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International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
Arjuna Subject : -
Articles 314 Documents
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K-means clustering based filter feature selection on high dimensional data Dewi Pramudi Ismi; Shireen Panchoo; Murinto Murinto
International Journal of Advances in Intelligent Informatics Vol 2, No 1 (2016): March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i1.54

Abstract

With hundreds or thousands of features in high dimensional data, computational workload is challenging. In classification process, features which do not contribute significantly to prediction of classes, add to the computational workload. Therefore the aim of this paper is to use feature selection to decrease the computation load by reducing the size of high dimensional data. Selecting subsets of features which represent all features were used. Hence the process is two-fold; discarding irrelevant data and choosing one feature that representing a number of redundant features. There have been many studies regarding feature selection, for example backward feature selection and forward feature selection. In this study, a k-means clustering based feature selection is proposed. It is assumed that redundant features are located in the same cluster, whereas irrelevant features do not belong to any clusters. In this research, two different high dimensional datasets are used: 1) the Human Activity Recognition Using Smartphones (HAR) Dataset, containing 7352 data points each of 561 features and 2) the National Classification of Economic Activities Dataset, which contains 1080 data points each of 857 features. Both datasets provide class label information of each data point. Our experiment shows that k-means clustering based feature selection can be performed to produce subset of features. The latter returns more than 80% accuracy of classification result.
RETRACTED: Comparison of running time between C4.5 and k-nearest neighbor (k-NN) algorithm on deciding mainstay area clustering Heru Ismanto; Retantyo Wardoyo
International Journal of Advances in Intelligent Informatics Vol 2, No 1 (2016): March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i1.49

Abstract

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Comparison of running time between C4.5 and k-nearest neighbor (k-NN) algorithm on deciding mainstay area clustering” Vol 2, No 1, pp. 1-6, March 2016, DOI: https://doi.org/10.26555/ijain.v2i1.49.This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in IOSR Journal of Computer Engineering (IOSR - JCE), Vol. 18, No. 2 (Mar-April 2016), pp. 86-92, 2016, DOI: http://doi.org/10.9790/0661-1802048692, URL: http://www.iosrjournals.org/iosr-jce/papers/Vol18-issue2/Version-4/K1802048692.pdf entitled "AnalysisofC4.5 andK-Nearest Neighbor(KNN)MethodonAlgorithmofClusteringForDeciding Mainstay Area".The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article.
A method for automatic gamelan music composition Khafiizh Hastuti; Khabib Mustafa
International Journal of Advances in Intelligent Informatics Vol 2, No 1 (2016): March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i1.57

Abstract

Aim of this study is designing a method for automatic gamelan music composition using rule-base expert system approach. The program is designed for non-expert user in order to help them composing gamelan music or analyzing their composition to achieve explanation and recommendation of ideal composition. There are 2 essential components in this method, which are knowledge and inference. Knowledge is represented into basic knowledge and melodic knowledge. Basic knowledge contains rules that control the structure of gamelan song, and melodic knowledge supports system in composing or analyzing notations sequence that fit the characteristics of melody in gamelan music. Basic knowledge represents basic rules of gamelan music that have quantitative value, so deterministic approach is used for basic knowledge acquisition. Melodic knowledge consists of dynamic data, so stochastic approach is used to create the melodic knowledge base. The rules of composing and analyzing a composition are defined based on basic knowledge and melodic knowledge. The inference engine is designed to compose and analyze a composition. Automatic composition for gamelan music is proposed using Generate and Test method (GAT) with random technique, and composition analysis is proposed using backward chaining method
Critical analysis of classification techniques for polarimetric synthetic aperture radar data Vikas Mittal; Dharmendra Singh; Lalit Mohan Saini
International Journal of Advances in Intelligent Informatics Vol 2, No 1 (2016): March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i1.52

Abstract

Full polarimetry SAR data known as PolSAR contains information in terms of microwave energy backscattered through different scattering mechanisms (surface-, double- and volume-scattering) by the targets on the surface of land. These scattering mechanisms information is different in different features. Similarly, different classifiers have different capabilities as far as identification of the targets corresponding to these scattering mechanisms. Extraction of different features and the role of classifier are important for the purpose of identifying which feature is the most suitable with which classifier for land cover classification. Selection of suitable features and their combinations have always been an active area of research for the development of advanced classification algorithms. Fully polarimetric data has its own advantages because its different channels give special scattering feature for various land cover. Therefore, first hand statistics HH, HV and VV of PolSAR data along with their ratios and linear combinations should be investigated for exploring their importance vis-à-vis relevant classifier for land management at the global scale. It has been observed that individually first hand statistics yield low accuracies. And their ratios are also not improving the results either. However, improved accuracies are achieved when these natural features are stacked together.
Implementation of fuzzy logic to measure supply chain agility Mehdi Karimimalayer; Nizaroyani Saibani
International Journal of Advances in Intelligent Informatics Vol 2, No 1 (2016): March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i1.32

