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
1,242 Documents
<![CDATA[Comparative Analysis of Convolutional Neural Network, MobileNetV2, and EfficientNet for Tomato Leaf Disease Classification ]]>
<![CDATA[Guntur Guntur]]>;
<![CDATA[Abdul Latief Arda]]>;
<![CDATA[Andy Lukman Affandy]]>;
<![CDATA[Syamsu Alam]]>;
<![CDATA[Matalangi Matalangi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5704
<![CDATA[Tomato leaf diseases pose a serious threat to crop productivity and require accurate and efficient identification methods. Traditional visual inspection is time-consuming and prone to human error, motivating the need for automated image-based classification approaches. This study aims to evaluate the effectiveness of deep learning models for tomato leaf disease classification by comparing a custom Convolutional Neural Network and a transfer learning–based EfficientNet-B0 model. An experimental methodology was employed using a publicly available tomato leaf image dataset comprising nine disease classes and one healthy class. Images were preprocessed and augmented before being used to train a custom CNN and an EfficientNet-B0 model with a two-stage fine-tuning strategy. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices, and Receiver Operating Characteristic–Area Under Curve analysis. The experimental results demonstrate that the transfer learning approach significantly outperformed the custom CNN, with EfficientNet-B0 achieving the highest classification accuracy of 95.73% and improved class separability across disease categories.This research contributes to the field of Informatics and Computer Science by providing empirical evidence on the effectiveness of efficient transfer learning architectures for agricultural image classification. The findings support the development of resource-efficient artificial intelligence systems suitable for smart agriculture and edge-based deployment.]]>
<![CDATA[User Experience Evaluation of the Hear Me Sign Language Learning Application in a Special Education Setting Using the UEQ Framework ]]>
<![CDATA[Nurrohmi Gita Permata]]>;
<![CDATA[Nurhadi Nurhadi]]>;
<![CDATA[Joni Devitra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5712
<![CDATA[The increasing adoption of mobile learning technologies in inclusive education highlights the need for systematic user experience (UX) evaluation, particularly for accessibility-oriented applications designed for students with hearing impairments. This study evaluates the UX of the Hear Me sign language learning application in a special education (SLB) context using the User Experience Questionnaire (UEQ). A quantitative evaluative design was applied to 27 respondents, including data transformation, reliability verification using Cronbach’s Alpha, dimensional mean analysis, group comparison, and benchmark interpretation based on the official UEQ dataset. The results indicate that all six UX dimensions fall within the lower benchmark range, reflecting limited experiential differentiation. Mean scores ranged from −0.065 to 0.176 and clustered near neutrality. Efficiency achieved the highest score (0.176), while stimulation (−0.065) and novelty (−0.056) indicated limited motivational engagement. Reliability coefficients across dimensions (α = 0.821–0.880) demonstrated acceptable internal consistency. A descriptive comparison between students and teachers revealed perceptual differences in pragmatic and hedonic evaluations. Despite a relatively high public rating (4.5/5) on the Google Play Store platform, benchmark analysis revealed a divergence between public satisfaction indicators and structured UX measurement. These findings emphasize the importance of standardized UX benchmarking for accessibility-oriented educational applications. Improvements in interactive feedback, animation stability, and gamification features are recommended to enhance engagement and experiential quality in inclusive mobile learning systems.]]>
<![CDATA[Hybrid TOPSIS–ELECTRE Decision Support System for Objective New Employee Selection: A Web-Based Implementation Study ]]>
<![CDATA[Agnesa Putri Utami]]>;
<![CDATA[Nurhadi Nurhadi]]>;
<![CDATA[Benni Purnama]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5713
