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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 255 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.
Multilingual Sentiment Analysis of RCTI+ Reviews Utilising Orange, ChatGPT, and Naïve Bayes Juita, Meilani Mega; Rahmi, Rahmi
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103787

Abstract

Low user ratings on mobile applications often reflect underlying dissatisfaction that is not immediately apparent through quantitative scores alone. To uncover the sentiment dynamics behind such evaluations, this study analyzes user reviews of the RCTI+ Superapp in both the original Indonesian and English-translated forms. Using the CRISP-DM framework, reviews were scraped from Google Play, normalized and translated via ChatGPT, and classified using Orange Data Mining with a Naïve Bayes algorithm. The analysis reveals that English-translated reviews yield sharper sentiment polarity and higher classification accuracy (100%) compared to the original Indonesian texts (99.1%), albeit with reduced lexical nuance. These findings suggest that generative AI-assisted translation enhances sentiment clarity in informal, low-resource language data, while potentially simplifying cultural or emotional expression. The study offers methodological insights for multilingual sentiment analysis and practical implications for app developers seeking to interpret user feedback more effectively across languages.
Prediction of On-Time Graduation of Students Using Random Forest Algorithm (Case Study: Faculty of Computer Science, Universitas Brawijaya) Ahnaf, Muhammad Farrel Reginado; Satrio Hadi Wijoyo; Nurul Hidayat
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103788

Abstract

The timeliness of student graduation is an important indicator of academic quality and institutional performance. Delayed graduation not only affects university evaluation metrics but also postpones students’ entry into the workforce. This study proposes a predictive model to identify students at risk of delayed graduation at the Faculty of Computer Science, Universitas Brawijaya. A comparative evaluation of three classification algorithms, namely Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), was conducted within a Knowledge Discovery in Databases (KDD) framework. SMOTE was applied to address class imbalance, while Stratified K-Fold Cross-Validation was used to ensure robust model assessment. Experimental results show that the Random Forest model achieves the best performance, with an accuracy of 73% and an AUC of 0.79, outperforming SVM and KNN. Feature importance analysis further indicates that Grade Point Average, particularly in the third semester, is a more influential predictor of on-time and delayed graduation than credit accumulation. These results demonstrate the potential of the proposed model as an early warning system for proactive academic intervention.
Root Cause Analysis of Government Readiness in Realizing the Concept of E-Government System: A Case Study of Palu City Government Permatasari, Ayu; Aknuranda, Ismiarta; Setiawan, Budi Darma
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103789

Abstract

This study aims to analyse the root causes of the readiness issues faced by the Palu City Government in implementing the concept of an E-Government System. The main focus of this research is to assess the government's readiness in realizing the implementation of E-Government System and to identify the root causes of the obstacles encountered in its application in Palu City. The study uses a qualitative approach, with data analysis conducted using the 3x5 Whys method and a Fishbone diagram. The findings indicate that the implementation of the E-Governemnt System in Palu City faces complex challenges, ranging from unaligned regulations, low human resource competencies, limited infrastructure, to suboptimal funding and an organizational culture that is not yet ready for change. Analysis based on academic theories confirms that the success of the E-Governemnt System implementation does not rely solely on technology but also on institutional readiness and human capacity. This research contributes recommendations that can be utilized by the Palu City Government to improve the quality of the E-Government services and achieve the goals of clean, effective, efficient, transparent, and accountable governance. In addition, this study emphasizes the importance of enhancing human resource capacity and managing change in order to optimize the implementation of the E-Government System in Palu City.
Prediction of Tides in the Gisik Cemandi Coastal Area Using the Support Vector Regression (SVR) Method Dewi Sukmawati, Chandra; Novitasari, Dian Candra Rini; Dewi, Ratna Cintya
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103790

Abstract

This study was conducted to predict tidal fluctuations in the coastal area of Gisik Cemandi Village, Sidoarjo, using the Support Vector Regression (SVR) method. The dataset consisted of  time series records of sea level height for the period of March . The prediction process was implemented by testing three SVR kernel types, namely linear, polynomial, and Gaussian Radial Basis Function (RBF), along with variations of the parameters Cost , Gamma , and epsilon . Based on the evaluation using Mean Absolute Percentage Error ( MAPE), the Linear kernel demonstrated the best predictive performance with the lowest MAPE value of  under a  train-test split. The prediction results with the Linear kernel closely matched the actual data, indicating the model’s accuracy and reliability in capturing the linear patterns of tidal data. This model can be utilized as a supporting tool for tidal prediction to aid coastal activities such as navigation and fisheries.
Integrated NTT-Karatsuba for fast multiplication of NTRU Algorithm Muhammad Fathan Rivaldi; Rohmat Gunawan; Irani Hoeronis
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103792

Abstract

The increasing threat of attacks from quantum computers requires the development of more efficient and secure post-quantum cryptographic algorithms, one of which is NTRU. The main challenge in this algorithm lies in the high complexity of large-dimensional polynomial multiplication operations and parameter sizes that affect system performance. This research implements the Hybridized Number Theoretic Transform and Karatsuba calculation methods with compressed parameters in the C programming language and integrates them into the NTRU algorithm. The evaluation was conducted by measuring the key generation, encryption, and decryption processing times, as well as analyzing the size of the public key, ciphertext, and bandwidth requirements before and after parameter compression. The experimental results show that this method is able to significantly reduce the modulus q value without compromising security, while increasing execution time efficiency. These findings prove that the hybrid NTT–Karatsuba method with compressed parameters supports the practical implementation of the NTRU algorithm in resource-constrained environments.

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