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
12 Documents
Search results for
, issue
"Vol. 5 No. 1: April 2020"
:
12 Documents
clear
<![CDATA[Inflation Rate Prediction in Indonesia using Optimized Support Vector Regression Model ]]>
<![CDATA[Oktanisa, Irvi]]>;
<![CDATA[Mahmudy, Wayan Firdaus]]>;
<![CDATA[Maski, Ghozali]]>
<![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 (1184.108 KB)
|
DOI: 10.25126/jitecs.202051173
<![CDATA[Inflation is a indicator which illustrated the economics condition of a country. This moneter phenomenom is signed with the increase of price in entire case. It can cause an effect for political sector which impact to economic stability in a nation. The importance of inflation control is very important due to the high and unstable of inflation will cause negative impact to economic and social in society. One of the solutions to control the inflation rate is predicting the inflation rate. This research using SVR as machine learning that is being optimized by GA as evolutionary agorithm as predicting method. SVR can solve nonlinear regression problems to linear regression using Kernel function that easy to implement. But, in SVR there is no general rule to set the parameters of SVR. Therefore, this research proposed to use GA to optimize the parameters of SVR. GA can solve the optimization problems in various research of economics prediction problem. Based on the testing that has been conducted, GA-SVR generate the MSE value is 0.03767, lower than SVR basic method is 0.053158. It proves that GA-SVR method can be utilized for predicting.]]>
<![CDATA[Developing Actor-Based Middleware as Collector System for Sensor Data in Internet of Things (IoT) ]]>
<![CDATA[Trisnawan, Primantara Hari]]>;
<![CDATA[Bakhtiar, Fariz Andri]]>;
<![CDATA[Pramukantoro, Eko Sakti]]>
<![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 (3329.019 KB)
|
DOI: 10.25126/jitecs.202051101
<![CDATA[The use of Internet of Things (IoT) plays an important role in supporting wireless communication for middleware in collecting data sensors. An actor-based middleware is designed to bridge protocol differences between cloud and sensor nodes. This middleware also acts as an initiator in accessing data from several sensor nodes, and then sending data that has been collected to the cloud. Incorporating the differences of communication protocols and data formats between sensor nodes and cloud is the responsibility of middleware. This Middleware acts as an actor by acting proactively accessing data from each sensor node, so that it can facilitate the completion of sending data from the sensor node to the middleware by avoiding from "signal collisions†among sensor nodes. After the data is collected in the middleware, the data is sent to the cloud using the Websocket or HTTP protocol above the TCP / IP protocol. The performance of the system is evaluated based on the success of the middleware bridging communication between sensor nodes and the cloud, as well as the readability of IoT data sensors that have been adjusted by cloud. The test results show that built-in middleware can bridge protocols between cloud and sensor nodes. In addition, the Websocket usage protocol produces a lower delay value than the MQTT and CoAP protocols.]]>
<![CDATA[Nearest Centroid Classifier with Outlier Removal for Classification ]]>
<![CDATA[Bawono, Aditya Hari]]>;
<![CDATA[Bahtiar, Fitra Abdurrahman]]>;
<![CDATA[Supianto, Ahmad Afif]]>
<![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 (1055.247 KB)
|
DOI: 10.25126/jitecs.202051162
<![CDATA[Classification method is misled by outlier. However, there are few research of classification with outlier removal, especially for Nearest Centroid Classifier Method. The proposed methodology consists of two stages. First, preprocess the data with outlier removal, removes points which are far from the corresponding centroid. Second, classify the outlier removed data. The experiment covers six data sets which have different characteristic. The results indicate that outlier removal as preprocessing method provide better result for improving Nearest Centroid Classifier performance on most data set.]]>
<![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[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[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[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]]>
<![CDATA[Cloud-based Middleware for Syntactical Interoperability in Internet of Things ]]>
<![CDATA[Pramukantoro, Eko Sakti]]>;
<![CDATA[Bakhtiar, Fariz Andri]]>
<![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 (1552.039 KB)
|
DOI: 10.25126/jitecs.202051148
<![CDATA[Heterogeneity of protocol communications, data formats, data structure, and hardware specifications on the Internet of things can lead to an Interoperability problem. The solution provides middleware that capable to work in heterogeneity communications, data formats, etc. This paper proposed. A cloud-based middleware that provides a communication interface to receive data from sensor nodes based on Restful and CoAP. Received data then stored in heterogenous IoT data storage based on the NoSQL database. From experiment and testing, interoperability testing methodology was used. The result shows proposed middleware can receive data from both protocols. The received data could store based on structure data or unstructured data on IoT data storage.]]>