<![CDATA[Employee recruitment plays a crucial role in organizational performance; however, manual selection processes often result in subjective assessments, inconsistent evaluations, and limited transparency. This study aims to develop a web-based Decision Support System (DSS) integrating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Elimination and Choice Expressing Reality (ELECTRE) to improve the objectivity and consistency of em16selection at Jurnal1Jambi.com. The proposed hybrid multi-criteria decision-making model evaluates candidates using six criteria: education level, test score, work experience, health, age, and legal record. TOPSIS is used to generate candidate rankings, while ELECTRE validates dominance relationships among alternatives. Experimental results show that the hybrid approach consistently identifies the best candidate with a preference value of 0.6652, indicating improved ranking stability compared with conventional procedures. The developed DSS enhances decision traceability, reduces evaluation bias, and provides a practical framework for implementing hybrid MCDM techniques in recruitment systems. This study contributes to the advancement of intelligent decision support applications in the field of informatics by demonstrating the effectiveness of integrated decision-making methods in human resource management.]]>
<![CDATA[Decision Support System for Prioritizing PKH Social Assistance Beneficiaries using Fuzzy-AHP, MOORA and Sensitivity Analysis ]]>
<![CDATA[Mychele Salsabila]]>;
<![CDATA[Nurhadi Nurhadi]]>;
<![CDATA[Dodo Zaenal Abidin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5714
<![CDATA[Prioritizing beneficiaries of the Program Keluarga Harapan (PKH) at the urban-village level is often conducted manually, making the process prone to subjectivity, inconsistency, and limited traceability. This study aims to develop a transparent and auditable Decision Support System (DSS) to prioritize PKH candidates objectively under multi-criteria conditions and assessment uncertainty. The proposed DSS combines AHP to verify judgment consistency, Fuzzy-AHP using Triangular Fuzzy Numbers to model linguistic uncertainty and derive criterion weights, and MOORA to compute preference values and generate candidate rankings. The approach is evaluated through a case study in Pasir Putih Urban Village involving 50 prospective beneficiaries and 18 regulation-aligned evaluation criteria. The consistency test yields a Consistency Ratio (CR) of 0.09, indicating acceptable consistency. The ranking results show that alternative A12 achieves the highest preference value, followed by A50. To assess recommendation reliability, a sensitivity analysis is performed by varying criterion weights by ±25% with proportional normalization. While several rank shifts occur, seven alternatives (A14, A32, A10, A26, A7, A12, and A50) remain relatively stable, indicating more robust outcomes. From an informatics perspective, this work contributes a reproducible MCDM-based DSS framework that integrates uncertainty modeling and robustness evaluation to improve accountability and decision transparency in public-sector social assistance prioritization.]]>
<![CDATA[Investigating the Mediating Role of Cybersecurity Awareness in Bridging Cognitive Factors and Secure Behavioural Intentions: A Quantitative Approach Using PLS-SEM ]]>
<![CDATA[Muhammad Agreindra Helmiawan]]>;
<![CDATA[Yanyan Sofiyan]]>;
<![CDATA[Esa Firmansyah]]>;
<![CDATA[Dody Herdiana]]>;
<![CDATA[Irfan Fadil]]>;
<![CDATA[Titik Khawa Abdul Rahman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5716
<![CDATA[Cybersecurity threats have become increasingly sophisticated, rendering the human element a critical vulnerability despite advanced technical safeguards. This study investigates the cognitive drivers of cybersecurity awareness and secure behaviour through the lens of Protection Motivation Theory (PMT), specifically examining the mediating role of Cybersecurity Awareness (CA). A quantitative approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyse data from students and faculty members in a higher education setting. The findings substantiate that Self-Efficacy (β=0.412,p<0.05) and Response Efficacy (β=0.385,p<0.05) are significant predictors of CA, with the model achieving a robust R2 value of 0.703. Crucially, the mediation analysis identifies CA as a vital cognitive bridge that translates internal confidence into Secure Behavioural Intentions. These results offer an integrated framework for developing targeted intervention strategies in academic institutions. For the field of Informatics, this research underscores the urgency of designing human-centric security systems that prioritize psychological empowerment to foster sustainable digital resilience against an evolving threat landscape.]]>
<![CDATA[Comparative Analysis of Logistic Regression and Random Forest with SMOTE for Sentiment Classification on Ethanol Policy in Indonesia ]]>
<![CDATA[Nabiel Muhammad Al Ghazali]]>;
