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[Tuberculosis Diagnosis From X-Ray Images Using Deep Learning And Contrast Enhancement Techniques ]]>
<![CDATA[Risma, Vita Melati]]>;
<![CDATA[Utami, Ema]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4315
<![CDATA[Tuberculosis (TB) is an infectious disease that poses a global health threat. Early diagnosis through chest X-ray (CXR) imaging is effective in reducing transmission and improving patient recovery rates. However, the limited number of radiologists in high TB burden areas hampers rapid and accurate detection. This study aims to improve TB diagnosis accuracy using deep learning models. Convolutional Neural Networks (CNN) are applied to analyze CXR images to support automated detection in regions with limited radiology personnel. The method involves image processing using Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. A public dataset consisting of 2,188 images was used, with preprocessing steps including resizing, normalization, and augmentation. The DenseNet201 model was employed as the main architecture, trained for 10 epochs with various batch sizes to evaluate its performance. Results show that the combination of CLAHE and DenseNet201 achieved the highest accuracy of 94.84%. Image quality enhancement with CLAHE proved to improve accuracy compared to models without preprocessing. This research contributes to enhancing the efficiency of automated early TB detection, reducing reliance on radiologists, and accelerating clinical decision-making.]]>
<![CDATA[Enhancing Cyberbullying Detection on Platform 'X' Using IndoBERT and Hybrid CNN-LSTM Model ]]>
<![CDATA[Hafiza, Annisaa Alya]]>;
<![CDATA[Setiawan, Erwin Budi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4321
<![CDATA[Cyberbullying on social media platforms has become widespread in society. Cyberbullying can take many forms, including hate speech, trolling, adult content, racism, harassment, or rants. One social media platform that has many cyberbullies is Twitter, which has been renamed 'X'. The anonymous nature of this 'X' platform allows users from all over the world to commit cyberbullying as they can freely share their thoughts and expressions without having to account for their identity. This research aims to explore the influence of IndoBERT’s semantic features on hybrid deep learning models for cyberbullying detection while integrating TF-IDF feature extraction and FastText feature expansion to enhance text classification performance. Specifically, this study examines how IndoBERT’s semantic capabilities affect the hybrid deep learning model in detecting cyberbullying on platform 'X'. This study has 30,084 tweets with a hybrid deep learning approach that combines CNN and LSTM. In the IndoBERT scenario, IndoBERT features were first combined with TF-IDF, then expanded using FastText before being applied to the hybrid deep learning model. The test results produced the highest accuracy rate by: CNN (80.69%), LSTM (80.67%), CNN- LSTM (81.18%), CNN-LSTM-IndoBERT (82.05%). This research contributes to informatics by integrating hybrid deep learning (CNN-LSTM) with IndoBERT and TF-IDF, demonstrating its effectiveness in improving cyberbullying detection in Indonesian text. Future research can explore the use of other transformer-based models such as RoBERTa or ALBERT to enhance contextual understanding in cyberbullying classification.]]>
<![CDATA[A TOGAF 10-Based Enterprise Architecture Framework for Digital Transformation in SME Banks ]]>
<![CDATA[Sari, Laras Ayu Ditya]]>;
<![CDATA[Mulyana, Rahmat]]>;
<![CDATA[Mukti, Iqbal Yulizar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4329
<![CDATA[The banking sector faces significant challenges in implementing Digital Transformation (DT), particularly among Bank Perekonomian Rakyat (BPR), which serves as SME banks in Indonesia. These institutions often struggle with infrastructure limitations, technological adoption, and regulatory compliance. While existing research extensively examines DT strategies and Enterprise Architecture (EA) frameworks in large-scale banking institutions, their application to smaller banks like BPR remains underexplored. This study addresses this gap by developing an EA- based solution tailored to BPRDCo, a representative SME bank. Using the Design Science Research (DSR) methodology, the study follows five structured stages: problem explication, requirement specification, design and development, demonstration, and evaluation. The framework leverages the TOGAF Standard 10th Edition, integrating best practices across business, information systems, and technology architectures. The resulting EA blueprint provides a structured guide for DT implementation, aligning business and IT strategies while ensuring regulatory compliance. This study contributes to the EA knowledge base and offers practical implications for SME banks to enhance operational efficiency, optimize resource utilization, and strengthen competitiveness in the evolving financial landscape. By offering a systematic approach DT, this research advances knowledge in IT governance, architecture modeling, and system integration. Moreover, it provides a replicable framework that can inform future developments in digital banking solutions, reinforcing the importance of EA as a strategic tool for sustainable technological innovation.]]>
