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Journal of Information Technology and Computer Science

jitecs
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Published by Universitas Brawijaya

ISSN : 25409433 EISSN : 25409824 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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.

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Articles 10 Documents

Search results for , issue "Vol. 5 No. 3: Desember 2020" : 10 Documents clear

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting Yulianto, Fendy; Mahmudy, Wayan Firdaus; Soebroto, Arief Andy
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java Hamdianah, Andi; Mahmudy, Wayan Firdaus; Widaryanto, Eko
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

The Design of Traceability Information System of Smart Packaging-Based Product Supply Chain to Improve A Competitiveness of Apple Processed Agro-Industry Amalia, Faizatul; Kurniawan, Miftakhurrizal; Setiawan, Danang Triagus
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Agricultural food products are various. Apple is one of the agricultural product which is popular in Malang. There are many processed products from apple. However, there is a problem of food security concerning on the agricultural processed products. The food security consists of the information of nutrition contained in it, expired period, and the supply of healthy food. Therefore, it is required a traceability system that gives a guarantee about product authenticity and entrusted information about the food products. Lack of good information and infrastructure will hamper the formation of an effective traceability system that has not been considered even considered to require high costs, especially for some Small and Medium Enterprises (SME) producers. In general, producers and consumers need an information system that can provide food information effectively and efficiently. In general, producers and consumers need an information system that can provide food information effectively and efficiently. The design concept uses object oriented methods using United Modeling Language (UML), which consists of: Use Case Diagrams, Sequence diagrams and Class Diagrams. The design results were tested using Requirement Traceability Matrix (RTM) and the value of Response for a Class (RFC). Based on this test, it is produced that the RTM can be traced to all artifacts that have been made and the average RFC value is 5.17 meaning the RFC value is between 1 to 69, so that the RFC between 1 to 69 then the coupling is adaptable

RESTful API Implementation in Making a Master Data Planogram Using the Flask Framework (Case Study: PT Sumber Alfaria Trijaya, Tbk) Susanti, Era; Mailoa, Evangs
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

One of developingÂ retail company and is one of the biggest retail companies in Indonesia, namely Alfamart which is owned by PT. Sumber Alfaria Trijaya, Tbk. Alfamart must have the best marketing strategy and increase innovation for the satisfaction of customers in order to survive in high business competition. One strategy to improve marketing is the arrangement of product displays in stores known as planograms. Planogram is a concept that is used in planning the arrangement and placement of products according to certain categories based on consumer spending habits that aim to increase sales at retail. This research was conducted to create a web-based planogram master application using the Flask framework with the python programming language. The method used in this study is the RESTful API, which is the implementation of web services that work through HTTP links. This research produces a web-based master data application that can be used by users in entering data needed in making a planogram.Keywords: RESTful API, Python Flask, Planogram

Analysis in the Strategic Formula for Business and Information Technology Alignment of the Research and Development Planning Institution in Batu City Musthafawi, Aulia Zahra; Aknuranda, Ismiarta; Ramdani, Fatwa
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

The Batu City Area Research and Development Development Planning Institution (Badan Perencanaan Pembangunan Penelitian dan Pengambangan Daerah-Bappelitbangda) is one of the important regional apparatus organizations for the Batu city government. However, in carrying out its performance, Bappelitbangda still faces difficulties in several cases in monitoring the vision and mission achievement, for instance; the reporting that is often not in accordance with the results achieved and not evaluating the achievement of indicators in the regional apparatus organizations that do not reach the target. The difficulty is due to the lack of technology's role in supporting Bappelitbangda's performance so that the objectives to be achieved are not maximal. To resolve this, it is necessary to have strategic planning of the information system that can help Bappelitbangda achieves the goals. The method proposed in this study is by adapting the strategic planning method that starts by analyzing both the internal and external environment in terms of business or term of Information Systems (IS)/Information Technology (IT) and also the SWOT analysis. The results of this study could be the proposals for business IS strategies, IT strategies and IS/IT management strategies. The objectives as well as the new strategic formulas analysis based on SPBE which are expected to help Bappelitbangda harmonize the technology utilization.

Utilizing Indonesian Universal Language Model Fine-tuning for Text Classification Bunyamin, Hendra
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Inductive transfer learning technique has made a huge impact on the computer vision field. Particularly, computer visionÂ applications including object detection, classification, and segmentation, are rarely trained from scratch; instead, they are fine-tuned from pretrained models, which are products of learning from huge datasets. In contrast to computer vision, state-of-the-art natural language processing models are still generally trained from the ground up. Accordingly, this research attempts to investigate an adoption of the transfer learning technique for natural language processing. Specifically, we utilize a transfer learning technique called Universal Language Model Fine-tuning (ULMFiT) for doing an Indonesian news text classification task. The dataset for constructing the language model is collected from several news providers from January to December 2017 whereas the dataset employed for text classification task comes from news articles provided by the Agency for the Assessment and Application of Technology (BPPT). To examine the impact of ULMFiT, we provide a baseline that is a vanilla neural network with two hidden layers. Although the performance of ULMFiT on validation set is lower than the one of our baseline, we find that the benefits of ULMFiT for the classification task significantly reduce the overfitting, that is the difference between train and validation accuracies from 4% to nearly zero.

