Saiful Akbar

Fostering government transparency and public participation through linked open government data: Case study: Indonesian public information service

Open data refers to data that is freely available in the Internet and can be used, reused, and redistributed without restrictions from copyright or patent. This paper describes our approach to fostering government transparency and public participation through linked open government data (LOGD). We have analyzed how Indonesian Government deals with the open government data issues, and we define its maturity level based on the existing maturity framework. Then, we proposed a conceptual framework advance the Indonesian open government data maturity level to encourage further improvement on the government transparency and public participation. To show the feasibility of our approach, we developed a case study to show how the open data advancement could support the government transparency and public participation. Our case study shows promising result and we are eager to continue working in the area in a bigger scale.

Reshaping software engineering education towards 2020 engineers

In this paper, we present an overview on how to reshape the software engineering education in our undergraduate study program (i.e., curriculum program, software engineering curriculum package, and learning process) so that our graduates have sufficient skills to be the 2020 software engineers. We believe that the corner blocks to produce fine engineers are good understanding in the following areas: basic fundamentals and principles of science and computing, methodology, techniques-tools-platform, capability to understand domain problems, communication and personal skill, attitude to be a good learner and self disciplined. We translate these values to our undergraduate curriculum with an aim to produce general software engineer who are quick to master specific platforms/technologies and devices and to understand domain problems.

Direct access in content-based Audio Information Retrieval: A state of the art and challenges

This paper surveys Audio Information Retrieval (AIR) using a literature review and classification of articles from 1994 to 2010 with a keyword index and article abstract in order to explore how AIR methodologies and applications have developed during this period. Based on the scope of many papers and journals of AIR, this paper surveys and classifies AIR problem domains into five domains: AIR framework, audio feature extraction, audio classification, audio/music similarity, and audio tools/applications with their applications for different research and problem domains. Based on the current state of the art, we discuss the major challenges for the future.

Review of ontology matching with background knowledge

The ontology matching process with background knowledge is more suitable to match heterogeneous ontologies, since background knowledge is used as a mediator or a reference to identify relation between two concepts being matched. This method is called indirect matching and the system is called indirect matching system. This paper reviews the motivation that described the urgency of ontology matching, the various background knowledge and their strengths, also indirect matching process. At the end we provide the comparison of indirect matching system. Based on the comparison, mapping repair function were added to improve the quality of mapping. The purpose of this paper is to help in guiding new practitioners get a general idea on the ontology matching field and to determine possible research lines.

Comparisons of diagnosis in mapping repair systems

Coherent mappings become very important things in improving the quality of alignments in ontology matching process. Mapping repairs will restore the incoherent condition into the coherent one by removing undesired correspondences. The ability to remove the undesired correspondences as minimal as possible is called minimal diagnosis. The purpose of this paper is to analyze and to compare minimal diagnosis in LogMap Repair, AML Repair, and ALCOMO. The results are comparisons of repair systems and diagnosis techniques. There are two quadrants (of four quadrants) that are matched to the goal of minimal diagnosis, namely quadrant 1 and quadrant 4. The open issues in this field will be exploring the characteristic of four quadrants and comparing them with diagnosis technique.

Open data strategy for enhancing the productivity and competitiveness of fishery SMEs in Indonesia

The using of open data to improve transparency and public participation in the national development has become an issue in Indonesia. The public, including Small and Medium Enterprises (SMEs) can use the open data provided by the government and other related institutions in order to increase their profits and benefits. The SMEs in fish farming and processing are also trying to use open data in order to improve their productivity and competitiveness. However, current open data in fishery SMEs are scattered in different sites with different status and lack of strategy to integrate and utilize them efficiently. This paper proposed a top-down model as a strategy to identify problem, opportunity and challenges of integrating and using open data for fishery SMEs. The result is a model that can be used for supporting decisions made by SMEs.

