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[Hybrid Efficientnetv2b0 with Attention Mechanism and Multi-Layer Perceptron for Optimized Multi-Class Skin Wound Classification ]]>
<![CDATA[Hartini Damanik]]>;
<![CDATA[Anjar Wanto]]>;
<![CDATA[Surya Darma]]>
<![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.2026.7.4.5631
<![CDATA[Skin wound classification remains a challenging task in medical image analysis due to variations in visual appearance and limitations of conventional diagnostic methods. This study aims to develop an optimized deep learning framework for multi-class skin wound classification by integrating EfficientNetV2B0, Convolutional Block Attention Module (CBAM), and Multi-Layer Perceptron (MLP). EfficientNetV2B0 is used for feature extraction, while CBAM enhances feature representation by emphasizing relevant channel and spatial information, and MLP strengthens the classification process in multi-class scenarios. Experimental results show that the proposed hybrid model achieves an accuracy of 95% and consistently outperforms the baseline EfficientNetV2B0 architecture. The findings demonstrate that the integration of attention mechanisms and MLP significantly improves classification performance. This study contributes to the development of automated skin wound analysis systems and provides insights into hybrid deep learning architectures for medical image classification. ]]>
<![CDATA[Health Index Modelling of Turbofan Engines Using Residual Dilated Convolutional Neural Networks for Predictive Maintenance ]]>
<![CDATA[Alfia Nurlaili Tahiyat]]>;
<![CDATA[Lusiana Efrizoni]]>;
<![CDATA[Triyani Arita Fitri]]>;
<![CDATA[Susanti Susanti]]>
<![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.2026.7.4.5636
<![CDATA[Data-driven prognostics and health management (PHM) for turbofan engines requires a Health Index (HI) that is learnable from multivariate telemetry and credible as a basis for maintenance decisions. This study presents a deep learning-based HI modelling framework on the N-CMAPSS benchmark that converts operating conditions and sensor streams into a bounded HI and, subsequently, into decision-oriented outputs for predictive maintenance. A baseline convolutional model is benchmarked against a residual dilated CNN to capture multi-scale degradation signatures from fixed-length temporal windows. To preserve evaluative integrity, health-zone thresholds are calibrated on validation predictions and then fixed, producing a three-zone taxonomy (critical, warning, healthy) for rapid field triage, alongside a continuous risk score that induces a rank-ordered maintenance priority list from most critical to most healthy. The selected model achieves HI regression performance of RMSE = 0.1266, MAE = 0.0720, and R² = 0.7241, while the calibrated zone mapping attains accuracy = 0.8688 and macro-F1 = 0.6124. The main contribution is a leakage-aware, decision-coupled pipeline that delivers both interpretable health zoning and risk-ranked prioritization, strengthening the operational linkage between predictive modelling and maintenance triage within PHM-oriented Informatics.]]>
<![CDATA[Real-Time Halal Product Ingredient Classification based on Bilingual Text using Optical Character Recognition and One-Dimensional Convolutional Neural Network ]]>
<![CDATA[Muhammad Thohir]]>;
<![CDATA[Muhammad Irfan Karim]]>;
<![CDATA[Vitri Tundjungsari]]>
<![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.2026.7.4.5638
<![CDATA[Instant verification of halal status for food ingredients remains a significant challenge for Muslim consumers, particularly when facing complex compositions and foreign-language labels without internet access. This study aims to develop an offline, real-time mobile application for halal ingredient classification by implementing lightweight deep learning models on edge devices. The proposed system integrates Google ML Kit for text extraction and evaluates five neural network architectures: FastText, 1D-CNN, LSTM, GRU, and Multi-CNN trained on a balanced, bilingual (Indonesian–English) dataset comprising 123,449 ingredient samples. Experimental results demonstrate that the 1D-CNN model was the optimal choice, achieving an average accuracy of 99.72%, an average inference time of 60.99 ms, and a compact model size of 719.2 KB. Although the GRU model achieved marginally higher accuracy, the 1D-CNN provided a superior efficiency trade-off with a 2.6× faster training time and lower computational complexity. The primary scientific contribution of this research lies in demonstrating that lightweight 1D-CNN architectures can deliver high-precision, fully offline text-based classification on mobile edge devices, offering a robust alternative to latency-constrained cloud-based verification systems.]]>
<![CDATA[Permutation-Based RFECV on Imbalanced Students’ Grade and Students’ Dropout Datasets ]]>
<![CDATA[Irfan Pratama]]>;
<![CDATA[Albert Yakobus Chandra]]>;
<![CDATA[Putry Wahyu Setyaningsih]]>;
<![CDATA[Husna Sarirah Husin]]>;
<![CDATA[Odi Nurdiawan]]>
<![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.2026.7.4.5641
<![CDATA[Predicting student academic performance is a critical task in EDM. To build predictive models, it is essential to identify the most informative features from LMS data. However, existing feature selection techniques exhibit significant drawbacks. Filter Methods evaluate features independently from the learning model, ignoring interactions and context. As a result, they may select redundant features and lack adaptability to specific classifiers. To enhance classification accuracy, this research proposes a permutation-based Recursive Feature Elimination with cross-validation (RFECV) feature selection method to strengthen machine learning models' student dataset classification, thereby eliminating bias from model-based feature importance calculations using the default method. The proposed method excels on the available educational datasets (Math, Por, Student Success, and Student dataset) in terms of F1-Score. Specifically, we achieved 80.59% on math dataset and 71.81% on student success dataset, both of which are multiclass datasets.]]>
