Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Articles
962 Documents
<![CDATA[Identification and Classification of Cyber Attacks on ELDIRU UNSOED using Random Forest Algorithm ]]>
<![CDATA[Caesario, Justicio]]>;
<![CDATA[Nofiyati]]>;
<![CDATA[Wibowo, Dwi Kurnia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5239
<![CDATA[Academic information systems, such as Eldiru Unsoed, function as vital digital assets vulnerable to cyberattacks, while conventional rule-based Web Application Firewalls exhibit detection weaknesses. Empirical testing in this study shows that the standard ModSecurity with Core Rule Set (CRS) system achieves a recall of only 5.34%, meaning it fails to identify the majority of actual attacks and creates a significant security gap. To address this problem, this research designs a detection system based on the Random Forest algorithm using Nginx server log data, validated with the public CSIC 2010 dataset. The model was developed by engineering hybrid features that include lexical analysis, CRS rule context, and N-grams to classify web traffic. Evaluation results show the proposed Machine Learning-Random Forest (ML-RF) model successfully increases recall from 5.34% to 72.00% and the F1-Score from 10.10% to 80.00%. This improvement in metrics, while maintaining a precision of 91.00%, proves that machine learning integration yields a more balanced and reliable cybersecurity defense mechanism. This research underscores the importance of implementing MLOps workflows for continuous model calibration and retraining to maintain detection effectiveness against evolving threats.]]>
<![CDATA[Performance Optimization of Support Vector Machine with SMOTE for Multiclass Stunting Prediction in Sumedang District, Indonesia ]]>
<![CDATA[Fadil, Irfan]]>;
<![CDATA[Surya Manggala, Ramdani]]>;
<![CDATA[Firmansyah, Esa]]>;
<![CDATA[Helmiawan, Muhammad Agreindra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4843
<![CDATA[The percentage of stunting toddlers in Sumedang Regency is the highest compared to other nutritional problems. Stunting imposes a significant risk to the future quality of human resources. This study explores the performance of the Support Vector Machine (SVM) algorithm in predicting the stunting status of toddlers in Tanjungmedar Subdistrict, the region with the highest incidence of stunting cases in Sumedang Regency in 2020. The testing uses RapidMiner software and applies the Synthetic Minority Oversampling Technique (SMOTE) to overcome the imbalanced dataset so that the resulting performance can be optimized. Accuracy, precision, recall, and F1-score are measured in performance evaluation using a confusion matrix. The findings demonstrate that SMOTE might adjust the distribution of target classes in the dataset to maximize the SVM algorithm's performance. At the start of the test, the SVM model produced an accuracy of 85.10%. After applying SMOTE, the accuracy of the SVM model increased to 89.08%. The F1-score also increased for each class, except for the Normal class, which decreased slightly. These results demonstrate the suitability of SVM combined with SMOTE for health-related multiclass classification tasks, especially in imbalanced public health datasets, contributing to the advancement of applied machine learning in healthcare informatics.]]>
<![CDATA[Integration of BERT-VAD, MFCC-Delta, and VGG16 in Transformer-Based Fusion Architecture for Multimodal Emotion Classification ]]>
<![CDATA[Nayoma, Fisan Syafa]]>;
<![CDATA[Kusnawi, Kusnawi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4915
<![CDATA[Emotion is a condition that plays an important role in human interaction and is the main focus of intelligence research in utilizing multimodal. Previous studies have classified multimodal emotions but are still less than optimal because they do not consider the complexity of human emotions as a whole. Although using multimodal data, the selection of feature extraction and the merging process are still less relevant to improving accuracy. This study attempts to categorize emotions and improve precision through a multimodal methodology that utilizes Transformer-based Fusion. The data used consists of a synthesis of three modalities: text (extracted through BERT and assessed through the affective dimensions of NRC Valence, Arousal, and Dominance), audio (extracted through MFCC and delta-delta2 from the RAVDESS and TESS datasets), and images (extracted through VGG16 on the FER-2013 dataset). The model is built by mapping each feature into an identical dimensional representation and processed through a Transformer block to simulate the interaction between modalities, known as feature-level interactions. The classification procedure is run through a dense layer with softmax activation. Model evaluation was performed using Stratified K-Fold Cross Validation with k=10. The evaluation results showed that the model achieved 95% accuracy in the ninth fold. This result shows a significant improvement from previous research at the feature level (73.55%), and underlines the effectiveness of the combination of feature extraction and Transformer-based Fusion. This study contributes to the field of emotion-aware systems in informatics, facilitating more adaptive, empathetic, and intelligent interactions between humans and computers in practical applications.]]>