Collecting health related text from patient health writings

The Internet has been a huge resource for sharing and collecting information including health related information. Some health related information is written by patients (lay persons) discussing their experience about health problems and treatments. This paper introduces our initial work on providing physicians with clinically useful patient health writings. More specifically, the paper presented our experiments, as a part of the whole research work, on filtering health related text from patient health writings. We focused on selecting possible feature for classifying text from breast cancer mailing list into health and non health related text. Using KNN classification method, we experimented with various features, i.e. all terms, all terms except most frequently used terms, UMLS terms, health related UMLS terms, and health related UMLS semantic types. The experiments showed that UMLS terms extracted from the text is a good feature, compared to the other features.

Multishape-features and text-feature integration on 3D model similarity retrieval

In this paper, we describe several 3D shape descriptors and integrate a textual descriptor with them for 3D model retrieval. We analyse five Shape-Feature Vector (FV) integration approaches, namely Pure FV Integration (PFI), Reduced FV Integration (RFI), Weight-Associated RFI (WRFI), Distance Integration (DI) and Rank Integration (RI). By running all possible-combinations of weighting factors on a training data set, the best weighting factor for each approach is obtained. Our experiments show that the best weighting factors improve the retrieval performance on not only the training data set, but also other data sets. This paper also shows that Distance Integration delivers the best retrieval effectiveness and Reduced FV Integration has the capability to deal with unknown query. In addition, the Distance Integration provides faster processing as it uses precomputed pair-wise distance and is more advantageous than PFI because of the dimension reduction. Hence, the use of both approaches (DI and RFI) is proposed. This paper also explains a use of the model file name as the only resource for text feature extraction. We study several textual similarity measures and then integrate multishape and text features into 3D model retrieval. Our experiments show that text feature can discriminate 3D models to each other in a certain degree of effectiveness, and integration text feature with multishape-feature improves retrieval effectiveness.

Public facilities recommendation system based on structured and unstructured data extraction from multi-channel data sources

Nowadays social media data has grown very rapidly by producing a huge amount and variety of data everyday. Those data can be analyzed and processed to deliver useful information especially for public needs. However, most of the data available in social media are unstructured. This paper proposes a recommendation system for public facilities by utilizing both structured and unstructured data gathered from multi-channel data sources. The system uses single-criteria rating, multi-criteria-rating, and text data as the inputs. The challenge is how to handle data variety such that any kind of data from any channel can be integrated. The second challenge is how to extract location-related data from the raw data. There are four data channels used in the system. Three of them are social media channels, i.e. Twitter, Instagram, and Foursquare, while the other is internal data channel built as a part of the system itself. The system deals with three categories of public facility, i.e. park, hospital, and mosque. The whole system consists of two sub systems, i.e. the extractor system including the rating input module and the recommendation system. The recommendation system is implemented as end-user mobile application such that the users are able to use it anytime and anywhere. The system successfully integrate data from different social media channels and in different format to provide users with useful information concerning public facilities in the form of recommendation (rating) and popularity of the facilities. The experiment has shown that above 90% of the data collected from the social media contains location-related information that is useful for further processing. The system has been tested using usability test, and it obtained an average users score 3.9 on a scale of 1 to 5.

A new direct access framework for speaker identification system

We present in this paper a new Direct Access Framework (DAF) for speaker identification system, to identify a speaker based on original characteristics of the human voice. Direct access method is a process to identify an object based on parts of the object itself, the parts called original characteristics. The proposed framework consists of two parts, the enrolment process and the identification process. Phases are as the following: speech preprocessing, speaker feature extraction, feature normalization, feature selection, speaker modeling, direct access method and speaker matching. In this paper, we used Indonesian speaker dataset containing 2,140 speech files, 142 speakers, 97 male and 45 female. The identification accuracy level based on MFCC features is 94.38% and the accuracy of speaker gender-based classification up to 100% based on pitch, flatness, brightness, and roll off features. The proposed framework helped the researcher in speaker identification system domain for implementing their proposed algorithms or model to obtain the best speaker identification system for various dataset. DAF is also could be used as a basic framework for the other multimedia data as well as image or video.