scholarly journals Mapping College Research Roadmap Based on Information Retrieval by Lecturers Scientific Publications Documents

2019 ◽  
Author(s):  
rusda wajhillah ◽  
Agung Wibowo ◽  
Saeful Bahri
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Tree Model ◽  
Scientific Publications ◽  
Space Model ◽  
Document Frequency ◽  
Scientific Papers

The quality of research needs to be directed and classified for improvement. A college roadmap must accordance interest and expertise from it lecturers. Therefore, be the duty of every college to create a strategic plan and pre-eminent research. Faculty in most all College has produced many scientific publications. Publication document of scientific papers is one example of unstructured documents. Its contents form of writing style, mostly defined by the author language. Generally, the document title only determined the maximum number of words. The main objective of the information retrieval system is to determine the documents keywords from the query provided by the user in a group of documents. TF/IDF Algorithm (Term Frequency – Inversed Document Frequency) and the Vector Space Model algorithm is several methods of the algorithm that can utilize on text mining in analysing phases as options document classification determination-based solutions words that often appear on the document title. This paper can help decision makers to determine, assess, adapt research roadmap to College. The depiction of a tree model using long-term roadmap makes it easier to read and understand. [Kualitas penelitian perlu diarahkan dan diklasifikasikan untuk perbaikan. Roadmap perguruan tinggi harus sesuai dengan minat dan keahlian dari dosen. Karena itu, jadilah tugas setiap perguruan tinggi untuk membuat rencana strategis dan penelitian unggulan. Fakultas - fakultas di hampir semua perguruan tinggi telah menghasilkan banyak publikasi ilmiah. Dokumen publikasi karya ilmiah adalah salah satu contoh dokumen tidak terstruktur. Isinya berupa gaya penulisan, sebagian besar ditentukan oleh bahasa penulis. Secara umum, judul dokumen hanya menentukan jumlah kata maksimum. Tujuan utama dari sistem pencarian informasi adalah untuk menentukan kata kunci dokumen dari permintaan yang diberikan oleh pengguna dalam sekelompok dokumen. Algoritma TF / IDF (TermFrequency - Inversed Document Frequency) dan algoritma Vector Space Model adalah beberapa metode algoritma yang dapat digunakan pada penambangan teks dalam menganalisis fase sebagai opsi dokumen klasifikasi penentuan kata-kata solusi berdasarkan solusi yang sering muncul pada judul dokumen. Makalah ini dapat membantu para pembuat keputusan untuk menentukan, menilai, mengadaptasi peta jalan penelitian ke perguruan tinggi. Penggambaran model pohon menggunakan peta jalan jangka panjang membuatnya lebih mudah dibaca dan dipahami.]

scholarly journals PENERAPAN SISTEM TEMU KEMBALI INFORMASI PADA KUMPULAN DOKUMEN SKRIPSI

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Karter D. Putung ◽  
Arie S.M. Lumenta ◽  
Agustinus Jacobus
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Retrieval System ◽  
Vector Space Model ◽  
Information Retrieval System ◽  
Frequency Vector ◽  
Inverse Document Frequency ◽  
Space Model ◽  
Document Frequency

Abstrak - Sistem temu kembali informasi (information retrieval system)merupakan sistem yang digunakan untuk menemukan informasi yang relevan dengan kebutuhan dari penggunanya, dengan menerapkan sistem tersebut permasalahan pencarian informasi dokumen skripsi bisa memberikan hasil yang relevan sesuai kebutuhan pengguna. Terdapat dua proses utama dalam sistem temu kembali informasi yaitu indexing dan retrieval. Proses indexing adalah proses untuk memberikan bobot pada kata dalam dokumen, metode pembobotan pada penelitian ini menggunakan metode pembobotan TF-IDF. Prosesretrieval adalah proses untuk menghitung kemiripan query terhadap dokumen, perhitungan kemiripan menggunakan konsepvector space modeldengan mencari nilai cosine similarity.Tujuan dari penelitian ini adalah untuk mengembangkan dan mengimplementasikan pengindeksan otomatis untuk membangun sistem pencarian dokumen di dalam sebuah system penyimpanan dokumen dengan konsep temu-kembali informasi. Kata kunci : Information retrieval,Term Frequncy Inverse Document Frequency, Vector Space Model.