<![CDATA[Hanif Fakhrurroja]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5717
<![CDATA[Public opinion plays a crucial role in the successful implementation of renewable energy policies, particularly regarding the transition to ethanol-based fuels in Indonesia. Understanding this sentiment is vital to mitigate social resistance and design effective communication strategies, as policy failure often stems from public rejection rather than technical issues. However, social media data regarding this topic is often highly imbalanced, with a dominance of non-positive sentiments (96%) compared to positive ones (4%), creating a severe bias in machine learning models known as the accuracy paradox. This study aims to classify public sentiment towards ethanol policy and evaluate the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in handling extreme class imbalance. The methods used include text preprocessing with Sastrawi, feature extraction using TF-IDF, and a comparative classification between Logistic Regression (LR) and Random Forest (RF). The novelty of this research lies in addressing the extreme imbalance in high-dimensional text data, proving that simpler linear models can outperform complex ensemble models in terms of minority class detection. The results show that the Optimized Logistic Regression model with SMOTE outperformed Random Forest, achieving a Precision of 1 and an F1-Score of 0.67 for the minority class, compared to RF which only reached an F1-Score of 0.55. This study concludes that for high-dimensional sparse text data, linear models combined with SMOTE provide superior performance in identifying minority sentiments.]]>
<![CDATA[Experimental and Comparative Evaluation of an Integral-Based Squeeze Mechanism for Enhanced Feature Calibration in DenseNet121 ]]>
<![CDATA[Pandi Barita Nauli Simangunsong]]>;
<![CDATA[Tuti Andriani]]>;
<![CDATA[Paska Marto Hasugian]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5725
<![CDATA[The Squeeze-and-Excitation (SE) Block, a concrete form of channel attention mechanisms, serves to flexibly readjust feature channels, with squeeze using Global Average Pooling for global context extraction across all channels. The limitation of the SE Block lies in discrete spatial merging, which causes important information to be lost. This research proposes an Integral-based SE Block by formulating the Squeeze operation as a continuous spatial integral to produce clearer channel feature merging. The IntSE block is implemented on the densenet121 architecture by replacing the discrete spatial SE without changing the structure of the backbone. The effectiveness of the architecture proposed in this study uses secondary datasets, namely Plant Village and HM10000, which were used for experiments during evaluation. The PlantVillage dataset consists of 12,246 tomato leaf data and consists of 10 classes, while the HAM10000 dataset consists of 10,015 data and has 7 classes. The results show that DenseNet121 with IntSE achieves an accuracy of 99.25% and an F1-score of 99.25% for the PlantVillage dataset, as well as an accuracy of 83.57% and an F1-score of 83.19% for the HAM10000 dataset. This experiment shows that densenet121 with IntSE outperforms basic densenet121, Discrete Spatial SE, and Attention CBAM in terms of accuracy and F1-Score. The ablation analysis shows that the formula of spatial squeeze integral performance is improved compared to global pooling and adaptive pooling. Statistical testing shows significant changes to state that the performance of the actual architecture.]]>
<![CDATA[Stacking Ensemble and Semi-Synthetic Target Engineering for Predicting User Engagement in Augmented Reality Digital Marketing ]]>
<![CDATA[Nurhadi Nurhadi]]>;
<![CDATA[Yessi Hartiwi]]>;
<![CDATA[Despita Meisak]]>;
<![CDATA[Admaja Admaja]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5748
<![CDATA[This study investigates the effectiveness of ensemble learning approaches for predicting user engagement, represented by time spent interacting, within Augmented Reality (AR) and AI-personalized digital marketing environments. Modeling engagement behavior is challenging due to its non-linear and heterogeneous nature. To address this issue, a semi-synthetic target engineering framework was implemented to construct a controlled dependent variable by combining deterministic behavioral components with stochastic variability. The experimental design incorporated a leakage-free preprocessing pipeline, Bayesian hyperparameter optimization, and rigorous validation strategies including holdout testing, repeated 5×5 cross-validation, and paired t-tests. The results show