<![CDATA[VGG16-Based Feature Extraction for Arabic Alphabet Sign Language Classification to Support Qur'anic Tadarus Accessibility ]]>
<![CDATA[Rakhmadi, Aris]]>;
<![CDATA[Yudhana, Anton]]>;
<![CDATA[Sunardi, Sunardi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4953
<![CDATA[This study addresses the limited availability of automated recognition systems for Arabic Alphabet Sign Language (ArSL), particularly in facilitating Qur’anic Tadarus for the deaf and hard-of-hearing community. While research on American and Indonesian sign languages has advanced significantly, ArSL studies, especially for static alphabet gestures, remain underrepresented. The aim of this research is to develop an accurate and efficient ArSL classifier using the VGG16 convolutional neural network with transfer learning. The study employs the publicly available RGB Arabic Alphabets Sign Language Dataset, comprising 7,856 annotated images across 31 Hijaiyah letters, collected under varied backgrounds and lighting conditions. The proposed model integrates pretrained ImageNet weights with a customized classification head, trained through a two-stage fine-tuning process with data augmentation. The model achieves 97.07% test accuracy, performing competitively against a ResNet-18 baseline (98.0%) while offering a simpler architecture suitable for resource-constrained deployments. Evaluation using precision, recall, F1-score, and confusion matrix shows consistently high performance, with minor misclassifications among visually similar letters. This work demonstrates a novel application of VGG16-based deep learning for ArSL recognition, contributing to inclusive religious education and accessibility technologies.]]>
<![CDATA[Hybrid Time-Series Approaches for PV Power Prediction: Evaluating SARIMAX and Generative Model ]]>
<![CDATA[Berutu, Sunneng Sandino]]>;
<![CDATA[Zakaria, Immanuel Richie De Harjo]]>;
<![CDATA[Yuan, Anita]]>;
<![CDATA[Rahman, Mosiur]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4955
<![CDATA[Forecasting the output power of photovoltaic (PV) systems is crucial in managing renewable energy efficiently and sustainably. The availability of historical data and environmental variables, such as temperature and humidity, greatly influences prediction accuracy. However, in practice, historical data is often incomplete due to technical constraints or limited monitoring infrastructure, which results in decreased prediction quality and system efficiency. To overcome these challenges, this study proposed a comparative approach between two predictive models, namely SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables) as a classical statistical model, and WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) as a generative deep learning model designed to handle incomplete data and capture nonlinear relationships. The datasets included PV power output from the monitoring system at Universitas Kristen Immanuel (UKRIM) Yogyakarta, along with temperature and humidity data from the Kalitirto weather station in Sleman, Yogyakarta. The research was conducted through several stages, namely: data collection, pre-processing, model training, and evaluation using MAE, MSE, RMSE, and MAPE metrics. The results show that the SARIMAX model using the Time-Series Cross-Validation (TSCV) achieves the best numerical performance (MAE = 0.085; RMSE = 0.145). However, this model fails to represent daily patterns realistically. In contrast, both the standard SARIMAX and WGAN-GP models are more consistent in representing seasonal patterns and daily fluctuations, even though their prediction errors were slightly higher in terms of numerical metrics. The findings advance scientific understanding of hybrid forecasting models and offer practical implications for improving energy reliability and decision-making in data-constrained environments.]]>
<![CDATA[A Comparative Analysis of Hyperparameter-Tuned XGBoost and LightGBM for Multiclass Rainfall Classification in Jakarta ]]>
<![CDATA[Pringandana, Cokorda Gde Lanang]]>;
<![CDATA[Kusnawi , Kusnawi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4965
