cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 8 Documents
 1 
Search results for , issue "Vol 4, No 2 (2018): July 2018" : 8 Documents clear
Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function Ian Mochamad Sofian; Azhar Kholiq Affandi; Iskhaq Iskandar; Yosi Apriani
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.208

Abstract

Two models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction.
Image processing of alos palsar satellite data, small unmanned aerial vehicle (UAV), and field measurement of land deformation Husnul kausarian; Josaphat Tetuko Sri Sumantyo; Dewandra bagus eka putra; Adi Suryadi; Gevisioner Gevisioner
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.221

Abstract

Pekanbaru, Indonesia is connected by four big bridges, Siak Bridge; I, II, III and IV. The quality of the Siak bridges deteriorated seriously at this time. Geological mapping for the land subsidence potency was conducted using small Unmanned Aerial Vehicle (UAV) in the Siak Bridge areas. The study of the Siak bridges are supported by the Differential Interferometric Synthetic Aperture Radar (DInSAR) analysis using ALOS PALSAR satellite data, and the deflection observation that occurs in Siak III Bridge was observed by field measurement. The results of 3D model analysis showed that there is no negative land deformation. DInSAR analysis shows the amount of positive deformation of Siak I is 81 cm, Siak II is 48 cm, Siak III is 89 cm, and Siak IV is 92. Deflection on Siak III Bridge was detected at around 25-26 cm. These models could be used as a new way of measuring the bridge deformation on a big scale.
Mathematics and statistics related studies in Indonesia using co-authorship network analysis Irene Muflikh Nadhiroh; Ria Hardiyati; Mia Amelia; Tri Handayani
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.120

Abstract

Indonesian scholars have published a numbers of articles in numerous international publications, however, it still lags behind other Singapore, Malaysia, and Vietnam. This article performs a bibliometrics analysis and examine the collaboration network in Mathematics and Statistics related subject of scholars with Indonesian affiliation as recorded in Web of Science. In total, based on article publications during 2009-2017, 426 articles were retrieved. Bandung Institute of Technology (ITB) was the affiliation with the highest number of articles (48%) and number of authors (27%). Using Social Network Analysis to examine co-authorship networks, this research shows that the co-author network has the highest centrality in the ITB affiliation. Meanwhile, dependency of foreign affiliation is still high, shown as a high percentage (84% of all articles) of international co-authorship. Co-authorship network of Mathematics and Statistics related studies in Indonesia possesses as a scale-free network and followed the power law distribution. This research showed the achievement of Indonesian scholars of Mathematics and Statistics, and can be used to evaluate the knowledge transfer in these subjects and related areas.
Parallel mathematical models of dynamic objects Roman Voliansky; Andri Pranolo
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.229

Abstract

The paper deals with the developing of the methodological backgrounds for the modeling and simulation of complex dynamical objects. Such backgrounds allow us to perform coordinate transformation and formulate the algorithm of its usage for transforming the serial mathematical model into parallel ones. This algorithm is based on partial fraction decomposition of the transfer function of a dynamic object. Usage of proposed algorithms is one of the ways to decrease calculation time and improve PC usage while a simulation is being performed. We prove our approach by considering the example of modeling and simulating of fourth order dynamical object with various eigenvalues. This example shows that developed parallel model is stable, well-convergent, and high-accuracy model. There is no defined any calculation errors between well-known serial model and proposed parallel one. Nevertheless, the proposed approach’s usage allows us to reduce calculation time by more than 20% by using several CPU’s cores while calculations are being performed.
Bootstrap-based model selection in subset polynomial regression Suparman Suparman; Mohd Saifullah Rusiman
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.173

Abstract

The subset polynomial regression model is wider than the polynomial regression model. This study proposes an estimate of the parameters of the subset polynomial regression model with unknown error and distribution. The Bootstrap method is used to estimate the parameters of the subset polynomial regression model. Simulated data is used to test the performance of the Bootstrap method. The test results show that the bootstrap method can estimate well the parameters of the subset polynomial regression model.
Cuckoo inspired algorithms for feature selection in heart disease prediction Ali Muhammad Usman; Umi Kalsom Yusof; Syibrah Naim
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.245

