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
1,002 Documents
<![CDATA[MAnTra: A Transformer-Based Approach for Malware Anomaly Detection in Network Traffic Classification ]]>
<![CDATA[Rizal, Randi]]>;
<![CDATA[Darmawan, Muhamad Aditya]]>;
<![CDATA[Selamat, Siti Rahayu]]>;
<![CDATA[Rahmatulloh, Alam]]>;
<![CDATA[Haerani, Erna]]>;
<![CDATA[Tarempa, Genta Nazwar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5462
<![CDATA[Cybersecurity is a critical priority in the ever-evolving digital era, particularly with the emergence of increasingly sophisticated and difficult to detect malware. Traditional detection techniques, such as static and dynamic analysis, are often limited in their ability to recognize novel and concealed malware that poses a threat to security systems. Consequently, this study investigates the potential of Transformer models for network traffic classification to detect anomalies associated with malware activity. The proposed approach emphasizes retrospective analysis, wherein the model is evaluated across various platforms and datasets encompassing different virus variants. By incorporating diverse types of malwares into the training data, the model is better equipped to identify a range of attack patterns. The Transformer model employed in this study was trained over 30 epochs. The evaluation results demonstrated excellent performance, achieving a training accuracy of 99.16% and a test accuracy of 99.32%. The very low average loss value of 0.01 indicates that the model effectively reduces classification errors. These findings underscore the potential of Transformer models as an efficient method for malware detection, offering greater accuracy and speed compared to traditional approaches. The results further reveal that the Transformer exhibits strong capabilities in handling sequential data, which is highly relevant to the dynamic nature of network traffic. For future research, it is recommended to explore the scalability of this method in larger network environments and assess its effectiveness in real-time detection scenarios. Expanding its application could establish the Transformer model as a more reliable and efficient solution for identifying evolving malware threats, thereby enhancing overall network security. This approach presents a robust framework for protecting systems and data against increasingly complex cyber threats.]]>
<![CDATA[Optimization of Software Effort Estimation Using Hybrid Consistent Fuzzy Preference Relation and Least Squares Support Vector Machine ]]>
<![CDATA[Lestari, Ika Indah]]>;
<![CDATA[Purwanto, Adnan]]>;
<![CDATA[Sulistiyasni, Sulistiyasni]]>;
<![CDATA[Sambath, Khoem]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5465
<![CDATA[The success of software project management hinges on the ability to reliably forecast development effort. However, achieving precise estimates is notoriously difficult, primarily due to inherent project complexities and numerous uncertain variables. While various techniques exist, no single method has proven consistently reliable, leading to inaccurate scheduling and cost overruns. This study aims to develop a more accurate and robust estimation model by hybridizing a multi-criteria decision-making (MCDM) method for handling uncertainty with a machine learning algorithm for predictive modeling. The proposed approach integrates the Consistent Fuzzy Preference Relation (CFPR) method to derive consistent weights for cost drivers from expert judgments. These weights are then used as Effort Adjustment Factors (EAF) to preprocess the COCOMO and NASA datasets, which are subsequently modeled using the Least Squares Support Vector Machine (LSSVM). Evaluation of the hybrid CFPR-LSSVM model confirmed its enhanced predictive accuracy. For the COCOMO dataset, the model yielded an MMRE of 28.463% and an RMSE of 0.4705. Its performance on the NASA dataset was particularly remarkable, with results indicating an MMRE of 1.104% and an RMSE of 0.4593, demonstrating a level of precision that underscores the model's effectiveness. This research contributes a novel hybrid framework that effectively combines consistent fuzzy preference handling with powerful non-linear regression. By providing a more structured and robust methodology for managing uncertainty, this approach offers a substantial advancement in software effort estimation, delivering more reliable predictions for improved project planning. ]]>
<![CDATA[Deep Learning Rnn-Lstm Model For Forecasting Tourist Visits In Yogyakarta Using Bps Time-Series Data ]]>
<![CDATA[Munir, Agus Qomaruddin]]>;
<![CDATA[Wardani, Ratna]]>;
<![CDATA[Setiyawan, Ramadhana]]>;
<![CDATA[Mustofa, Zaenal]]>;
<![CDATA[Nurkhamid, Nurkhamid]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5467
