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INDONESIA
Journal of Information Technology and Computer Science
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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Gold Prices Prediction using Univariate Long Short Term Memory Method Aditama, Gustian; Yudistira, Novanto; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102525

Abstract

Gold is one of the precious metals that serves various purposes beyond being a jewelry material. When it comes to gold, it is often associated with the economy. Before the existence of currency, humans used gold as the base material for coins as a medium of exchange. Currently, one of the commonly utilized functions of gold is as an investment asset. Due to its utility and high demand, the price of gold can fluctuate over time. This research aims to predict the price of gold using the Long Short Term Memory (LSTM) method. LSTM is a deep learning technique that performs well when applied to time series data. The performance of LSTM can be assessed using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Thus, this study proposes the prediction of gold prices using LSTM with an optimized architecture. In order to achieve it, testing is conducted based on sequence length and hidden size. The best results were achieved using Univariate LSTM with a sequence length of 25 and a hidden size of 150, that produce RMSE of 22.014 and MAPE of 1.133%.
The Transforming Requirement Sentences in The Form of Requirement Boilerplates to Ontology Using Natural Language Processing: Transforming Requirement Sentences in The Form of Requirement Boilerplates to Ontology Using Natural Language Processing Ismail, Muhammad Farabi; Kurniawan, Tri Astoto; Akbar, Sabriansyah Rizqika
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102535

Abstract

Requirements sentences in the software development process should be consistent. If there is inconsistency, there must be a conflict between requirements in a software project. We propose converting a requirement sentence into an ontology to check such inconsistency. Our approach separates each noun and verb in a requirements sentence using Natural Language Processing (NLP) technique to find concepts (nouns) and relations (verbs) between words. Our proposed ontology structure can be dynamically updated based on changes in the requirements sentences. We validated such an approach using precision and recall testing based on four requirements sentences with three times changes on four requirements sentences. The accuracy results are 100%. It means our approach can accommodate any changes in the requirements.
Application of YOLOv5 and EasyOCR for Odd-Even Detection and Recognition on License Plates Candra, Alvin; Utaminingrum, Fitri; Tolle, Herman
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102553

Abstract

The increase in the number of vehicles in Indonesia is directly correlated with the escalation in traffic congestion. In addition to congestion, an indirect repercussion of vehicle proliferation is the rise in traffic accidents. The government has undertaken various measures to alleviate traffic congestion, one of which is the implementation of the odd-even system. Automatic License Plate Recognition (ALPR) can be employed to facilitate the identification of odd-even plate numbers. In this study, ALPR employs machine learning methods (K-Nearest Neighbors and Support Vector Machine) as well as deep learning techniques (YOLOv5). EasyOCR is utilized for character recognition on the license plates. Based on the test results, YOLOv5 emerges as the optimal approach for license plate detection. EasyOCR demonstrates proficient character recognition for license plates at distances less than 2 meters.
Automated Cloud Migration System for Permissioned Blockchain Infrastructure Annisa, Faradiba; Bhawiyuga, Adhitya; Akbar, Sabriansyah Rizqika; Shaffan, Nur Hazbiy; Kartikasari, Dany Primanita
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102602

Abstract

Blockchain is a technology that stores data in a distributed manner. There are two types of blockchains: permissionless and permissioned. In permissionless blockchains, nodes are operated by anonymous participants who do not know each other. Meanwhile, permissioned blockchain nodes run on a private infrastructure owned by the organizations participating in the blockchain network. This infrastructure may need to be migrated for various reasons, either from on-premises to the cloud or between clouds. Therefore, in this research, a migration system for permissioned blockchain infrastructure is developed. This migration system operates automatically to reduce human errors, inconsistencies, and time inefficiencies. To achieve automation, Infrastructure as Code (IaC) and automation tools are used. The IaC tool is used to automate infrastructure provisioning on the target cloud platform, while the automation tool is used to configure and deploy the blockchain on virtual machines in the target cloud. The chosen cloud platform is a public cloud. The experiment on the automated migration system focuses on two aspects. The first aspect evaluates the system's capability to perform infrastructure provisioning, blockchain configuration, and blockchain deployment on the target cloud platform. The second aspect assesses the migrated blockchain's functionality compared to the source infrastructure. The experimental results demonstrate that the automated migration system can successfully provision infrastructure, configure, and deploy the blockchain on virtual machines in the target cloud. Furthermore, the results confirm that the blockchain on the target infrastructure can add new data and access previously generated data within the blockchain.
Development Of E-Module In Subject Basic Computer Graphics In Class X – DKV SMK NEGERI 11 Malang To Increase Student Interest In Learning Faradillah Aditya Purnomo; Faizatul Amalia; Satrio Hadi Wijoyo
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102605

Abstract

Learning can run well with an interest in learning for students. Based on this, researchers found problems at SMK NEGERI 11 MALANG. One of the problems encountered was the low interest in learning for class X – DKV students in the Basic Computer Graphics subject. Therefore, the Basic Computer Graphic E-module is here to provide a solution to this problem. The development of this E- module uses the ADDIE method which consists of five stages (analyze, design, development, implementation and evaluation). Data collection was carried out using structured interview techniques, literature studies and filling out questionnaires (pre-E-module and post-E-module). Tests carried out include testing the feasibility of the E-module and testing the increase in student learning interest. Questionnaire calculations were carried out using a Likert scale, with a scale of 1-4. Based on the tests carried out by the E-module, the results of the eligibility percentage were 70% (Eligible) by material experts, 79.16% (Decent) by media experts, 88.3% (Very Feasible) by media eligibility experts and 84.19% (Very Worthy) by users. Furthermore, the percentage of students' interest in learning obtained an increase of 66.65% with a percentage of interest in learning before the E-module of 12.7% (Very Poor) and after using the E-module of 79.35% (Good). So it can be concluded that the E-module can be considered feasible and able to increase student learning interest.

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