<![CDATA[Suicidal Ideation Detection on Twitter in Indonesia: A Comparative Transfer Learning Study using IndoBERT and Multilingual BERT ]]>
<![CDATA[Wiyan Herra Herviana]]>;
<![CDATA[Retno Kusumaningrum]]>;
<![CDATA[Bayu Surarso]]>
<![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.2026.7.4.5652
<![CDATA[The effectiveness of suicide prevention in Indonesia is severely hindered by significant underreporting and social stigma, leading at-risk individuals to express their distress on social media platforms such as Twitter. However, detecting these signals is computationally challenging due to the informal nature of Indonesian slang and the risk of losing emotional context through aggressive pre-processing. This study aims to evaluate the performance of various deep learning and traditional models in detecting suicidal ideation while specifically analyzing the impact of syntactic preservation. We performed a comparative analysis using IndoBERT, Multilingual BERT (mBERT), and three baseline models including Logistic Regression, Support Vector Machine, and Random Forest which were evaluated through two distinct pre-processing strategies, namely Dataset A (fully preprocessed) and Dataset B (partially preprocessed). Experimental results demonstrate that IndoBERT achieved the highest performance on Dataset B with an accuracy of 90.6%, outperforming traditional baselines. The results indicate that omitting stopword removal and stemming is more effective for transformer-based architectures, as retaining original word forms helps preserve semantic integrity and emotional nuances required for contextual understanding. These findings highlight the importance of context-aware solutions to bridge the data gap in public health. By providing a robust computational framework that maintains linguistic integrity, this research has the potential to facilitate scalable, real-time interventions for supporting the identification of high-risk individuals within the Indonesian digital landscape. This study contributes to the development of automated mental health screening tools by demonstrating the potential of NLP-based methods for detecting psychological distress in low-resource, informal languages.]]>
<![CDATA[Title Inclusive Educational Data Mining Framework Using Classification, Clustering, and Association Rules for Parent–Child Interactions and Reflective Learning ]]>
<![CDATA[Handoko Handoko]]>;
<![CDATA[Handri Santoso]]>
<![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.2026.7.4.5656
<![CDATA[Inclusive education requires integrated academic, social, and emotional support systems to address learner diversity; however, existing Educational Data Mining (EDM) research has largely concentrated on academic or log-based data, with limited attention to family-centered dynamics. This study aims to develop and validate a family-centered EDM framework to examine how the quality of parent–child interactions predicts students’ reflective learning behaviors. A cross-sectional survey was conducted with 150 junior and senior high school students from urban Indonesian schools using the Parent–Child Inclusivity Scale (PCIS) and the Self-Assessment Behavior Inventory (SABI). Data were analyzed using descriptive statistics, reliability testing, and Confirmatory Factor Analysis, followed by EDM techniques including Decision Tree and Naïve Bayes classification, K-Means clustering, and Apriori association rule mining. The classification models achieved accuracies of 82% and 78%, while clustering revealed three distinct inclusivity–reflection profiles. Association rules further identified key combinations of autonomy support, constructive feedback, and joint decision-making that consistently predict high levels of reflective behavior. By explicitly integrating family-centered variables into predictive, profiling, and rule-based EDM analyses, this study advances Informatics by proposing a novel family-centered EDM framework that bridges data analytics with inclusive educational practice and adaptive learning system design.]]>
<![CDATA[Improving Brain Tumor Classification In Mri Using Hybrid Mobilenetv2-Densenet121 With Cbam: English ]]>
<![CDATA[Jamilatul Muyasaroh]]>;
<![CDATA[Wiharto Wiharto]]>;
<![CDATA[Endra Pratama]]>
<![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.2026.7.4.5669
<![CDATA[Brain tumor classification from magnetic resonance imaging (MRI) images requires robust feature learning because tumor classes show subtle visual differences, especially between glioma and meningioma. This work improves accuracy and stability in multi-class brain tumor classification by enriching features and directing attention to discriminative MRI patterns. A hybrid model uses DenseNet121 and MobileNetV2 as parallel backbones, applies a Convolutional Block Attention Module (CBAM) to each stream, and performs late fusion by concatenating the two feature vectors for final prediction. CBAM improves performance by adaptively reweighting channels that encode tumor-relevant intensity and texture cues while suppressing less informative responses. It also strengthens spatial focus by emphasizing regions aligned with tumor cores and boundaries, reducing interference from normal tissue and artifacts and lowering cross-class confusion. Performance is evaluated with 10-fold cross-validation on the Figshare dataset and a standard hold-out split on the Sartaj Bhuvaji dataset. The proposed model achieves strong results on Figshare (3 classes) with accuracy 0.9892, precision 0.9880, recall 0.9876, F1-score 0.9877, and AUC 0.9995, and it also achieves strong results on Sartaj Bhuvaji (4 classes) with accuracy 0.9816, precision 0.9814, recall 0.9848, F1-score 0.9829, and AUC 0.9984. These results indicate that dual-backbone fusion increases feature diversity and CBAM reduces cross-class confusion, supporting stable generalization across independent MRI datasets. The model improves accuracy in separating visually similar tumors, especially glioma versus meningioma, and maintains strong generalization across different datasets.]]>
