems. So that each type of digital document may be analysed and searched by the elements of language appropriate to its nature, search criteria must be extended. Such an approach is known as the Content Based Information Retrieval (CBIR), and is the core of MIR. This novel content-based concept of information handling needs to be integrated with more traditional semantics. Multimedia Information Retrieval focuses on the tools of processing and searching applicable to the content-based management of new multimedia documents. Translated from Italian by Giles Smith, the book is divided into two parts. Part one discusses MIR and related theories, and puts forward new methodologies; part two reviews various experimental and operating MIR systems, and presents technical and practical conclusions."

"Skripsi ini membahas mengenai penelusuran informasi dengan menggunakan OPAC, hambatan yang dialami pemakai saat melakukan penelusuran dan tingkat keberhasilan dalam temu kembali dokumen. Penelitian ini menggunakan penelitian kualitatif dengan metode studi kasus dan disajikan secara deskriptif. Hasil penelitian menyarankan untuk memperbaiki fitur dan isi yang terdapat di OPAC, menambah fasilitas komputer dan membuat panduan mengenai penggunaan OPAC yang baik. Hal tersebut dilakukan agar mempermudah dan mempercepat pengguna dalam melakukan proses penelusuran informasi di perpustakaan.

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This essay discussed about information retrieval through OPAC, barriers experienced by user, and success rate in document retrieval. This research using qualitative methods and case studies presented by descriptive. Results of the essay is suggesting to fix the contents and feature of OPAC, adding computer facilities and create guidelines on the use of OPAC good. This is done in order to simplify and speed up the user in the process of information retrieval in the library."

"Website Helpdesk PDDikti memiliki sekumpulan knowledge yang belum didukung oleh sistem pencarian yang efektif. Penelitian ini bertujuan untuk membangun system temu-balik informasi yang efektif dengan skor metrik yang tinggi terhadap knowledge Helpdesk PDDikti. Metode yang digunakan dalam penelitian ini adalah penerapan model temu-balik informasi berbasis text matching pada berbagai model, yang kemudian disempurnakan dengan dua metode perbaikan ranking dokumen: (1) metode eskpansi istilah pada dokumen menggunakan prediksi kueri dari model doc2query, dan (2) metode reranking dokumen menggunakan model LambdaMART. Hasil penelitian menunjukkan bahwa di antara beberapa model temu-balik informasi berbasis text matching, model BM25 memberikan kinerja terbaik dengan skor MRR 0,781. Selanjutnya, hasil ranking dokumen dari model BM25 dapat ditingkatkan akurasinya melalui metode ekspansi istilah dengan lima kueri menggunakan pemilihan kandidat random sampling, yang meningkatkan skor MRR menjadi 0,799. Namun, penggunaan metode reranking dengan model LambdaMART untuk meningkatkan akurasi hasil ranking dokumen belum memberikan hasil yang lebih baik dibandingkan metode ekspansi istilah. Meskipun demikian, terdapat varian model reranking LambdaMART yang menggunakan fitur semantic similarity dan fitur skor agregat, yang mampu mengalahkan model BM25 tanpa ekspansi istilah, dengan skor MRR terbaik masing-masing 0,782 dan 0,787. Meskipun begitu, peningkatan ini masih belum cukup signifikan.

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The PDDikti Helpdesk website has a collection of knowledge that is not yet supported by an effective search system. This study aims to develop an effective information retrieval system with high metric scores for the PDDikti Helpdesk knowledge base. The method used in this research involves applying text matching-based information retrieval models across various models, which are then refined using two document ranking improvement methods: (1) term expansion in documents using query predictions from the doc2query model, and (2) document reranking using the LambdaMART model.The research results indicate that among several text matching-based information retrieval models, the BM25 model provides the best performance with an MRR score of 0.781. Furthermore, the document ranking results from the BM25 model can be improved in accuracy through the term expansion method with five queries using random sampling for candidate selection, which increases the MRR score to 0.799.However, the use of the reranking method with the LambdaMART model to improve document ranking accuracy did not yield better results compared to the term expansion method. Nevertheless, there are variants of the LambdaMART reranking model that use semantic similarity features and aggregate score features, which managed to outperform the BM25 model without term expansion, with the best MRR scores of 0.782 and 0.787, respectively. However, these improvements are still not significant enough."