Automated Features Extraction from Software Requirements Specification (SRS) Documents as The Basis of Software Product Line (SPL) Engineering Haris, M Syauqi; Kurniawan, Tri Astoto; Ramdani, Fatwa
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Extractive Software Product Line Engineering (SPLE) puts features on the foremost aspect in domain analysis that needs to be extracted from the existing system's artifact. Feature in SPLE, which is closely related to system functionality, has been previously studied to be extracted from source code, models, and various text documents that exist along the software development process. Source code, with its concise and normative standard, has become the most focus target for feature extraction source on many kinds of research. However, in the software engineering principle, the Software Requirements Specification (SRS) document is the basis or main reference for system functionality conformance. Meanwhile, previous researches of feature extraction from text document are conducted on a list of functional requirement sentences that have been previously prepared, not literally SRS as a whole document. So, this research proposes direct processing on the SRS document that uses requirement boilerplates for requirement sentence statement. The proposed method uses Natural Language Processing (NLP) approach on the SRS document. Sequence Part-of-Speech (POS) tagging technique is used for automatic requirement sentence identification and extraction. The features are acquired afterward from extracted requirement sentences automatically using the word dependency parsing technique. Besides, mostly the previous researches about feature extraction were using non-public available SRS document that remains classified or not accessible, so this work uses selected SRS from publicly available SRS dataset to add reproducible research value. This research proves that requirement sentence extraction directly from the SRS document is viable with precision value from 64% to 100% and recall value from 64% to 89%. While features extraction from extracted requirement sentences has success rate from 65% to 88%.

Promoting Interest in Learning YorÃ¹bÃ¡ Language Using Mobile Game Oladimeji, Oladosu; Olorunfemi, Temitope; Oladimeji, Olayanju
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

This paper describes acute areas in which technology plays a role in language and culture revitalization. It was discovered that in order for people to learn a new language, they must express interest in that language. This work presents a new way of arousing the interest of people in learning YorÃ¹bÃ¡ language through the use of mobile game thereby promoting and revitalizing YorÃ¹bÃ¡ language and culture. The mobile application was evaluated using questionnaire to selected participants who have the mobile game developed installed on their phones and explored the application, and then rated based on some criteria such as extensibility, ease of use and user interest in learning YorÃ¹bÃ¡ Language after playing the game. The results showed that 76% of respondents rated the game ease of use as above average, 70% and 90% of the respondents rated the extensibility of the game and interest in learning YorÃ¹bÃ¡ after playing game above average respectively. This technology-based application will serve as an interesting and fun-filled approach of getting people to express interest in learning native indigenous language individually and as a group.

Development of Big Data App for Classification based on Map Reduce of Naive Bayes with or without Web and Mobile Interface by RESTful API Using Hadoop and Spark Cholissodin, Imam; Seruni, Diajeng Sekar; Zulqornain, Junda Alfiah; Hanafi, Audi Nuermey; Ghofur, Afwan; Alexander, Mikhael; Hasan, Muhammad Ismail
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Big Data App is a developed framework that we made based on our previous project research and we have uploaded it on github, which is developing lightweight serverless both on Windows and Linux OS with the term of EdUBig as Open Source Hadoop Distribution. In this study, the focus is on solving problems related to difficulties in building a frontend and backend model of a Big Data application which by default only runs scripts through consoles in the terminal. This will be quite a tribulation for the end users when the Big Data application has been released and mass produced to general users (end users) and at the same time how the end users test the performance of the Map Reduce Naive Bayes algorithm used in several datasets. In accordance to these problems, we created the Big Data App framework to make the end users, especially developers, feel easier to build a Big Data application by integrating the frontend using the Web App from Django framework and Mobile App Native, while for the backend, we use Django framework that is able to communicate directly with the script either hadoop batch, streaming processing or spark streaming very easily and also to use the script for pig, hive, web hdfs, sqoop, oozie, etc. the making of which is extremely fast with reliable results. Based on the test results, a very significant result in the ease of data computation processing by the end users and the final results showing the highest classification accuracy of 88.3576% was obtained.Keywords: big data, map reduce of naive bayes, serverless, web and mobile app, restful api, django framework

Mobile Application Architecture Restructuring with Microservice Approach Nugraha, Ardiono Roma; Talita, Aini Suri
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Microservice is an architecture that can solve many problems in a monolithic architecture. One of the problems is the ability to handle many concurrent users. The existing monolithic application can be restructured into microservices to increase robustness in handling a lot of users, without exception native mobile application. This study aimed to restructure the existing native mobile application named TemanBisnis into microservices. The restructuring process can be done by splitting the application features according to its business domain into one service. Two microservice architecture designs were proposed in this study, named 3-1 architecture and 2-1-1 architecture. Both architectures can handle up to 100 concurrent users, although they start to produce errors. By performance, the 3-1 architecture is better than the 2-1-1 architecture. In the end, an existing native mobile application can be restructured into microservices. The 3-1 architecture should be adopted to achieve the best results between these two architectures.

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