Module/Course Title: Information Retrieval
Module course code KOMS120503	Student Workload 119 hours	Credits 3 / 4.5 ETCS	Semester 5	Frequency Odd Semester	Duration 16
1	Type of course Field of Study Courses	Contact hours 40 hours of face-to-face (theoretical) class activity	Independent Study 48 hours of independent activity 48 hours of structured activities	Class Size 30
2	Prerequisites for participation (if applicable) -
3	Learning Outcomes Students can demonstrate systematic thinking in analyzing and designing intelligent system solutions Students can apply effective methods in developing intelligent systems Students can create and evaluate intelligent systems Students can describe information retrieval concepts such as indexing, searching, and evaluation of search engines Students can apply text classification, clustering, and summarization approaches Students can design and create a text-based search engine Students can evaluate the performance of information retrieval with various evaluation techniques
4	Subject aims/Content This module provides a comprehensive exploration of the field of information retrieval, starting with an introduction to its core principles. The course explores the fundamental concepts underlying information retrieval, including indexing, searching, and evaluating search engines. Starting with the concepts of indexing such as boolean retrieval, index construction, scoring and weighting, and vector space model. The module addresses the process of web crawling for efficient data collection and web search, demonstrate the techniques employed by search engines to index and retrieve information from the World Wide Web. The module also emphasizes the evaluation of search engines, providing students with insights into assessing information retrieval systems effectiveness and performance, as well as covers a range of specific topics related to information retrieval, such as relevance feedback and query expansion. Study Material Database vs IR (Simple IR example -Boolean query) Text processing - text statistics Basic Concepts of Information Gathering System Basic Concepts of Information Retrieval System Information Retrieval System Components Differences in Information Retrieval System with other Systems Inverted index Inverted index construction Indexing (manual and automatic): tokenization, stopwords, stemming, weighting, IR Modeling Boolean Model Vector Space Model IR models IR Modeling Boolean models Vector space model I IR Evaluation Evaluation Benchmarks Recall Precision Interpolation Other evaluation measures Relevance Feedback Probabilistic Relevance Feedback Pseudo relevance feedback Query Expansion Probability ranking Binary independence model Language model for IR Probability ranking Binary independence model Language model for IR - Text Classification Document classification Probability classification Vector space classification Text Classification Document classification Probability classification Vector space classification Clustering Clustering in IR Flat clustering : K-means, model- based Hierarchical clustering : dendogram, single-link, complete link, average link Mabeling Text Summarization Document summary Summary type Approach: traditional, statistics XML Basic Concept  Model for XML IR XML IR model Evaluation Model MIRS Pattern Recognition for Multimedia Contentd Analysis Image Processing for Feature Extraction Question Answering System and CLIR QA vs IR QAS method and evaluation CLIR Translation method -
5	Teaching methods Lectures, discussions and questions and answers
6	Assesment Methods Attendance and Participation
7	This module/course is used in the following study programme/s as well Computer Science Study Programme
8	Responsibility for module/course A.A. Gede Yudhi Paramartha, S.Kom., M.Kom. NIDN : 0022068803
9	Other Information Books: Yates, R.B., Neto, B.R., 2009, Modern Information Retrieval, ACM Press New York, Addition Wesley. Manning, C. D., Raghavan, P., and Schutze, H., 2008, Introduction to Information Retrieval, Cambridge University Press Publications: Paramartha, et al. Ontology-based Learning Object Searching Technique with Granular Feature Extraction, in Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS '14) Liu, et al. Data Mining and Information Retrieval in the 21st century: A bibliographic review, in Computer Science Review Volume 34, November 2019 Lin, et al. Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format, in SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Websites: What is Information Retrieval? (https://www.geeksforgeeks.org/what-is-information-retrieval/) NLP - Information Retrieval (https://www.tutorialspoint.com/natural_language_processing/natural_language_processing_information_retrieval.htm)