<![CDATA[Web-Based Expert System for Human Skin Disease Diagnosis Using Forward Chaining and Mamdani Fuzzy Inference ]]>
<![CDATA[Muhammad Khadafi]]>;
<![CDATA[Muhammad Faisal]]>;
<![CDATA[Muhyiddin AM Hayat]]>;
<![CDATA[Muh. Arief Muhsin]]>;
<![CDATA[Muhammad Syafaat S.Kuba]]>;
<![CDATA[Lukman Anas]]>;
<![CDATA[Andi Makbul Syamsuri]]>
<![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.2026.7.4.5676
<![CDATA[This paper aims to develop a web-based expert system aimed at assisting the general public and parties involved in the health sector, particularly in the field of human skin diseases. This system uses a combination of forward chaining and Mamdani fuzzy inference methods. The knowledge base is created based on observations and interviews with a dermatologist at Bima Regional General Hospital, covering 13 symptoms and 6 types of skin diseases, each equipped with expert confidence weights. Disease candidates are inferred from symptoms selected by the user and rules using forward chaining, while symptom weights are managed through fuzzy logic using the Laravel framework with the PHP language. From 20 test data, a comparison between the results from doctors and the system in an operational web application model shows a compatibility level of 95%. This system is suitable for use by doctors with further review. With this application, it is hoped that it can solve problems experienced by the community, such as limited access to dermatologists and economic problems, which are some of the main reasons why this application was designed and developed.]]>
<![CDATA[Laboratory Assistant Selection Using Fuzzy BWM and Fuzzy TOPSIS with Sensitivity Analysis and Monte Carlo Simulation ]]>
<![CDATA[Dwi Jatmiko]]>;
<![CDATA[Benni Purnama]]>;
<![CDATA[Effiyaldi Effiyaldi]]>;
<![CDATA[Nurhadi Nurhadi]]>
<![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.2026.7.4.5686
<![CDATA[This study addresses the challenge of laboratory assistant selection at Universitas Nurdin Hamzah (UNH) Jambi, where manual assessment can introduce subjectivity and limited transparency. To improve objectivity and traceability, this research develops a web-based Decision Support System (DSS) by integrating the Fuzzy Best–Worst Method (Fuzzy-BWM) to derive criteria weights and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to rank candidates. The evaluation uses four criteria: Competency Test (C1), Certification (C2), Grade Point Average (GPA) (C3), and Interview (C4), with assessments represented by linguistic fuzzy scales to accommodate uncertainty in human judgment. The system generates ranking outputs along with the Closeness Coefficient (CC) as an interpretable decision indicator. Robustness is further examined using sensitivity analysis and Monte Carlo simulation under varying criteria weights to evaluate ranking stability. Results show that the proposed DSS produces measurable and explainable rankings and provides additional evidence of decision robustness under weight perturbations. From an Informatics and Computer Science perspective, this work demonstrates the practical integration of fuzzy-MCDM algorithms into a reliable computerized DSS that supports transparent, reproducible, and auditable decision-making in academic operational management.]]>
<![CDATA[Comparative Evaluation of ResNet-50, MobileNetV2, and EfficientNet for Real-Time Exam Cheating Detection and Chatbot-Based Alert System ]]>
<![CDATA[Nanang Prihatin]]>;
<![CDATA[Herri Mahyar]]>;
<![CDATA[Muhammad Azzahari]]>;
<![CDATA[Muhammad Kahfi Aulia]]>
<![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.2026.7.4.5687
<![CDATA[Academic integrity during examinations remains a persistent challenge, as conventional human-based supervision often struggles to detect subtle cheating behaviors. This study proposes the development of a camera-based exam cheating detection system leveraging transfer learning with state-of-the-art Convolutional Neural Network (CNN) architectures. Three pretrained models—ResNet-50, MobileNetV2, and EfficientNet—were comparatively evaluated to identify suspicious gestures such as peeking at peers or using hidden notes. The first training phase utilized a publicly available dataset from Kaggle, where ResNet-50 outperformed the other models, achieving a validation accuracy of 98.7% with an F1-score of 0.987. To further assess robustness, a second training phase was conducted using a newly collected private dataset reflecting real exam scenarios. With only 10 epochs, ResNet-50 maintained strong generalization performance, reaching a test accuracy of 97.7%. These results highlight the consistency of ResNet-50 across different datasets and conditions. The selected model was subsequently integrated into a prototype application capable of real-time monitoring and instant notifications via chatbot, enabling timely intervention by exam supervisors. The findings underscore the critical role of model selection in real-time AI proctoring systems and provide a benchmarked, scalable solution that advances the field of computer vision-based academic integrity monitoring.]]>