<![CDATA[Comparison of SVM and Gradient Boosting with PCA for Website Phising Detection ]]>
<![CDATA[Syam, Nur Aini]]>;
<![CDATA[Arifin, Nurhikma]]>;
<![CDATA[Firgiawan, Wawan]]>;
<![CDATA[Rasyid, Muhammad Furqan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4344
<![CDATA[The increasing use of the internet has led to a rise in phishing attacks, posing a threat to user data security. This study compares the performance of the Support Vector Machine (SVM) and Gradient Boosting algorithms, integrated with Principal Component Analysis (PCA) for dimensionality reduction, in classifying phishing websites. The dataset consists of 11,054 samples classified into two categories: phishing (1) and non-phishing (-1), with three data partition scenarios for training and testing: 70:30, 80:20, and 90:10. Experimental results indicate that SVM outperforms Gradient Boosting in terms of accuracy and recall, particularly in detecting phishing websites. In the 80:20 and 70:30 data partition scenarios, the SVM model achieved an accuracy of 96% to 97% and had a higher recall for phishing websites, making it more sensitive to phishing detection. However, Gradient Boosting demonstrated consistent performance with an accuracy of around 94%, providing a balanced result between precision and recall for both classes. Therefore, the SVM model is superior for phishing detection tasks requiring high sensitivity to phishing websites, while Gradient Boosting remains a viable alternative when a more balanced performance between phishing and non-phishing sites is needed. The study concludes that both algorithms can be effectively used for phishing detection, with potential improvements through further experiments and hyperparameter tuning.]]>
<![CDATA[Corn Leaf Diseases Classification Using CNN with GLCM, HSV, and L*a*b* Features ]]>
<![CDATA[Johari, Putri Fausyah]]>;
<![CDATA[Arifin, Nurhikma]]>;
<![CDATA[Muzaki, Muzaki]]>;
<![CDATA[Utama, Muhammad Surya Alif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4345
<![CDATA[Corn leaf diseases can damage plants and reduce crop yields, thus affecting the quality and quantity of corn production. This study aims to classify corn leaf diseases using the Convolutional Neural Network (CNN) method with different color features, namely Gray Level Co-Occurrence Matrix (GLCM), HSV, and L*a*b*. The dataset consists of 1,739 corn leaf images, which are divided into four disease classes: Blight, Common Rust, Gray Spot, and Healthy. The data is split into training and testing sets using an 80:20 ratio. Two testing scenarios were conducted: individual feature evaluation and feature combination. The results show that in the first scenario, the L*a*b* feature provides the best accuracy at 91.75%, followed by the HSV feature with an accuracy of 90.29%, and GLCM with an accuracy of 78.40%. In the second scenario, the combination of HSV and L*a*b* features results in the highest accuracy of 92.48%, indicating that combining color and brightness information can improve the model's performance. The combination of GLCM and L*a*b* features results in an accuracy of 91.75%, while the combination of GLCM and HSV results in an accuracy of 90.29%. These findings demonstrate that integrating HSV and L*a*b features enhances CNN performance in corn leaf disease classification, outperforming individual feature- based approaches, thus contributing to more effective AI-based agricultural disease diagnosis.]]>
<![CDATA[Analyzing Blockchain Adoption for Copyright Certification in Lombok's Woven Industry: An Extended TAM Perspective ]]>
<![CDATA[Bimantari, Joselina Rizki]]>;
<![CDATA[Wijayanto, Heri]]>;
<![CDATA[Widiartha, Ida Bagus Ketut]]>;
<![CDATA[Afwani, Royana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4366
