Journal of Information Technology and Computer Science
The Journal of Information Technology and Computer Science (JITeCS) is a peer-reviewed open access journal published by Faculty of Computer Science, Universitas Brawijaya (UB), Indonesia. The journal is an archival journal serving the scientist and engineer involved in all aspects of information technology, computer science, computer engineering, information systems, software engineering and education of information technology. JITeCS publishes original research findings and high quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementations and applications.
Articles
245 Documents
<![CDATA[Cover and Table of Contens ]]>
<![CDATA[Purbosari, Lina]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (4216.735 KB)
|
DOI: 10.25126/jitecs.202051187
<![CDATA[Cover and Table of Contens]]>
<![CDATA[Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry ]]>
<![CDATA[Verano, Dwi Asa]]>;
<![CDATA[Husnawati, Husnawati]]>;
<![CDATA[Ermatita, Ermatita]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (3173.78 KB)
|
DOI: 10.25126/jitecs.202051117
<![CDATA[The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.]]>
<![CDATA[Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images ]]>
<![CDATA[Oktaria, Anindita Safna]]>;
<![CDATA[Prakasa, Esa]]>;
<![CDATA[Suhartono, Efri]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1569.891 KB)
|
DOI: 10.25126/jitecs.201943155
<![CDATA[Indonesia is a country that is very rich in tree species that grow in forests. Wood growth in Indonesia consists of around 4000 species that have different names and characteristics. These differences can determine the quality and exact use of each type of wood. The procedure of standard identification is currently still carried out through visual observation by the wood anatomist. The wood identification process is very in need of the availability of wood anatomists, with a limited amount of wood anatomist will affect the result and the length of time to make an identification. This thesis uses an identification system that can classify wood based on species names with a macroscopic image of wood and the implementation of the Convolutional Neural Network (CNN) method as a classification algorithm. Supporting architecture used is AlexNet, ResNet, and GoogLeNet. Architecture is then compared to a simple CNN architecture that is made namely Kayu30Net. Kayu30Net architecture has a precision performance value reaching 84.6%, recall 83.9%, F1 score 83.1% and an accuracy of 71.6%. In the wood species classification system using CNN, it is obtained that AlexNet as the best architecture that refers to a precision value of 98.4%, recall 98.4%, F1 score 98.3% and an accuracy of 96.7%.]]>
<![CDATA[An Exploratory Study of Requirements Engineering Practices in Indonesia – Part 2: Efforts, Processes and Techniques ]]>
<![CDATA[Kurniawan, Tri Astoto]]>;
<![CDATA[Rusdianto, Denny S.]]>;
<![CDATA[Brata, Adam H.]]>;
<![CDATA[Amalia, Faizatul]]>;
<![CDATA[Santoso, Angga]]>;
<![CDATA[Raharjo, Dini I. N. R. P.]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (2679.256 KB)
|
DOI: 10.25126/jitecs.202051164
<![CDATA[This paper provides the second part of statistical research findings of an exploratory study of the requirements engineering practices implemented in software development processes in Indonesia. This second part attempts to reveal facts regarding efforts, processes and techniques exist in such requirements engineering practices. Such facts were captured in accordance with the first part which were surveyed through a comprehensive online questionnaire consisting of both closed- and open-ended questions. We invited 158 participant candidates representing industry and higher education institutions, however, 31 of them joined our web-based survey. Results which respect to efforts, processes and techniques are presented along with related interpretations.]]>
<![CDATA[Cover and Table of Contens ]]>
<![CDATA[Purbosari, Lina]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (4393.646 KB)
|
DOI: 10.25126/jitecs.201943175
<![CDATA[Cover and Table of Contens]]>
<![CDATA[Classification Tuberculosis DNA using LDA-SVM ]]>
<![CDATA[Anshori, Mochammad]]>;
<![CDATA[Mahmudy, Wayan Firdaus]]>;