<![CDATA[Tourism is a crucial sector in Indonesia's economic growth, particularly in Yogyakarta, contributing significantly to revenue, job creation, and infrastructure development. However, the COVID-19 pandemic has significantly impacted the tourism industry, making tourist arrival forecasting crucial for effective government policy decision-making. This study aims to predict tourist arrivals in Yogyakarta using deep learning models, specifically the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms, chosen for their ability to process time series data and address non-linearity issues. Tourist arrival data from the Yogyakarta Central Statistics Agency (BPS) was used to train and test the model. Model evaluation was conducted using the Root Mean Squared Error (RMSE) metric to measure prediction accuracy. The results show that this model can accurately predict tourist arrival patterns, which can support strategic decision-making regarding the procurement of tourism facilities in Yogyakarta. The impact of this research is to provide practical benefits for local governments and tourism industry players in planning tourism promotion and management strategies. With more accurate predictions, relevant parties can prepare necessary resources and optimize tourism services according to projected visitor numbers.]]>
<![CDATA[Improving the Accuracy of Stunting Prediction in Children in Pagar Alam City Using XGBoost Feature Selection and K-Nearest Neighbor Classification ]]>
<![CDATA[Putrawansyah, Ferry]]>;
<![CDATA[Idris, Mohd. Yazid]]>;
<![CDATA[Febriansyah, Febriansyah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5473
<![CDATA[Stunting remains a major public health concern in Indonesia, including in Pagar Alam City. Early identification of at-risk children is essential to enable timely interventions and reduce long-term developmental consequences. However, predictive models such as K-Nearest Neighbor (K-NN) often experience reduced accuracy when faced with irrelevant features and imbalanced class distributions. This study integrates feature selection using Extreme Gradient Boosting (XGBoost) to enhance the predictive performance of K-NN in assessing stunting risk. Child growth data obtained from local health facilities were analyzed to build an initial baseline model, which exhibited limited accuracy due to excessive attributes and class imbalance. Through feature-importance analysis, XGBoost identified key predictors including sex, age, weight, and height. The optimized dataset was then used to retrain the K-NN model. Evaluation using accuracy, precision, recall, and F1-score demonstrated an improvement in accuracy from 85.63% to 93.72%. Beyond the computational results, this research provides significant contributions to the field of health informatics. The integration of XGBoost and K-NN offers an efficient analytical mechanism suitable for clinical decision support systems, particularly for data-driven screening in primary healthcare settings. The optimized, lightweight model can be embedded into health information systems to support child growth monitoring, strengthen evidence-based policymaking, and assist healthcare workers in targeting interventions more effectively. This approach can be replicated across other regions, supporting nationwide efforts to reduce stunting prevalence.]]>
<![CDATA[Enhancing Classification of Self-Reported Monkeypox Symptoms on Social Media Using Term Frequency-Inverse Document Frequency Features and Graph Attention Networks ]]>
<![CDATA[Rizian, Rizailo Akfa]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Faisal, Mohammad Reza]]>;
<![CDATA[Kartini, Dwi]]>;
<![CDATA[Indriani, Fatma]]>;
<![CDATA[Ahmad, Umar Ali]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5482
<![CDATA[Early detection of infectious diseases plays a crucial role in minimizing their spread and enabling timely intervention. In the digital era, social media has emerged as a valuable source of real-time health information, where individuals often share self-reported symptoms that can serve as early warning signals for disease outbreaks. However, textual data from social media is typically unstructured, noisy, and contextually diverse, posing challenges for conventional text classification methods. This study proposes a hybrid model combining Term Frequency–Inverse Document Frequency (TF-IDF) feature representation with a Graph Attention Network (GAT) to enhance the early detection of Monkeypox-related self-reported symptoms on Indonesian social media. A dataset of 3,200 tweets was collected through Tweet-Harvest and subsequently preprocessed and manually labeled, producing a balanced distribution between positive (51%) and negative (49%) samples. TF-IDF vectors were used to construct a document similarity graph via the k-Nearest Neighbors (k-NN) method with cosine similarity, enabling GAT to leverage both textual and relational information across posts. The model’s performance was evaluated using accuracy, precision, recall, and macro-F1, with macro-F1 serving as the primary indicator. The proposed TF-IDF + GAT model achieved 93.07% accuracy and a macro-F1 score of 93.06%, outperforming baseline classifiers such as CNN (92.16% macro-F1), SVM (85.73%), Logistic Regression (84.89%). These findings demonstrate the effectiveness of integrating classical text representations with graph-based neural architectures for improving social media based disease surveillance and supporting early epidemic response strategies.]]>
