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Imam Mukhlash
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Departemen Matematika, Gedung F Lantai II, Kampus ITS, Keputih, Sukolilo-Surabaya 60111 Jawa Timur, Indonesia Phone: +62 31-5943354 Email:ijcsam.matematika@its.ac.id
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International Journal of Computing Science and Applied Mathematics-IJCSAM
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 24775401     DOI : -
Core Subject : Education,
Mathematics
IJCSAM (International Journal of Computing Science and Applied Mathematics) is an open access journal publishing advanced results in the fields of computations, science and applied mathematics, as mentioned explicitly in the scope of the journal. The journal is geared towards dissemination of original research and practical contributions by both scientists and engineers, from both academia and industry. IJCSAM (International Journal of Computing Science and Applied Mathematics) is a journal published by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Arjuna Subject : Matematika - Matematika Terapan
Articles 137 Documents
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Second Refinement of Jacobi Iterative Method for Solving Linear System of Equations Tesfaye Kebede Eneyew; Gurju Awgichew; Eshetu Haile; Gashaye Dessalew Abie
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 5 No. 2 (2019)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

In this paper, the new method called second refinement of Jacobi (SRJ) method for solving linear system of equations is proposed. The method can be used to solve ODE and PDE problems where the problems are reduced to linear system of equations with coefficient matrices which are strictly diagonally dominant (SDD) or symmetric positive definite matrices (SPD) or M-matrices. In this case, our new method minimizes the number of iterations as well as spectral radius and increases rate of convergence. Few numerical examples are considered to show the efficiency of SRJ over Jacobi (J) and refinement of Jacobi (RJ) methods.
Texture-Based Woven Image Classification using Fuzzy C-Means Algorithm Soetrisno Soetrisno; Dwi Ratna Sulistyaningrum; Isi Bifawa’idati
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 8 No. 1 (2022)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

There are a lot of texture-based image data stored in the storage media Internet. Most of these data portray the cultural fabric texture results from a State. Because of the many variants of the existing texture, the data need to be easily accessible through the Internet. Moreover, the area of origin of weaving the surface is easily known. Therefore, it is necessary to develop a classification system based on woven image data. The texture of the image data stored in a database on the Internet can be grouped/clustered well, making it easy to access. This study examines a texture-based woven image classification using fuzzy c-means algorithm. This method combines extraction methods Gabor filter, fuzzy c-means algorithm and Euclid distance similarity measure. An experiment was done using the system as many as 60 woven images from Bali, NTT and Central Java areas, each taken as many as 25 images weaving. The test results stated that testing using the test images taken from the images in the database generates a 100% accuracy rate, and testing using test images taken from outside the database produces an accuracy rate of 94%.
Error Modeling Radar Rainfall Estimation Through Incorporating Rain Gauge Data Over Upper Blue Nile Basin, Ethiopia Megbar Wondie Birhan; U. Jaya Prakash Raju; Samuel T. Kenea
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 5 No. 2 (2019)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Accurate and precise measurements of rainfall from weather radar reflectivity data is essential to supplement the limited characterization of spatial and temporal measurements provided by insufficient network and density of rain gauges. While weather radar has high spatial and temporal resolution, it contaminated with various sources of errors due to the conversion of reflectivity to rain rate and the projectile rainfall motion. Error modeling improvement with the application of projectile rainfall motion correction is essential to improve the radar data. However, stile is not well documented for over the world as well as Ethiopia. Therefore, the aim of this study was to generate an error model for weather radar rainfall estimation by incorporating gauge rainfall data over upper Blue Nile basin, Ethiopia. Projectile rainfall motion correction is considered on the data of reflectivity and rain rate to determine empirical error model parameter values. The model parameter values are found, multiplicative factor (a) was 55, the exponent factor (b) was 1.12, standard deviation of proportional error was 0.08 and standard deviation of random error was 0.07. The value of the total error varied from -0.45 to 1.16 mm and the domain of proportional error was greater than random error. After applying the projectile rainfall motion correction, the total error is reduced by 12%. In general, the assumption of projectile method is quite useful for improving the radar data over upper Blue Nile basin in Ethiopia as well as over the world. Hence, we wish to extend this method for other regions.
Further Results on Ph-supermagic Trees Tita Khalis Maryati; Otong Suhyanto; Fawwaz Fakhrurrozi Hadiputra
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 9 No. 2 (2023)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Let $G$ be a simple, finite, and undirected graph. An $H$-supermagic labeling is a bijective map $f : V(G) \cup E(G) \to \{1,2,\cdots,|V(G)|+|E(G)|\}$ in which $f(V) = \{1,2,\cdots,|V(G)|\}$ and there exists an integer $m$ such that $w(H') = \sum_{v  \in V(H')} f(v) + \sum_{e \in E(H')} f(e) = m$, for every subgraph $H' \cong H$ in $G$. In this paper, we determine some classes of trees which have $P_h$-supermagic labeling.
Implementation of Convolutional Neural Networks for Batik Image Dataset Vina Ayumi; Ida Nurhaida; Handrie Noprisson
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 8 No. 1 (2022)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

