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International Journal of Advances in Intelligent Informatics
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.
Articles
70
Articles
Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

Sofian, Ian Mochamad, Affandi, Azhar Kholiq, Iskandar, Iskhaq, Apriani, Yosi

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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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 results. 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 in the Siak bridges, Pekanbaru City, Indonesia

kausarian, Husnul, Sri Sumantyo, Josaphat Tetuko, putra, Dewandra bagus eka, Suryadi, Adi, Gevisioner, Gevisioner

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Pekanbaru is the capital city of Riau province, located in the center of the island of Sumatra as the rapid growth city in Indonesia. The city is split by the Siak River which is the deepest river in Indonesia. Four big bridges connect this city. They are named Siak Bridge; I, II, III and IV. The quality of the Siak bridges deteriorated seriously at this time, especially the deflection of Siak III Bridge, despite its being completely rebuilt in recent times. 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 also 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 small Unmanned Aerial Vehicle (UAV) 3D model analysis show no negative land deformation. Differential Interferometric Synthetic Aperture Radar (DInSAR) analysis shows the amount of positive deformation of Siak I Bridge is 81 cm, Siak II Bridge is 48 cm, Siak III Bridge is 89 cm, and Siak IV Bridge is 92. Deflection on Siak III Bridge was detected at around 25-26 cm. These models and findings will be helpful in a new way of measuring the bridge deformation on a big scale.

Mathematics and statistics related studies in Indonesia using co-authorship network analysis

Nadhiroh, Irene Muflikh, Hardiyati, Ria, Amelia, Mia, Handayani, Tri

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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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. 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/Statistics related studies in Indonesia possesses as a scale-free network and followed the powerlaw distribution. This research shows the achievement of Indonesian scholars of Mathematics and Statistics, and can be used to evaluate the knowledge transfer in Mathematics and Statistics.

Parallel mathematical models of dynamic objects

Voliansky, Roman, Pranolo, Andri

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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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 algorithm of its usage for transforming serial mathematical model into parallel ones. This algorithm is based on partial fraction decomposition of object’s transfer function. Usage of proposed algorithms is one of the ways to decrease calculation time and improve PC usage while simulation is being performed. We prove our approach by considering 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, proposed approach’s usage allow us reduce calculation time more than 20% by using several CPU’s cores while calculations is being performed.

Cuckoo inspired algorithms for feature selection in heart disease prediction

Usman, Ali Muhammad, Yusof, Umi Kalsom, Naim, Syibrah

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Heart disease is the 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 i.e. 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 results obtained 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.

Bootstrap-based model selection in subset polynomial regression

Suparman, Suparman, Rusiman, Mohd Saifullah

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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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.

Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning

Chieng, Hock Hung, Wahid, Noorhaniza, Pauline, Ong, Perla, Sai Raj Kishore

International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

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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.

Variable precision rough set model for attribute selection on environment impact dataset

Apriani, Ani, Tri Riyadi Yanto, Iwan, Fathurrohmah, Septiana, Haryatmi, Sri, Danardono, Danardono

International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment.

A novel intelligent approach for detecting DoS flooding attacks in software-defined networks

Latah, Majd, Toker, Levent

International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Software-Defined Networking (SDN) is an emerging networking paradigm that provides an advanced programming capability and moves the control functionality to a centralized controller. This paper proposes a two-stage novel intelligent approach that takes advantage of the SDN approach to detect Denial of Service (DoS) flooding attacks based on calculation of packet rate as the first step and followed by Support Vector Machine (SVM) classification as the second step. Flow concept is an essential idea in OpenFlow protocol, which represents a common interface between an SDN switch and an SDN controller. Therefore, our system calculates the packet rate of each flow based on flow statistics obtained by SDN controller. Once the packet rate exceeds a predefined threshold, the system will activate the packet inspection unit, which, in turn, will use the (SVM) algorithm to classify the previously collected packets. The experimental results showed that our system was able to detect DoS flooding attacks with 96.25% accuracy and 0.26% false alarm rate.

Biased support vector machine and weighted-smote in handling class imbalance problem

Hartono, Hartono, Sitompul, Opim Salim, Tulus, Tulus, Nababan, Erna Budhiarti

International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Class imbalance occurs when instances in a class are much higher than in other classes. This machine learning major problem can affect the predicted accuracy. Support Vector Machine (SVM) is robust and precise method in handling class imbalance problem but weak in the bias data distribution, Biased Support Vector Machine (BSVM) became popular choice to solve the problem. BSVM provide better control sensitivity yet lack accuracy compared to general SVM. This study proposes the integration of BSVM and SMOTEBoost to handle class imbalance problem. Non Support Vector (NSV) sets from negative samples and Support Vector (SV) sets from positive samples will undergo a Weighted-SMOTE process. The results indicate that implementation of Biased Support Vector Machine and Weighted-SMOTE achieve better accuracy and sensitivity.