<![CDATA[Comparative Sentiment Analysis of YouTube Comments on Indonesia's Electric Vehicle Incentive Policy Using TF-IDF and IndoBERTweet Models ]]>
<![CDATA[Chairat, Arief Suardi Nur]]>;
<![CDATA[Rizal, Randi]]>;
<![CDATA[Himawan, Hidayatulah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5499
<![CDATA[Indonesia’s battery electric vehicle (KBLBB) incentives aim to accelerate low-carbon mobility, yet public reactions regarding affordability, charging infrastructure readiness, and subsidy equity remain highly heterogeneous. This research systematically compares classical machine learning and transformer-based models for classifying sentiment in 1,516 YouTube comments discussing the incentive policy and broader EV ecosystem. Comments are collected via web scraping and processed through filtering, case folding, normalization, tokenization, stopword removal, stemming, lexicon-based sentiment labelling, TF-IDF bigram vectorization, random oversampling, and hyperparameter optimization with GridSearch. Support Vector Machine and Random Forest serve as baseline models, while Logistic Regression with TF-IDF bigram and IndoBERTweet represent advanced approaches that exploit richer feature representations. Results show that the baseline models achieve around 65–66% accuracy, Logistic Regression improves performance to 88%, and IndoBERTweet attains the highest accuracy of 94% with balanced precision, recall, and F1-score across sentiment classes. Sentiment distribution indicates that 63.3% of comments are negative, dominated by concerns over limited charging networks, high upfront costs, and perceived unfairness of public subsidies, while 36.7% of comments highlight support for cleaner transportation, technological innovation, and national industrial competitiveness. These findings demonstrate that transformer-based contextual embeddings substantially enhance sentiment classification for noisy Indonesian social media text and provide a scalable informatics tool for continuous monitoring of EV policy reception. The resulting empirical evidence can inform more targeted refinements of incentive design, infrastructure planning, and communication strategies, thereby supporting inclusive, data-driven, and sustainable KBLBB adoption across diverse demographic groups and evolving policy scenarios nationwide over time.]]>
<![CDATA[Complex Word Identification in Indonesian Children’s Texts: An IndoBERT Baseline and Error Analysis ]]>
<![CDATA[Lisnawita, Lisnawita]]>;
<![CDATA[Bakar, Juhaida Abu]]>;
<![CDATA[Rasli, Ruziana Mohamad]]>;
<![CDATA[Costaner, Loneli]]>;
<![CDATA[Guntoro, Guntoro]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5501
<![CDATA[Complex Word Identification (CWI) is a crucial step for building text simplification systems, especially for Indonesian children’s reading materials where unfamiliar vocabulary can hinder comprehension. This study formulates token-level CWI for Indonesian children’s texts and establishes two baselines: an interpretable rule-based model using linguistic features e.g., length, syllable heuristics, and affix patterns, and an IndoBERT model fine-tuned for token classification. This study construct and annotate a children’s text corpus and evaluate both approaches using standard classification metrics. On the test set (22.584 tokens), IndoBERT achieves an F1-score of 0.9972 for the CWI class, substantially outperforming the rule-based baseline (F1 = 0.8607). The IndoBERT system makes only 39 errors (23 false positives and 16 false negatives), indicating near-perfect performance under the evaluated setting. Furthermore, this study provides an error analysis to highlight remaining failure patterns and borderline cases that are difficult even for contextual models. The resulting benchmark and findings contribute to Informatics/Computer Science by providing a strong baseline and analysis for educational NLP in a low-resource language setting, supporting the development of Indonesian child-oriented NLP resources and downstream text simplification tools.]]>
<![CDATA[Fault-Tolerant Telegram Bot Architecture for Odoo 14: Validated Production Reporting in Flexible Packaging ]]>
<![CDATA[Tarigan, Masmur]]>;
<![CDATA[Paramita, Adi Suryaputra]]>;
<![CDATA[Dewi, Deshinta Arrova]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5515
