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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 6 Documents
 1 
Search results for , issue "Vol 2, No 3 (2016): November 2016" : 6 Documents clear
Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami
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.v2i3.69

Abstract

Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast
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%.
Overdispersion study of poisson and zero-inflated poisson regression for some characteristics of the data on lamda, n, p Lili Puspita Rahayu; Kusman Sadik; Indahwati Indahwati
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.v2i3.73

Abstract

Poisson distribution is one of discrete distribution that is often used in modeling of rare events. The data obtained in form of counts with non-negative integers. One of analysis that is used in modeling count data is Poisson regression. Deviation of assumption that often occurs in the Poisson regression is overdispersion. Cause of overdispersion is an excess zero probability on the response variable. Solving model that be used to overcome of overdispersion is zero-inflated Poisson (ZIP) regression. The research aimed to develop a study of overdispersion for Poisson and ZIP regression on some characteristics of the data. Overdispersion on some characteristics of the data that were studied in this research are simulated by combining the parameter of Poisson distribution (λ), zero probability (p), and sample size (n) on the response variable then comparing the Poisson and ZIP regression models. Overdispersion study on data simulation showed that the larger λ, n, and p, the better is the model of ZIP than Poisson regression. The results of this simulation are also strengthened by the exploration of Pearson residual in Poisson and ZIP regression.
Brainwaves feature classification by applying K-Means clustering using single-sensor EEG Ahmad Azhari; Leonel Hernandez
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.v2i3.86

Abstract

The use of brainwave signal is a step in the introduction of the individual identity using biometric technology based on characteristics of the body. Brainwave signal has unique characteristics and different on each individual because the brainwave cannot be read or copied by people so it is not possible to have a similarity of one person with another person. To be able to process the identification of individual characteristics, which obtained from the signal brainwave, required a pattern of brain activity that is prominent and constant. Cognitive activity testing using a single-sensor EEG (Electroencephalogram) divided into two categories, called the activity of cognitive involving the ability of the right brain (creativity, imagination, holistic thinking, intuition, arts, rhythms, nonverbal, feelings, visualization, tune of songs, daydreaming) and the left brain (logic, analysis, sequences, linear, mathematics, language, facts, think in words, word of songs, computation) give a different cluster based on two times the test on mathematical activities (no cluster slices of experiment 1 and experiment 2). The result showed that cognitive activity based on math activity can provide a signal characteristic that can be used as the basis for a brain-computer interface applications development by utilizing EEG single-sensor.
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: Minimum makespan task scheduling algorithm in cloud computing N Sasikaladevi
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.v2i3.59

Abstract

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Minimum makespan task scheduling algorithm in cloud computing” Vol 2, No 3, pp. 123-130, November 2016, DOI: http://dx.doi.org/10.26555/ijain.v2i3.59.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 Grid and Distributed Computing, Vol. 9, No. 11, pp. 61-70, 2016, DOI: http://dx.doi.org/10.14257/ijgdc.2016.9.11.05.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.

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