<![CDATA[This research explores the Extended Technology Acceptance Model (TAM) and Partial Least Squares Structural Equation Modelling (PLS-SEM) to investigate the acceptability of blockchain-based digital copyright certification among traditional woven fabric SMEs (Small and Medium Enterprises) in Lombok. This research develops a blockchain-based certification system using NFTs, IPFS, and ECDSA to secure ownership, metadata, and authentication of traditional woven fabrics in Lombok. The problem addressed is the lack of understanding and acceptance of blockchain technology for copyright certification among SMEs, which can impede the protection of their innovations. The aim of this study is to analyze the variables that influence this technology's acceptance and to provide strategies for increasing its adoption. This study explores blockchain-based copyright certification adoption among Lombok's woven fabric SMEs using an Extended TAM with novel variables: Perceived Trust, Privacy, and Government Regulations. Findings from PLS-SEM reveal these, alongside traditional TAM factors, significantly impact adoption. By addressing digital literacy gaps and regulatory challenges, this research provides insights into promoting blockchain adoption through targeted training and outreach, contributing to innovation protection for traditional artisans. A quantitative method was implemented with a validated and reliable surveys distributed both online and offline to SMEs in three main woven villages in Lombok. Data analysis using PLS-SEM revealed significant impacts of perceived usefulness (PU), perceived ease of use (PEOU), Perceived Trust (PT), Government Regulations (GR), Perceived Protection (PP), attitude towards using (ATU), and behavioral intention to use (BITU) on the acceptance of blockchain technology. This study concludes that TAM factors are crucial in evaluating these SMEs' acceptance of blockchain-based copyright certification. Recommendations are provided to enhance SMEs understanding and skills in applying this technology through targeted training and outreach.]]>
<![CDATA[Improving Infant Cry Recognition Using MFCC And CNN-Based Audio Augmentation ]]>
<![CDATA[Setyoningrum, Nuk Ghurroh]]>;
<![CDATA[Utami, Ema]]>;
<![CDATA[Kusrini, Kusrini]]>;
<![CDATA[Wibowo, Ferry Wahyu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4373
<![CDATA[Recognizing infant cries is essential for understanding a baby's needs; however, previous research has struggled with imbalanced datasets and limited feature extraction techniques. Conventional methods utilizing CNN without data augmentation often failed to accurately classify minority classes such as belly pain, burping, and discomfort, resulting in biased models that predominantly recognized majority classes. This study proposes an MFCC-based data augmentation pipeline, incorporating time stretching, pitch scaling, noise addition, polarity inversion, and random gain adjustments to increase dataset diversity and enhance model generalization. By applying this approach, the dataset size was expanded from 457 to 8,683 samples, and a CNN model with three convolutional layers, ReLU activation, and max pooling was trained for cry pattern classification. The results indicate a substantial accuracy improvement from 78% to 98%, with F1-scores for minority classes rising from 0.00 to above 0.90, confirming that augmentation effectively addresses dataset imbalance. This research advances computer science and artificial intelligence, particularly in audio signal processing and deep learning for healthcare applications, by demonstrating the role of data augmentation in improving cry classification performance. Future directions include integrating multimodal data (visual and physiological signals), exploring advanced deep learning architectures, and developing real-time applications for smart baby monitoring systems to further enhance infant cry recognition technology.]]>
<![CDATA[Performance Comparison of LSTM Models with Various Optimizers and Activation Functions for Garlic Bulb Price Prediction Using Deep Learning ]]>
<![CDATA[Aldo, Dasril]]>;
<![CDATA[Paramadini, Adanti Wido]]>;
<![CDATA[Amrustian, Muhammad Afrizal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4412
<![CDATA[Accurate commodity price forecasting is crucial for market stability and decision-making. This study evaluates the performance of the Long Short-Term Memory (LSTM) model using various activation functions and optimization algorithms for predicting garlic bulb prices. Historical price data was collected from panelharga.badanpangan.go.id and preprocessed through normalization and dataset splitting into training, validation, and test sets. The model was trained for 200 epochs using activation functions ReLU, Sigmoid, and Tanh, combined with optimization algorithms Adam, RMSprop, SGD, Adagrad, Adadelta, Nadam, and AdamW. Experimental results indicate that ReLU + Adam achieves the best performance with Final Epoch Loss of 0.001789, RMSE of 0.701632, MAPE of 0.009593, and R² of 0.909794, followed by Sigmoid + Nadam and Tanh + Adam, which also yielded high accuracy. These findings reinforce prior research, highlighting Adam and its momentum-based variants as effective optimizers for LSTM training. This study provides insights into selecting optimal activation functions and optimizers for commodity price forecasting. Future work may explore hybrid models and external factors, such as global market trends, to enhance predictive accuracy in time series data analysis.]]>