<![CDATA[In flexible-packaging manufacturing, manual reporting dramatically delays synchronization with the ERP — and that means operational latency and traceability issues. The proposed work is the design, implementation, and validation of a fault-tolerant Telegram bot interconnected with Odoo 14 for six production departments. Our bot architecture that combines conversational workflows with schema-based validation and XML-RPC for slow, large payloads, enables accurate and timely reporting. In a four-week pilot with 1,066 production entries, we achieved 98.7% field completeness and lowered reporting latency to less than 2 minutes. Manual baselines received 75% more requests for corrections. At disconnected state, the layered middleware of the system abstracted retry logic and media ingestion. Both SDG 9 (Resilient infrastructure, including ) and SDG 12 (Continue to reduce production waste at source, including consumables) are connected to the work presented here which evidence the feasibility of automatic conversational interfaces with a computer in the manufacturing informatics domain, and provide pathways towards scalable digital transformation and sustainability in the small-to-medium industry sector.]]>
<![CDATA[Evaluating Lexicon Weighting and Machine Learning Models for Sentiment Classification of Indonesian Mangrove Ecotourism Reviews ]]>
<![CDATA[Chahyadi, Ferdi]]>;
<![CDATA[Uperiati, Alena]]>;
<![CDATA[Pratiwi , Risdy Absari Indah]]>;
<![CDATA[Hamid, Nur]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5563
<![CDATA[Sentiment analysis on ecotourism reviews presents specific challenges due to descriptive writing styles, the use of ambiguous words, and contextual meaning shifts (contextual polarity shift). These characteristics often cause lexicon-based approaches to produce unstable polarity labels. This study aims to evaluate the influence of two lexicon weighting methods, namely Mean Weighting and Summation Weighting, on the initial sentiment labeling of mangrove ecotourism reviews and to assess the performance of machine learning models trained using these labels. The research method includes text preprocessing, lexicon-based scoring using the InSet lexicon, feature extraction with Term Frequency–Inverse Document Frequency (TF–IDF), and the training of two classification algorithms, Support Vector Machine (SVM) and Logistic Regression (LR). The results show that the Mean Weighting method produces more stable polarity scores and higher model performance. The combination of SVM with Mean Weighting achieves the best results with an accuracy of 0.902, macro precision of 0.876, macro recall of 0.819, a macro F1-score of 0.841, and a weighted F1-score of 0.899. Meanwhile, LR with Mean Weighting reaches an accuracy of 0.891 with a similar performance pattern. In contrast, the Summation Weighting method results in lower performance for both algorithms. Error analysis indicates that neutral sentences and ambiguous words such as “bagus” and “ramai” frequently lead to misclassification. These findings highlight that the choice of lexicon weighting method plays a crucial role in improving sentiment classification accuracy and contributes to the development of hybrid approaches in text mining and sentiment analysis for the Indonesian language.]]>
<![CDATA[Comparative Analysis of RGB and Grayscale Pixel-Based Similarity Methods for Lung X-ray Image Retrieval in Clinical Decision Support Systems ]]>
<![CDATA[Widiyanto, Wahyu Wijaya]]>;
<![CDATA[Nizam Husen, Mohd]]>;
<![CDATA[Pariyasto, Sofyan]]>;
<![CDATA[Susanto, Edy]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5570
<![CDATA[Chest X-ray imaging is widely used to support the diagnosis of lung diseases, yet many automated similarity techniques still rely on RGB formats, which differ from the grayscale images commonly used in clinical systems. This discrepancy raises the question of whether color information is necessary for effective similarity assessment. This study aims to evaluate the performance of RGB and grayscale pixel-based similarity methods for lung X-ray analysis and determine whether grayscale images can provide comparable similarity performance with lower computational demands. A total of 300 chest X-ray images representing normal, pneumonia, and COVID-19 categories were processed in both formats. Pixel-level similarity was calculated across 30,000 image pairings, followed by statistical testing to assess differences between methods. The results show that grayscale similarity scores closely match those of RGB, with variations generally below 0.3%. A meaningful difference was observed only in the comparison between normal and COVID-19 images, indicating that RGB may capture subtle visual variations not present in grayscale. Overall, this study demonstrates that grayscale pixel-based similarity analysis provides a reliable and computationally efficient approach, contributing to the development of lightweight medical image retrieval and clinical decision support systems in the field of health informatics.]]>
