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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
77
Articles
Performance evaluation and mathematical analysis of direct sequence and frequency hopping spread spectrum systems under wideband interference

Bawahab, Fawzan Ghalib Abdul Karim, Kurniawan, Edi, Yuniarti, Elvan, Widiyatmoko, Bambang, Bayuwati, Dwi

International Journal of Advances in Intelligent Informatics 2018: Articles in press 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

This paper presents performance evaluation and comparison analysis of Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) systems. The evaluation and analysis are done based on the systems performance against wideband interferences. The interferences are signals with similar spectrum characteristic to the transmitted signals of DSSS and FHSS systems. Bit Error Ratio (BER) is used as evaluation parameter to assess the performance of both systems. Simulation and mathematical analysis are performed to test and verify the performance of both systems. Mathematical analysis also verifies that increasing Spreading Frequency on certain conditions will reduce the BER. This research also points out that FHSS system has a better performance compared to DSSS system indicated by smaller BER.

An efficient meta-heuristic algorithm for solving capacitated vehicle routing problem

Faiz, Alfian, Subiyanto, Subiyanto, Arief, Ulfah Mediaty

International Journal of Advances in Intelligent Informatics 2018: Articles in press 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

The aim of this work is to develop an enhanced Perturbation based Variable Neighborhood Search with Adaptive Selection Mechanism (PVNS_ASM) to solve the capacitated vehicle routing problem (CVRP). This approach combined Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on success rate of each perturbation scheme along the search. The ASM helped algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with existing heuristics in the literature has been performed on 21 CVRP benchmarks. The computational results proof that the developed method is competitive and very efficient in achieving high quality solution within reasonable computation time.

Hybrid SSA-TSR-ARIMA for water demand

Suhartono, Suhartono, Isnawati, Salafiyah, Salehah, Novi Ajeng, Prastyo, Dedy Dwi, Kuswanto, Heri, Lee, Muhammad Hisyam

International Journal of Advances in Intelligent Informatics 2018: Articles in press 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data, i.e. for forecasting monthly water demand at Wonogiri regency in Central Java Province, Indonesia. The data were observed from January 2006 to August 2017. The results of simulation study showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods.

Fusion noise-removal technique with modified dark-contrast algorithm for robust segmentation of acute leukemia cell images

Harun, Nor Hazlyna, Abu Bakar, Juhaida, Hambali, Hamirulaini’, Khair, Nurnadia M, Mashor, M.Y., Hassan, R.

International Journal of Advances in Intelligent Informatics 2018: Articles in press 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Segmentation is the major area of interest in the field of image processing stage. In an automatic diagnosis of acute leukemia disease, the crucial process is to achieve the accurate segmentation of acute leukemia blood image. Generally, there are three requirements of image segmentation for medical purposes, namely; accuracy, robustness and effectiveness which have received considerable critical attention. As such, we propose a new (modified) dark contrast enhancement technique to enhance and automatically segment the acute leukemic cells. Subsequently, we used a fusion 7 × 7 median filter as well as the seeded region growing area extraction (SRGAE) algorithm to minimise the salt-and-pepper noise, apart from preserving the post-segmentation edge. As per the outcomes, the accuracy, sensitivity, and specificity of this method were 91.02%, 83.68%, and 91.57% respectively.

Multiscale tsallis entropy for pulmonary crackle detection

Rizal, Achmad, Hidayat, Risanuri, Nugroho, Hanung Adi

International Journal of Advances in Intelligent Informatics 2018: Articles in press 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Abnormalities in the lungs can be detected from the sound produced by the lungs. Diseases that occur in the lungs or respiratory tract can produce a distinctive lung sound. One of the examples of the lung sound is the pulmonary crackle caused by pneumonia or chronic bronchitis. Various digital signal processing techniques are developed to detect pulmonary crackle sound automatically, such as the measurement of signal complexity using Tsallis entropy (TE). In this study, TE measurements were performed through several orders on the multiscale pulmonary crackle signal. The pulmonary crackle signal was decomposed using the coarse-grained procedure since the lung sound as the biological signal had a multiscale property. In this paper, we used 21 pulmonary crackle sound and 22 normal lung sound for experiment. The results showed that the second order TE on the scale of 1-15 had the highest accuracy of 97.67%. This result was better compared to the use of multi-order TE from the previous study, which resulted in an accuracy of 95.35%.

Constraint-based discriminative dimension selection for high-dimensional stream clustering

Waiyamai, Kitsana, Kangkachit, Thanapat

International Journal of Advances in Intelligent Informatics 2018: Articles in press 2018
Publisher : Universitas Ahmad Dahlan

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Abstract

Clustering data streams is one of active research topic in data mining. However, runtime of the existing stream clustering algorithms increases and their performance drop in the face of large number of dimensions. Complexity of the stream clustering methods is increased when perform on data with large number of dimensions. In order to reduce the clustering complexity, one possible solution consists in determining the appropriate subset of cluster dimensions via dimension projection. SED-Stream is an efficient clustering algorithm that supports high dimension data streams. The aim of this paper is to increase performance of SED-Stream in terms of both clustering quality and execution-time. In order to improve the clustering process, background or domain expert knowledge are integrated as “constraints” in SEDC-Stream. The new algorithm, SEDC-Stream, supports the evolving characteristics of the dynamic constraints which are activation, fading, outdating and prioritization. SEDC-Stream algorithm is able to reduce cluster splitting time, and place new incoming points to their suitable clusters. Compared to SED-Stream on the three real-world streams datasets, SEDC-Stream is able to generate a better clustering performance in terms of both purity and f-measure.

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 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

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, 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

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. 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

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 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.