One method of image recognition that can be used is a convolutional neural network (CNN). However, the training model of CNN is not an easy thing; it takes tuning parameters that take a long time in the training process. This research will do Batik pattern recognition by using CNN. From the experiment that we conducted, the result shows that the feature extraction, selection, and reduction give the accuracy more significant than raw image dataset. The feature selection and reduction also can improve the execution time. Parameters value that gave best accuracy are: epoch = 200, batch_size = 20, optimizer = adam, learning_rate = 0.01, network weight initialization = lecun_uniform, neuron activation function = linear.
Identification of Rainfall Intensity by Using Bahir Dar C-band Weather Radar products Abebe Kebede Habtegebreal; Abebaw Bizuneh Alemu
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 5 No. 2 (2019)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Observation of rainfall accumulation, distribution and wind direction spatially and temporally is very essential for meteorologists and hydrologists in the Upper Blue Nile basin and different locations of Ethiopia. The Bahir Dar (BAH) Cband weather radar used to estimate the intensity, amount and distribution of precipitation with a high resolution temporally and spatially. It is dual polarization with horizontal and vertical reflectivity; six variables are used to determine the hydrometer and non-hydrometer types, shape and sizes of targets up to range of 250 km and 450 km at constant altitude plan position indicator (CAPPI) and plan position indicator (PPI) products. From our first observation, we used different types of products to observe rainfall intensity, distribution, wind direction and warnings across Upper Blue Nile. For RainN on 01 July 2016 the rainfall distribution over twenty four hours (24 Hr) UTC at Bahir Dar, parts of lake Tana, most northern parts, western parts of Gondar town, and south western parts of lake Tana a resolution of 809 m/Pixel with frequency of 600 Hz the maximum rainfall accumulation 91.6 mm was achieved. By using CAPPI product with vertical height of 1 km on 26 June 2016 the maximum reflectivity 53.5 dBZ is observed.
Combined VGG-Long Short Term Memory with Gamma Correction for Pneumonia Type Classification based on Chest X-Rays Nia Amelia; Riskyana Dewi Intan Puspitasari; Hasanuddin Al-Habib; Elly Matul Imah
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 9 No. 2 (2023)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Pneumonia is the second disease with the most patients being treated in the emergency department. Pneumonia can be distinguished based on its severity into viral and bacterial pneumonia. The coronavirus (COVID-19) has become a pandemic that spread globally. Panic during the pandemic has caused many people to self-diagnose and acknowledge common pneumonia as COVID-19. Despite having almost similar symptoms, not all pneumonia is COVID-19. Pneumonia is an inflammation of the lungs caused by bacteria, viruses, or fungi. In contrast, pneumonia in COVID-19 is caused by the SARS-CoV-2 virus. Early diagnosis of COVID-19 and pneumonia is crucial to perform the optimal treatment. A chest x-ray is a common way to detect pneumonia and is recommended for COVID-19. This study proposes a Pneumonia classification including a COVID-19 system based on X-Rays images using VGG-long short-term memory (LSTM) on chest X-ray images. This study applied gamma correction image enhancement to the thorax X-ray image. In the proposed system, VGG is used for feature extraction, and LSTM is used as a classifier. The experimental results show that the proposed system got an accuracy of 96.88% compared to previous state-of-the-art methods for pneumonia classification
The Adomian Decomposition Method with Discretization for Second Order Initial Value Problems Dagnachew Mengstie Tefera; Awoke Andargie
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 5 No. 2 (2019)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

In this paper, Adomian Decomposition Method with Discretization (ADMD) is applied to solve both linear and nonlinear initial value problems (IVP). Comparison with Adomian Decomposition Method (ADM) is presented. To illustrate the efficiency and accuracy of the method, five examples are considered. The result shows that ADMD is more efficient and accurate than ADM.
Evaluating the Fitting Performance of AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) Models Didit Budi Nugroho; Veny M. Ningtyas; Hanna A. Parhusip
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 9 No. 2 (2023)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

This study compares the performance of the GARCH(1,1), AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) models fitted to real data. The observed real data are the USD exchange rate against IDR in the daily period from January 2010 to December 2017. To identify the superiority and evaluate the performance of those models in capturing the heavy-tailed and skewed character in exchange rate distribution, the return error is assumed to be the Normal, Skew Normal (SN), Skew Curved Normal (SCN), and Student-t distributions. The model's parameters are estimated using the GRG Non-Linear method in Excel Solver and the ARWM method in the MCMC scheme implemented in the Scilab program. Estimation results using Excel's Solver have similar values to the estimates obtained using MCMC, concluding that Excel's Solver has a good ability in estimating the model's parameters. Based on AIC values, this study concludes that the NAGARCH(1,1) model under Student-t distribution performs the best.
Modeling Portfolio Based on Linear Programming for Bank Business Development Project Plan Shanti Wulansari; Mauridhi Hery Purnomo
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 8 No. 1 (2022)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

The bank’s business processes target business plans for the next year. Existing conditions, the business plan is based on the growth asset portfolio every year, so that the purchase of productive assets awaits issuers’ offers. This condition will cause a portfolio not to be measured and the inaccuracy of portfolio selection. Asset Liability Management (ALM) is the management of the structure of assets and liabilities to achieve profit. Banking books and trading books are bank portfolios to earn income. In selecting each portfolio, it contains liquidity risk, market risk and, credit risk. The level of profit is reflected in returns, while returns and risks are a trade-off so that calculations require mathematical and simulation models. Each bank needs an overview of the composition of productive assets, as short-term, medium-term and, long-term assets must be measured risk and target achievement. Linear programming method will allocate productive assets as the bank’s leading source of income, to achieve optimization of profit on the risks received. The problem with this research is that there are 830 variables as banking assets and 19 constraints as indicators of risk. In the seventh iteration of mathematical models, return 1,803 Trillyun from 11 banking book assets.

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