Abstract

Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets.
Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning Hock Hung Chieng; Noorhaniza Wahid; Ong Pauline; Sai Raj Kishore Perla
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.249

Abstract

Activation functions are essential for deep learning methods to learn and perform complex tasks such as image classification. Rectified Linear Unit (ReLU) has been widely used and become the default activation function across the deep learning community since 2012. Although ReLU has been popular, however, the hard zero property of the ReLU has heavily hindering the negative values from propagating through the network. Consequently, the deep neural network has not been benefited from the negative representations. In this work, an activation function called Flatten-T Swish (FTS) that leverage the benefit of the negative values is proposed. To verify its performance, this study evaluates FTS with ReLU and several recent activation functions. Each activation function is trained using MNIST dataset on five different deep fully connected neural networks (DFNNs) with depth vary from five to eight layers. For a fair evaluation, all DFNNs are using the same configuration settings. Based on the experimental results, FTS with a threshold value, T=-0.20 has the best overall performance. As compared with ReLU, FTS (T=-0.20) improves MNIST classification accuracy by 0.13%, 0.70%, 0.67%, 1.07% and 1.15% on wider 5 layers, slimmer 5 layers, 6 layers, 7 layers and 8 layers DFNNs respectively. Apart from this, the study also noticed that FTS converges twice as fast as ReLU. Although there are other existing activation functions are also evaluated, this study elects ReLU as the baseline activation function.
Soil porosity modelling for immersive serious game based on vertical angle, depth, and speed of tillage Anang Kukuh Adisusilo; Mochamad Hariadi; Eko Mulyanto Yuniarno; Bambang Purwantana; Radi Radi
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.215

Abstract

The real data support the “seriousness” of the serious game and give more authentic situations, which can make players feel immersed in scenarios, and gain a real experience. Therefore, the modeler must be able to recognize whether a model reflects reality to identify and deal with divergences between theory and data. In this paper, we present a model for design a basis of immersive in serious games. The studied case is the tillage using a moldboard plow, by taking real data through an experiment use a device called soil bin. It aims to determine the effect of angle, depth, and speed on the soil porosity; by comparing the value of the smallest error using the polynomial function of the use of different orders. The result of an average smallest error with the polynomial approach is 1.10E-07 in the 3rd order, closer to the experimental value. Therefore, the model can be used for designing immersive serious game.

Page 1 of 1 | Total Record : 8

 1 

Filter by Year

2018 2018


Reset
Filter By Issues
All Issue Vol 8, No 1 (2022): March 2022 Vol 7, No 3 (2021): November 2021 (Issue in Progress) Vol 7, No 3 (2021): November 2021 Vol 7, No 2 (2021): July 2021 (Issue in Progess) Vol 7, No 2 (2021): July 2021 Vol 7, No 1 (2021): March 2021 Vol 6, No 3 (2020): November 2020 Vol 6, No 2 (2020): July 2020 Vol 6, No 1 (2020): March 2020 Vol 5, No 3 (2019): November 2019 Vol 5, No 2 (2019): July 2019 Vol 5, No 1 (2019): March 2019 Vol 4, No 3 (2018): November 2018 Vol 4, No 2 (2018): July 2018 Vol 4, No 1 (2018): March 2018 Vol 3, No 3 (2017): November 2017 Vol 3, No 2 (2017): July 2017 Vol 3, No 1 (2017): March 2017 Vol 2, No 3 (2016): November 2016 Vol 2, No 2 (2016): July 2016 Vol 2, No 1 (2016): March 2016 Vol 1, No 3 (2015): November 2015 Vol 1, No 2 (2015): July 2015 Vol 1, No 1 (2015): March 2015 More Issue