<![CDATA[Enhancing MPEG-1 Video Quality Using Discrete Wavelet Transform (DWT) with Coefficient Factor and Gamma Adjustment ]]>
<![CDATA[Krismawan, Andi Danang]]>;
<![CDATA[Susanto, Ajib]]>;
<![CDATA[Rachmawanto, Eko Hari]]>;
<![CDATA[Muslih, Muslih]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Ali, Rabei Raad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4422
<![CDATA[Low-quality video caused by compression artifacts, noise, and loss of detail remains a significant challenge in video processing, affecting applications in streaming, surveillance, and medical imaging. Existing enhancement techniques often struggle with excessive noise amplification or high computational complexity, making them inefficient for real-time applications. This study proposes an improved video enhancement method using Discrete Wavelet Transform (DWT) with optimized coefficient factor and gamma adjustment. DWT is a mathematical approach that decomposes video frames into frequency subbands, enabling selective enhancement of important details. To analyze the impact of different wavelets, this study evaluates Coif5, db1, sym4, and sym8 wavelets. The sym8 wavelet, known for its high symmetry and ability to minimize artifacts, achieves the best results in preserving fine details and structural integrity. The coefficient factor is dynamically adjusted to sharpen details while preventing noise amplification, and gamma adjustment is applied to optimize brightness and contrast. The proposed method was evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Experimental results show that sym8 wavelet with gamma 0.7 and coefficient factor 0.3 provides the best balance, achieving an MSE of 0.062, a PSNR of 12.050 dB, and an SSIM of 0.674, outperforming Coif5, db1, and sym4 wavelets. The results indicate that wavelet selection significantly impacts video enhancement performance, with sym8 providing superior contrast enhancement and noise suppression. This study contributes to real-time video processing and AI-based applications, ensuring enhanced visual quality with minimal computational overhead.]]>
<![CDATA[Imperceptible Watermarking Using Discrete Wavelet Transform and Daisy Descriptor for Hiding Noisy Watermark ]]>
<![CDATA[Abdussalam, Abdussalam]]>;
<![CDATA[Umam, Chaerul]]>;
<![CDATA[Sari, Wellia Shinta]]>;
<![CDATA[Rachmawanto, Eko Hari]]>;
<![CDATA[Shidik, Guruh Fajar]]>;
<![CDATA[Andono, Pulung Nurtantio]]>;
<![CDATA[Lestiawan, Heru]]>;
<![CDATA[Islam, Hussain Md Mehedul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4423
<![CDATA[This research aims at overcoming the challenge of improving security and robustness in digital image watermarking, a critical activity in protecting intellectual property against misuse and manipulation. In a move to overcome such a challenge, this work introduces a new form of watermarking that incorporates Discrete Wavelet Transform (DWT) and Daisy Descriptor, with a view to enhancing both durability and invisibility of the watermark. The proposed method embeds a noise-variant watermark into selected frequency sub-bands using DWT, while the Daisy Descriptor enhances resistance to noise-based attacks. Testing conducted with three grayscale images, namely Lena, Cameraman, and Lion, each with a resolution of 512 × 512 pixels, showed that the proposed DWT-Daisy Descriptor outperforms current methodologies, producing high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values. In fact, in Lena, a PSNR value of 63.71 dB and an SSIM value of 1 were attained, with Cameraman having a PSNR value of 68.33 dB and an SSIM value of 1. As for attack resistivity, a high PSNR value of 50.11 dB under Gaussian attack and 55.70 dB under Salt-and-Pepper attack, with SSIM values approaching 1, confirm the robustness of the proposed scheme. This study highlights the significance of an efficient and secure watermarking technique that not only preserves image quality but also withstands various distortions, making it highly relevant for digital content protection in modern multimedia applications.]]>