<![CDATA[The increasing frequency of extreme weather events in Jakarta has disrupted daily life and critical infrastructure, highlighting the urgent need for accurate rainfall prediction models to support disaster mitigation and early warning systems. This study aims to evaluate and compare the performance of two machine learning algorithms Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) for multiclass rainfall classification using historical meteorological data. The dataset, which includes features such as temperature, humidity, wind speed, and rainfall, was preprocessed through mean imputation, oversampling to address class imbalance, one-hot encoding, and feature engineering. Both models were trained and tuned using RandomizedSearchCV and assessed through cross-validation and independent testing. The results show that XGBoost consistently outperformed LightGBM, achieving 94% accuracy compared to 91%. Furthermore, XGBoost demonstrated higher precision, recall, F1-score, and specificity across all rainfall categories, resulting in fewer misclassifications and more stable predictions. Confusion matrices confirmed its superior ability to distinguish between similar weather conditions such as cloudy and rainy classes. These findings indicate that XGBoost is more effective in capturing nonlinear interactions between weather features and is therefore better suited for use in complex tropical climates. The study concludes that XGBoost is the more reliable model and recommends its integration into real-time early warning systems to improve climate resilience and disaster preparedness in urban areas like Jakarta that are increasingly affected by climate variability.]]>
<![CDATA[Forecasting Indonesian Banking Stock Prices Using Prophet, XGBoost, and Ridge Regression: A Comparative Analysis ]]>
<![CDATA[Tony, Tony]]>;
<![CDATA[Ratchagit, Manlika]]>;
<![CDATA[Hiryanto, Lely]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4973
<![CDATA[This study investigates the efficacy of Prophet, XGBoost, and Ridge Regression in forecasting stock prices of four major Indonesian banks—Bank Central Asia (BBCA.JK), Bank Negara Indonesia (BBNI.JK), Bank Rakyat Indonesia (BBRI.JK), and Bank Mandiri (BMRI.JK)—using daily historical data from January 2020 to March 2025, sourced from Yahoo Finance. The banking sector's volatility, evidenced by year-to-date declines ranging from 7.59% (BBCA) to 22.69% (BMRI) as of May 1, 2025, underscores the need for accurate predictive models. Performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), revealing Ridge Regression as the superior method, consistently achieving the lowest errors (i.e., MAE of 23.81 for BBNI.JK and RMSE of 55.75 for BBCA.JK). Prophet exhibited the highest errors, suggesting its seasonal focus is less suited to stock price unpredictability, while XGBoost performed moderately better but lagged behind Ridge Regression. The results highlight Ridge Regression’s effectiveness in handling multicollinearity and noise in financial data. Our discussions emphasize the importance of model selection based on data characteristics, with implications for investment decision-making in the Indonesian market. This research contributes to the field of computational finance by providing a comparative analysis that not only identifies Ridge Regression as a superior method for forecasting stock prices but also illuminates the limitations of popular models like Prophet and XGBoost in handling financial data's unique characteristics, guiding future model selection and development. Future research could explore hybrid models to enhance accuracy across varied market conditions, addressing the study’s 60-day forecasting horizon limitation.]]>
<![CDATA[Validation and Evaluation of Browser Forensics Using Digital Forensic Approach Based on the National Institute of Standards and Technology (NIST) Framework ]]>
<![CDATA[Syukri, Muhammad]]>;
<![CDATA[Riadi, Imam]]>;
<![CDATA[Sutikno, Tole]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4977
<![CDATA[Browsers have become essential applications in digital life alongside the advancement of internet technology. However, users’ low awareness of privacy security during web browsing can lead to the risk of data theft by malicious parties. This study analyzes digital traces in Google Chrome and Mozilla Firefox using a digital forensic approach based on the standards of the National Institute of Standards and Technology (NIST). The method involves four testing scenarios to compare digital traces in storage media (hard drive) and RAM between normal and private/incognito browsing modes. The objective of this research is to validate and evaluate previous findings conducted on the Linux operating system, using a different approach within a Windows environment. The experiment uses the same digital forensic tools to ensure data accuracy. This study contributes to the advancement of browser forensics by presenting a validated and reproducible framework for memory-based privacy evaluation, thereby supporting more accurate and systematic analysis of digital traces.]]>