Abstract

In our age of perennial changing environment, supply chain agility is a crucial factor having a great impact on the company's competitiveness. For transforming supply chain into an agile supply chain, first it is necessary to comprehend the meaning of agile supply chain, since agility has wide range of meanings and various dimensions which covers different aspects of an organization. Generally, however, there have been many researches on agility, proportionally; the concept of agility in supply chain has not been much surveyed. The circumstance unveils the necessity of a technique to measure the supply chain agility. The purpose of the article is to propose a technique, using fuzzy logic which supply chain agility be measured.
Human action recognition using support vector machines and 3D convolutional neural networks Majd Latah
International Journal of Advances in Intelligent Informatics Vol 3, No 1 (2017): March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i1.89

Abstract

Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.
Influence of overweight and obesity on the diabetes in the world on adult people using spatial regression Tuti Purwaningsih; Baharudin Machmud
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.72

Abstract

This research discussed about the case of diabetes, overweight, and obesity which aimed to determine the factors that most affect the number of adult people with Diabetes from Obesity and Overweight in the world and looking for the best spatial model to make predictions in the next period. This research based on data WHO in 2015 from The 2016 Global Nutrition Report. At 5% level of significance for 2015, factor that influence diabetes is obesity and the most excellent spatial model used in the analysis is Spatial Error Model (SEM) that use Weight Level Order 1 and has R2 value 81.82%.
Circular(2)-linear regression analysis with iteration order manipulation Muhamad Irpan Nurhab; Badaruddin Nurhab; Tuti Purwaningsih; Ming Foey Teng
International Journal of Advances in Intelligent Informatics Vol 3, No 2 (2017): July 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i2.90

Abstract

Data in the form of time cycle or point position to the angle of possibility is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data. This paper aims to examine the comparison of Linear Regression Analysis with Circular Regression Analysis. The writing method used is literature review using simulation data. Data simulation and analysis is done with the help of R program. The results showed that circular data is better analyzed by Circular Regression Analysis rather than Classical Linear Regression Analysis. The use of classical linear statistic method is not recommended due to the direction and the angle influence the position between one data with other data.
Data mapping process to handle semantic data problem on student grading system Arda Yunianta; Norazah Yusof; Arif Bramantoro; Haviluddin Haviluddin; Mohd Shahizan Othman; Nataniel Dengen
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.84

Abstract

Many applications are developed on education domain. Information and data for each application are stored in distributed locations with different data representations on each database. This situation leads to heterogeneity at the level of integration data. Heterogeneity data may cause many problems. One major issue is about the semantic relationships data among applications on education domain, in which the learning data may have the same name but with a different meaning, or learning data that has a different name with same meaning. This paper discusses on semantic data mapping process to handle semantic relationships problem on education domain. There are two main parts in the semantic data mapping process. The first part is the semantic data mapping engine to produce data mapping language with turtle (.ttl) file format as a standard XML file schema, that can be used for Local Java Application using Jena Library and Triple Store. The Turtle file contains detail information about data schema of every application inside the database system. The second part is to provide D2R Server that can be accessed from outside environment using HTTP Protocol. This can be done using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. To implement the semantic data process, this paper focuses on the student grading system in the learning environment of education domain. By following the proposed semantic data mapping process, the turtle file format is produced as a result of the first part of the process. Finally, this file is used to be combined and integrated with other turtle files in order to map and link with other data representation of other applications.
RETRACTED: Implementation of reversible jump MCMC algorithm to segment the piecewise Polynomial Regression Suparman Suparman
International Journal of Advances in Intelligent Informatics Vol 2, No 2 (2016): July 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i2.62

Abstract

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Implementation of reversible jump MCMC algorithm to segment the piecewise Polynomial Regression” Vol 2, No 2, pp. 88-93, July 2016, DOI: http://dx.doi.org/10.26555/ijain.v2i2.62.This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, Vol. 10, No. 5 (2016), pp. 232-235, URL: http://scholar.waset.org/1999.7/10004307, entitled "Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm".The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article.

Page 3 of 32 | Total Record : 314

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