<![CDATA[Supianto, Ahmad Afif]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (2061.76 KB)
|
DOI: 10.25126/jitecs.201943113
<![CDATA[Tuberculosis is a disease caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is included in the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to other diffuse lungs. The challenge is how to better detect using DNA sequence data from mycobacterium tuberculosis. Therefore, preprocessing data is necessary. Preprocessing method is used for feature extraction, it is k-Mer which is then processed again with TF-IDF. The use of dimensional reduction is needed because the data is very large. The used method is LDA. The overall result of this study is the best k value is k = 4 based on the experiment. With performance evaluation accuracy = 0.927, precision = 0.930, recall = 0.927, F score = 0.924, and MCC = 0.875 which obtained from extraction using TF-IDF and dimension reduction using LDA.]]>
<![CDATA[Index and Back Cover ]]>
<![CDATA[Purbosari, Lina]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (5051.637 KB)
|
DOI: 10.25126/jitecs.202051188
<![CDATA[K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN ]]>
<![CDATA[Satria Bahari Johan, Ahmad Wali]]>;
<![CDATA[Utaminingrum, Fitri]]>;
<![CDATA[Budi, Agung Setia]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1299.817 KB)
|
DOI: 10.25126/jitecs.202051144
<![CDATA[This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair]]>
<![CDATA[Short Term Forecasting of Electricity Load: A Comparison of Methods to Paiton Subsystem East Java & Bali ]]>
<![CDATA[Ardilla, Yunita]]>;
<![CDATA[Suhartono, Suhartono]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (988.915 KB)
|
DOI: 10.25126/jitecs.201943159
<![CDATA[Electricity consumption in Indonesia is expected to continue to grow by average of 6,5% per year until 2020. Therefore, PT. PLN had to make an effective subsystem that can provide electrical energy based on customer needs. The electrical energy is converted from the mechanical energy and can’t be stored. Because of that reason, if the electrical energy isn’t channeled properly then PT. PLN will suffer losses. It is necessary to plan a proper distribution system of electrical energy. The aim of this research is to predict short-term electricity consumption for Paiton’s subsystem in East Java Indonesia by using ARIMA and Multilayer Perceptron. The best model is measured based on MAPE, SMAPE, and RMSE value in data sample. The result of the analysis shows that Multilayer Perceptron method provides better accuracy rate for electricity consumption forecasting in Paiton subsystem based on peak load compared to ARIMA]]>
<![CDATA[2D and 3D Geovisualization: Learning user Preferences in Landslide Vulnerability ]]>
<![CDATA[Wahyudi, Hafif Bustani]]>;
<![CDATA[Ramdani, Fatwa]]>;
<![CDATA[Bachtiar, Fitra Abdurrachman]]>
<![CDATA[ Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020 ]]>
Publisher : <![CDATA[Faculty of Computer Science (FILKOM) Brawijaya University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (2894.991 KB)
|
DOI: 10.25126/jitecs.202051167
<![CDATA[Landslides often cause impacts on the environment, infrastructure, and society. The impacts of landslides can be minimized by creating disaster awareness. Landslide vulnerability mapping can be used as a dissemination media to increase disaster awareness. The mapping methods that can be used are 2D and 3D geovisualization. There is very few research in current literature explaining the user preferences on geovisualization 2D and 3D related to landslide vulnerability. In this paper, the user preferences of both 2D and 3D geovisualization will be evaluated. This study will focus to find out which geovisualization suits most users and their literacy spatial among those provided geovisualizations. From our results, 90% of users prefer 3D geovisualization over 2D. Furthermore, our analysis shows that 2D geovisualization has the advantage of being easily understood by users in all ages. Meanwhile, 3D geovisualization is better at increasing users' spatial literacy at all ages and levels of education in knowing the causes of landslide vulnerability. Appropriate geovisualization will provide information and knowledge that is useful for communities in regards of landslide vulnerability for better disaster awareness]]>