<![CDATA[Literature Study on AI Mechanisms, Consciousness, and Emotion Integration in Chat GPT ]]>
<![CDATA[Ivanka, Angelicha Putri Dewi]]>;
<![CDATA[Ayu, Jenar Mahesa]]>;
<![CDATA[Rabbani, Sarah Surya]]>;
<![CDATA[Darwis, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4985
<![CDATA[The development of artificial intelligence (AI), particularly ChatGPT, demonstrates the ability to generate responses that resemble human emotional understanding and raises questions about the integration of consciousness, emotions, and algorithms in the context of singularity. This study aims to analyze how AI builds the illusion of consciousness and emotional closeness through computational mechanisms and its impact on human-AI interactions across various sectors. The method used is a structured literature review, examining academic journals, official reports, and the latest technical documentation classified by technical domain, including model architecture, emotion simulation, ethical implications, and publication year to assess its developmental dynamics. The results show that ChatGPT is capable of simulating empathy through affective computing and language prediction patterns, but it does not possess subjective emotional experiences like humans. This illusion of emotional closeness has proven beneficial in enhancing the effectiveness of interactions in education, public services, and healthcare, although it also poses risks such as emotional manipulation, data bias, and unrealistic empathy standards. The discussion emphasizes that the term “empathy” in AI should be understood technically as a data-driven adaptive response, not authentic emotional experience, and thus must be distinguished from human empathy. Critical analysis also reveals contradictions between AI's effectiveness in mimicking human behavior and its limitations in achieving genuine emotional connection. The discussion emphasizes that the term “empathy” in AI should be understood technically as a data-driven adaptive response, not an authentic emotional experience, and therefore needs to be distinguished from human empathy. Critical analysis also reveals a contradiction between AI's effectiveness in mimicking emotional behavior and its limitations in understanding meaning and consciousness at a deeper level. Therefore, this research contributes to the field of Computer Science by presenting a conceptual synthesis that clarifies both the limitations and potential of AI, while offering a foundation for designing more ethical interaction systems and developing risk assessment models in vulnerable sectors.]]>
<![CDATA[Design and Evaluation of a Hybrid AES-ECC Model for Secure Server Communication using REST API ]]>
<![CDATA[Saputra, Made Wisnu Adhi]]>;
<![CDATA[Huizen, Roy Rudolf]]>;
<![CDATA[Hostiadi, Dandy Pramana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4989
<![CDATA[Security in server-to-server communication is essential, especially in open networks vulnerable to data breaches and service disruptions. However, many existing solutions rely on a single cryptographic algorithm, limiting their ability to address diverse threats. This study aims to develop and evaluate a hybrid security model by combining the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) to ensure confidentiality, integrity, and authenticity of transmitted data. An experimental approach is applied through direct implementation in server communication. The model uses AES for symmetric encryption, ECC for dynamic session key exchange, and JSON Web Token (JWT) reinforced by nonce, timestamp, and HMAC-SHA256 for authentication and integrity verification. Test results show the model detects payload modification, replay attacks, JWT manipulation, and passive interception, with processing time still within an acceptable range. Communication efficiency is maintained with negligible payload overhead. The novelty of this research lies in integrating hybrid encryption with stateless authentication and integrity validation into a unified architecture. This integration allows security elements to be delivered systematically via REST API, making the model easy to adopt in existing architectures. The results of this study contribute to the advancement of secure API-based communication frameworks in the field of informatics, providing a practical, adaptable, and scalable solution for protecting data in distributed information systems.]]>
