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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.

Arjuna Subject : -

Articles 245 Documents

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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

Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network Fajri, Diny Melsye Nurul; Mahmudy, Wayan Firdaus; Yulianti, Titiek
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.

Comparison of Bagging Ensemble Combination Rules for Imbalanced Text Sentiment Analysis Cahya, Reiza Adi; Bachtiar, Fitra A.; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, e.g. the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial NaÃ¯ve Bayes, Complement NaÃ¯ve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.

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%.

Event Recommendation System using Hybrid Method Based on Mobile Device Kudori, Dio Saputra
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system doesnâ€™t use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filteringwith sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. Itshows that the hybrid method can be utilized for developing event recommendation systems.

Decision Support System for Diagnose Hepatitis Type using Expert System Method Mulya, Dimmas; Pratiwi, Dian; Mardianto, Is
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

In the medical world, there are five types of Hepatitis, namely Hepatitis A, B, C, D, and E. However, the five types of hepatitis have similar symptoms, including yellowing of the skin color, yellowing of the eyeball, loss of appetite, etc. Thus, many of the Medical Personnel often misdiagnose the patient for the type of hepatitis or not suffer from hepatitis.Therefore, previous diagnostic data ware collected from Medical Specialists which will be processed and developed into the Java-based Decision Support System Application with Expert System method with the percentage output of the likelihood of patients from each type of hepatitis along with the possibility of patients not suffering from hepatitis.With the output of this application, the percentage of the possibility of each type of hepatitis or the possibility of not suffering from hepatitis can help Medical Personnel to make diagnostic decisions based on alternatives provided by the application.

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