Intelligent Tutoring System Sebagai Upaya Inovatif dalam Pembelajaran untuk Pembelajaran Berbantuan Komputer

Perkembangan teknologi informasi dan komunikasi yang pesat juga telah merambah bidang pendidikan dan pengajaran. Penggunaan pembelajaran berbasis komputer dalam pembelajaran telah diteliti dan memberikan dampak positif dalam pembelajaran. Salah satu pembelaajran berbasis komputer yang saat ini masih  terus dikembangkan adalah  Intelligent Tutoring System (ITS) yang dikembangkan untuk mengatasi kelemahan pembelajaranberbasis komputer sebelumnya yang belum memperhatikan keberagaman siswa. ITS merupakan sebuah aplikasi komputer yang dibuat untuk meniru mimik manusia dalam memberikan materi pengajaran. ITS menggunakan pendekatan one-to-one.  ITS merupakan sistem yang cerdas karena memiliki komponen kecerdasan buatan

Intelligent Tutoring System sebagai Upaya Inovatif dalam Pembelajaran untuk Pembelajaran Berbantuan Komputer

Perkembangan teknologi informasi dan komunikasi yang pesat juga telah merambah bidang pendidikan dan pengajaran. Penggunaan pembelajaran berbasis komputer dalam pembelajaran telah diteliti dan memberikan dampak positif dalam pembelajaran. Salah satu pembelaajran berbasis komputer yang saat ini masih terus dikembangkan adalah Intelligent Tutoring System (ITS) yang dikembangkan untuk mengatasi kelemahan pembelajaran berbasis komputer sebelumnya yang belum memperhatikan keberagaman siswa. ITS merupakan sebuah aplikasi komputer yang dibuat untuk meniru mimik manusia dalam memberikan materi pengajaran. ITS menggunakan pendekatan one-to-one. ITS merupakan sistem yang cerdas karena memiliki komponen kecerdasan buatan

Information Support Technology of Ship Survey Based on Case-based Reasoning

Recently, the significance of ship inspections hasbeen increasingly recognized because sea pollution andsafety problems are occurring more and more frequently. However, current ship inspections rely on the experience ofthe workers. Therefore, it is difficult to understand, and hence to improve, the state of ship inspections. The present problemsare that the ship inspection technical support level in China is not balanced, especially as to the current situation with low level, poor technologyin under-developed areas. In this paper, the case technology about the case-based reasoning to the ship inspection is proposed. The methods of normative inspection case representation are discussed.This is considered to be the basis of case-based reasoning. Then the tertiary case structure with the index is defined, in which the K-nearest neighbor method to calculate the similarity between caseswas used so that the ship’s inspection information can be searched effectively. In addition, animproved retrievalstrategy of the nearest neighbor method is introduced. Therefore, in the inspection site,the inspectors can acquire support information by the case bases, and then the new cases are calculated automatically. Further, a ship inspection case managementwereintroduced to improve the stability of the system. By carrying the case-based reasoning into an inspection, an inspector can generate fault information and inspection information simply and easily. Some examples of the organization and retrieval are shown at the end of the paper

The development of hashing indexing technique in case retrieval

Case-based reasoning (CBR) considers previous experience in form of cases to overcome new problems. It requires many solved cases in case base in order to produce a quality decision. Since today, database technology has allowed CBR to use a huge case storage therefore the case retrieval process also reflects the final decision in CBR. Traditionally, sequential indexing method has been applied to search for possible cases in case base. This technique is worked fast when the number of cases is small but it consumes more time to retrieve when the number of data grows in case base.To overcome the weakness, this study researches the non-sequential indexing called hashing as an alternative to mine large cases and faster the retrieval time in CBR. Hashing indexing searches a record by determines the index using only an entry's search key without traveling to all records. This paper presents the review of a literature and early stages of the integration hashing indexing method in CBR. The concept of hashing indexing in case retrieving process, the model development, and the preliminary algorithm testing result will be discussed in this paper

Reputation-based maintenance in case-based reasoning

Case Base Maintenance algorithms update the contents of a case base in order to improve case-based reasoner performance. In this paper, we introduce a new case base maintenance method called Reputation-Based Maintenance (RBM) with the aim of increasing the classification accuracy of a Case-Based Reasoning system while reducing the size of its case base. The proposed RBM algorithm calculates a case property called Reputationfor each member of the case base, the value of which reflects the competence of the related case. Based on this case property, several removal policies and maintenance methods have been designed, each focusing on different aspects of the case base maintenance. The performance of the RBM method was compared with well-known state-of-the-art algorithms. The tests were performed on 30 datasets selected from the UCI repository. The results show that the RBM method in all its variations achieves greater accuracy than a baseline CBR, while some variations significantly outperform the state-of-the-art methods. We particularly highlight theRBM_ACBR algorithm, which achieves the highest accuracy among the methods in the comparison to a statistically significant degree, and the RBMcr algorithm, which increases the baseline accuracy while removing, on average, over half of the case basehis work has been partially supported by the SpanishMinistry of Science and Innovation with project MISMIS-LANGUAGE (grantnumber PGC2018-096212-B-C33), by the Catalan Agency of University andResearch Grants Management (AGAUR) (grants number 2017 SGR 341 and 2017SGR 574), by Spanish Network ‘‘Learning Machines for Singular Problems andApplications (MAPAS)’’ (TIN2017-90567-REDT, MINECO/FEDER EU) and by theEuropean Union’s Horizon 2020 research and innovation programme under theMarie Sklodowska-Curie grant agreement No. 860843Peer ReviewedPostprint (author's final draft

A concept drift-tolerant case-base editing technique

© 2015 Elsevier B.V. All rights reserved. The evolving nature and accumulating volume of real-world data inevitably give rise to the so-called "concept drift" issue, causing many deployed Case-Based Reasoning (CBR) systems to require additional maintenance procedures. In Case-base Maintenance (CBM), case-base editing strategies to revise the case-base have proven to be effective instance selection approaches for handling concept drift. Motivated by current issues related to CBR techniques in handling concept drift, we present a two-stage case-base editing technique. In Stage 1, we propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets the removal of noise in a dynamic environment, and in Stage 2, we develop an innovative Stepwise Redundancy Removal (SRR) algorithm, which reduces the size of the case-base by eliminating redundancies while preserving the case-base coverage. Experimental evaluations on several public real-world datasets show that our case-base editing technique significantly improves accuracy compared to other case-base editing approaches on concept drift tasks, while preserving its effectiveness on static tasks

Partner selection in virtual enterprises

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200

Mining competent case bases for case-based reasoning

Case-based reasoning relies heavily on the availability of a highly competent case base to make high-quality decisions. However, good case bases are difficult to come by. In this paper, we present a novel algorithm for automatically mining a high-quality case base from a raw case set that can preserve and sometimes even improve the competence of case-based reasoning. In this paper, we analyze two major problems in previous case-mining algorithms. The first problem is caused by noisy cases such that the nearest neighbor cases of a problem may not provide correct solutions. The second problem is caused by uneven case distribution, such that similar problems may have dissimilar solutions. To solve these problems, we develop a theoretical framework for the error bound in case-based reasoning, and propose a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently. We support our theory and algorithm with extensive empirical evaluation using different benchmark data sets