Nyoman Karna

Knowledge sharing between similar domain knowledge management systems

Knowledge Management System (KMS) provides a library of knowledge, starting from collecting, organizing, summarizing, analyzing, synthesizing, and decision-making. Each KMS usually designed to accommodate specific knowledge domain based on the expertise of the institution or company, which make each KMS has its own expertise. This characteristic of KMS gives benefit to user where they can find the perfect (deep and thorough) knowledge they need, however, this also create gap where user must first find the correct KMS and then search the correct document. This research proposes a model where a KMS has the ability to share its content (knowledge sharing) to other KMS with similar knowledge domain, which in return provides benefit such as knowledge enhancement for the first KMS (harvester). The model uses OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) as a basis of development to find a suitable KMS (repository), retrieve specific knowledge from the KMS and proposing a mechanism to store...

Knowledge sharing filtering on OAI-PMH

There are three kind of protocols available to establish knowledge sharing between agents like knowledge management system, web page, and expert system. All these three protocols are OAI-PMH, Semantic Web, and KQML. OAI-PMH developed and used by librarians as an excerpt of each document and it can be used as knowledge sharing system on metadata level. This is also the reason why PMH stands for Protocol for Metadata Harvesting. OAI-PMH make any data provider agents become open for any other harvester agents to harvest their metadata, as intended by Open Archives Initiatives. However, as open as their intention, there are some circumstances where some specific metadata are only intended for a specific harvester. Thus, this research propose a mechanism where a data provider can restrict access through credential filtering.

Implementation of e-leaming based on knowledge management system for Indonesian academic institution

As per May 2016, in the second semester of 2015-2016 there are 5.144.659 active students served by all academic institutions in Indonesia. This number is large compared to the number of academic document indexed by Google Scholar, which only reaches about 1.150.000 documents. The main purpose of this research is to propose a design of information system solution that has the capability on acquiring knowledge, manage the knowledge, and empowering the knowledge in the form of academic document, which in return can improve the number of documents indexed by Google Scholar. We use TM Forum Application Map or TAM from Frameworx 13.5 as a framework to design the solution as Knowledge Management System. Empowerment of well-documented explicit knowledge to create eLearning will focus on Data Presentation and Distribution functionality, which means both explain how eLearning presents the knowledge to create courses and how eLearning distribute the courses. There are several advantages offered by an eLearning based on Knowledge Management System, include creation of a course freely by using cross knowledge study, updating the reference whenever a newer edition is available, and creation of unlimited number of courses by using abundant explicit knowledge. This will gives further benefit in knowledge sharing and reuse. The novelty of this research is by implementing eLearning based on Knowledge Management System for Indonesia academic Institution.

Social CRM using web mining

Traditional CRM (Customer Relationship Management) contains 3 modules, Marketing, Sales, and Support, which rely on the customer relationship and profiling information. While the information contained in those 3 modules is input by operator, it will be prudent to gather much more information from the Internet. We can find relationship between customers and find their profile from the Internet. This information can be used to enrich and direct the CRM to perform better in supporting the business objectives. Gathering information from the Internet means that we need Information Retrieval and Information Extraction that involve many sources from Internet, such as social media, net blog, and news. This research provides the model of data mining utilization in traditional CRM to become social CRM. This research contributes for CRM enhancement where customer centric application becomes automated.

Knowledge Representation for Image Feature Extraction

In computer vision, the feature(s) of an image can be extracted as information using deep learning approach. This type of information can be used for further processing, for example to establish a visual semantic, which is a sentence that gives a description about the image. Usually this type of information is stored in a database point of view, which explains the relation between image feature and image description. This research proposes knowledge representation point of view to store the information gathered from image feature extraction, which in return, some new benefits can be obtained by using this approach. Two benefits that can be delivered by using knowledge representation instead of database point of view are integration capability with another source of information from related knowledge-based system and possibility to produce a high-level specific knowledge.

Social CRM using web mining for Indonesian academic institution

Indonesian academic institutions commonly use direct marketing strategy, such as visiting high schools to find potential candidate for students, however, only few institutions use social media as an alternative for marketing channel and as the source of information. Social CRM as a social media extension can gives benefits where direct marketing cannot, for example, marketing team can profile their potential candidate to provide a better proposal. To do this, social CRM must have a feature like web mining where a crawler engine gather information from social media for a specific customer, for example high school students and/or parents, and create better segmentation using clustering algorithm. This research proposes a model and implementation of web mining extension part for social CRM, especially for Indonesian academic institution. This model and web mining part can focus marketing strategy by segmenting potential candidates.