that boosting methods outperform bagging approaches, where XGBoost achieves higher generalization performance compared to Random Forest (R² 0.6567 vs 0.6158). The Stacking Ensemble, combining both models through a Ridge meta-learner, produces the best predictive performance (R² 0.6607; RMSE 21.0100). These findings demonstrate that integrated ensemble strategies can provide more stable predictions for complex engagement patterns. From an informatics perspective, this research contributes a controlled experimental framework for evaluating ensemble learning models in behavioral prediction problems, supporting the development of data-driven engagement prediction systems in AI-driven digital marketing platforms.]]>
<![CDATA[Improvement Of User Experience Website Center For Technology Development And Application Of XZY University ]]>
<![CDATA[Arief Kelik Nugroho]]>;
<![CDATA[Fara Anggia Rengganis]]>;
<![CDATA[Aini Hanifa]]>;
<![CDATA[Nofiyati Nofiyati]]>;
<![CDATA[Nurul Tiara Kadir]]>;
<![CDATA[Axl Adilla]]>;
<![CDATA[Mohammad Irham Akbar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5819
<![CDATA[The Center for Technology Development and Application (P3T) is one part of the Institute for Research and Community Service owned by XYZ University. P3T has a website that is intended for publication activities regarding activities, services, and innovations related to the application and development of technology. Therefore, the website needs to highlight the existing information through published content. In this publication, a good delivery method is needed including the information to be conveyed and the UI UX, which is directly in contact with the users who will access the website. In this case, the role of UX is very important in providing convenience and comfort in accessing the information on the website. In improving the UX of the website of the XYZ University Center for Technology Development and Application, the user centered design method was used, and UEQ was used in its analysis to determine six rating scales. The results of the analysis will be processed using the Data Analysis Tools provided by the UEQ Team and will be used as a reference in making improvements and re-design of the previously used themes, as well as references in adjustments to the available themes. The resulting output scale is a benchmark of the six existing rating scales.]]>
<![CDATA[Comparative Evaluation of Machine Learning and Ensemble Learning with Synthetic Minority Oversampling and Random Undersampling for Loan Approval Prediction Across Multiple Datasets ]]>
<![CDATA[Ilham Putra Ariatama]]>;
<![CDATA[Wahyu Fajar Setiawan]]>;
<![CDATA[Afif Amirullah]]>;
<![CDATA[Ratih Nur Esti Anggraini]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5854
<![CDATA[Loan approval prediction remains a critical yet challenging task in financial risk management, particularly due to class imbalance and variability across lending datasets. This study presents a comparative evaluation of eleven machine learning and ensemble learning algorithms for loan approval prediction, evaluated across three publicly available datasets with distinct characteristics to ensure generalizability of findings. The algorithms evaluated include Logistic Regression, Decision Tree, Random Forest, Soft Voting, Hard Voting, AdaBoost, Gradient Boosting, XGBoost, Bagging, Pasting, and Stacking. To address class imbalance, three data balancing strategies are applied: original imbalanced data, Synthetic Minority Oversampling Technique, and random undersampling. Model performance is assessed using accuracy, precision, recall, and F1-score under 5-fold cross-validation with a 75:25 stratified train-test split. Experimental results demonstrate that ensemble methods consistently outperform single classifiers across all datasets. Stacking achieves the highest accuracy on original data across all three datasets (88.33%, 76.00%, and 93.16%, respectively), while XGBoost demonstrates competitive and stable performance across multiple data balancing conditions. Results also reveal that Synthetic Minority Oversampling does not universally improve model performance, as certain dataset characteristics lead to accuracy degradation under oversampling conditions. These findings contribute to the field of computer science by establishing a multi-dataset benchmark for ensemble learning in automated credit scoring and by providing empirical evidence on the interaction between data balancing strategies and classifier architectures, offering a practical framework for selecting robust machine learning pipelines in financial decision support systems.]]>