scholarly journals System of Information Feedback on Archive Using Term Frequency-Inverse Document Frequency and Vector Space Model Methods

2020 ◽  
Vol 3 (1) ◽  
pp. 36-42
Author(s):  
Didit Suhartono ◽  
Khodirun Khodirun
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Retrieval Performance ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Space Model ◽  
Storage And Retrieval ◽  
Document Frequency ◽  
Speed Up

The archive is one of the examples of documents that important. Archives are stored systematically with a view to helping and simplifying the storage and retrieval of the archive. In the information retrieval (Information retrieval) the process of retrieving relevant documents and not retrieving documents that are not relevant. To retrieve the relevant documents, a method is needed. Using the Term Frequency-Inverse Document and Vector Space Model methods can find relevant documents according to the level of closeness or similarity, in addition to applying the Nazief-Adriani stemming algorithm can improve information retrieval performance by transforming words in a document or text to the basic word form. then the system indexes the document to simplify and speed up the search process. Relevance is determined by calculating the similarity values between existing documents by querying and represented in certain forms. The documents obtained, then the system sort by the level of relevance to the query.

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

On Generalized Vector Space Model in Information Retrieval

1985 ◽  
Vol 8 (2) ◽  
pp. 253-267
Author(s):  
S.K.M. Wong ◽  
Wojciech Ziarko
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
A Priori ◽  
Vector Space Model ◽  
Smart System ◽  
Space Model ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Index Terms ◽  
Minimal Modification

In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems.

scholarly journals The Performance of Boolean Retrieval and Vector Space Model in Textual Information Retrieval

2017 ◽  
Vol 11 (1) ◽  
pp. 33 ◽  
Author(s):  
Budi Yulianto ◽  
Widodo Budiharto ◽  
Iman Herwidiana Kartowisastro
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Experimental Results ◽  
Inverted Index ◽  
Exact Results ◽  
Textual Information ◽  
Space Model ◽  
Corpus Data

Boolean Retrieval (BR) and Vector Space Model (VSM) are very popular methods in information retrieval for creating an inverted index and querying terms. BR method searches the exact results of the textual information retrieval without ranking the results. VSM method searches and ranks the results. This study empirically compares the two methods. The research utilizes a sample of the corpus data obtained from Reuters. The experimental results show that the required times to produce an inverted index by the two methods are nearly the same. However, a difference exists on the querying index. The results also show that the numberof generated indexes, the sizes of the generated files, and the duration of reading and searching an index are proportional with the file number in the corpus and thefile size.

scholarly journals Analysis of a Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for Information Retrieval

2012 ◽  
Vol 12 (1) ◽  
pp. 34-48 ◽  
Author(s):  
Ch. Aswani Kumar ◽  
M. Radvansky ◽  
J. Annapurna
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Formal Concept Analysis ◽  
Vector Space Model ◽  
Latent Semantic Indexing ◽  
Concept Analysis ◽  
Formal Concept ◽  
Semantic Indexing ◽  
Space Model ◽  
Classical Vector

Abstract Latent Semantic Indexing (LSI), a variant of classical Vector Space Model (VSM), is an Information Retrieval (IR) model that attempts to capture the latent semantic relationship between the data items. Mathematical lattices, under the framework of Formal Concept Analysis (FCA), represent conceptual hierarchies in data and retrieve the information. However, both LSI and FCA use the data represented in the form of matrices. The objective of this paper is to systematically analyze VSM, LSI and FCA for the task of IR using standard and real life datasets.

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