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
1,048 Documents
<![CDATA[Comparative Analysis of ArUco Marker Detection Techniques Using Adaptive Thresholding, CLAHE, and Kalman Filter for Smart Cane Applications ]]>
<![CDATA[Yulianto, Koko Edy]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Utomo, Fandy Setyo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4883
<![CDATA[This study aims to analyze and compare the effectiveness of three image processing techniques Adaptive Thresholding, CLAHE, and Kalman Filter in enhancing the performance of ArUco marker detection for a smart cane system designed for visually impaired individuals at SLB Kuncup Mas Banyumas. The evaluation method includes detection accuracy, marker position precision, and computational time required by each technique under two different lighting conditions: daytime and nighttime. The results show that all three image processing techniques successfully achieved a 100% detection accuracy for ArUco markers. However, significant differences were observed in computational time, with Kalman Filter demonstrating the fastest processing speed, making it the most efficient option for real-time applications requiring quick response. CLAHE and Adaptive Thresholding performed better in uneven lighting conditions, although they required longer computational times. Kalman Filter is therefore recommended for marker-based navigation systems in environments demanding fast response times, while CLAHE and Adaptive Thresholding are better suited for settings with variable lighting intensities. The implications of these findings open opportunities for developing adaptive navigation systems capable of dynamically adjusting image preprocessing methods based on real-time environmental conditions. This study contributes practically to the advancement of assistive navigation technologies for visually impaired individuals, particularly in the development of visual marker-based detection systems. The results also provide a useful guideline for selecting appropriate image processing techniques according to environmental characteristics, thereby improving the accuracy and adaptability of navigation systems across diverse lighting conditions and operational environments.]]>
<![CDATA[A Hybrid SOAR-BSC-AHP Framework for Strategy Selection in Digital Cultural Tourism ]]>
<![CDATA[Kurniawan, Rahadian]]>;
<![CDATA[Kusumadewi, Sri]]>;
<![CDATA[Sujarwo, Ari]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4886
<![CDATA[Digital transformation in cultural tourism presents significant challenges, particularly in heritage villages like Kotagede, Yogyakarta. Problems such as limited infrastructure, low digital literacy, and the absence of a structured planning framework hinder progress toward community-based sustainable tourism development. This study addresses these challenges by proposing an integrated decision-making framework that combines SOAR analysis, the Balanced Scorecard (BSC), and the Analytic Hierarchy Process (AHP). The SOAR-BSC framework captures strategic objectives from qualitative data through focus group discussions and stakeholder interviews, while the AHP quantitatively prioritizes eight strategic alternatives based on hierarchical criteria and subcriteria. The most impactful strategies identified were: (1) developing partnerships with tour operators, and (2) promoting community cultural education and training. The Learning and Growth Perspective emerged as the most influential factor (weight = 0.5549), highlighting the importance of community empowerment and digital skills development. Sensitivity analysis and cross-validation using the Simple Additive Weighting (SAW) method confirmed the consistency and robustness of the rankings. In practice, this framework offers a participatory, data-driven guide for digital transformation in heritage tourism, supporting not only improved destination management but also long-term cultural preservation through inclusive digital initiatives.]]>
<![CDATA[A Comprehensive Benchmarking Pipeline for Transformer-Based Sentiment Analysis using Cross-Validated Metrics ]]>
<![CDATA[Abidin, Dodo Zaenal]]>;
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Toscany, Afrizal Nehemia]]>;
<![CDATA[Nurhadi, Nurhadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4894
<![CDATA[Transformer-based models have significantly advanced sentiment analysis in natural language processing. However, many existing studies still lack robust, cross-validated evaluations and comprehensive performance reporting. This study proposes an integrated benchmarking pipeline for sentiment classification on the IMDb dataset using BERT, RoBERTa, and DistilBERT. The methodology includes systematic preprocessing, stratified 5-fold cross-validation, and aggregate evaluation through confusion matrices, ROC and precision-recall (PR) curves, and multi-metric classification reports. Experimental results demonstrate that all models achieve high accuracy, precision, recall, and F1-score, with RoBERTa leading overall (94.1% mean accuracy and F1), followed by BERT (92.8%) and DistilBERT (92.1%). All models exceed 0.97 in ROC-AUC and PR-AUC, confirming strong discriminative capability. Compared to prior approaches, this pipeline enhances result robustness, interpretability, and reproducibility. The provided results and open-source code offer a reliable reference for future research and practical deployment. This study is limited to the IMDb dataset in English, suggesting future work on multilingual, cross-domain, and explainable AI integration.]]>
<![CDATA[Development of a Distributed Gradient Boosting Forest Algorithm with Residual Connections in Data Classification ]]>
<![CDATA[Respati, Rayhan Dhafir]]>;
<![CDATA[Soim, Sopian]]>;
<![CDATA[Fadhli, Mohammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4899
<![CDATA[The growing complexity and volume of data across various domains necessitate machine learning models that are scalable and robust for large-scale classification tasks. Ensemble methods such as Gradient Boosting Decision Trees (GBDT) demonstrate effectiveness; however, they encounter issues concerning scalability and training stability when applied to very deep architectures. This work presents a novel enhancement using residual connections derived from deep neural networks into the Distributed Gradient Boosting Forest (DGBF) algorithm. By enabling direct gradient propagation across layers, residual connections solve the vanishing gradient problem and so improve gradient flow, accelerate convergence, and stabilise the training process. The Residual DGBF model was assessed using seven distinct datasets across the domains of cybersecurity, financial fraud, phishing, and malware detection. The Residual DGBF consistently surpassed the baseline DGBF in terms of accuracy, precision, recall, and F1-score across all datasets. Particularly in datasets marked by imbalanced classes and complex feature interactions, this suggests improved generalisation and higher predictive accuracy. By proving more stable and strong gradients across the depth of the model, layer-wise gradient magnitude analysis supports these improvements and so confirms the effectiveness of residual connections in deep ensemble learning. This work improves ensemble techniques by combining the scalability and interpretability of decision tree ensembles with the residual architecture optimising benefits. The proposed Residual DGBF enables future research on enhanced deep boosting frameworks by offering a strong and scalable method to address challenging real-world classification tasks.]]>
<![CDATA[Mosquito Species Classification Using Wingbeat Acoustic Signals Based on Bidirectional Long Short-Term Memory ]]>
<![CDATA[Dwifani, Bella Melati Wiranur]]>;
<![CDATA[Kasyidi, Fatan]]>;
<![CDATA[Ilyas, Ridwan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4922
<![CDATA[The increasing prevalence of mosquito-borne diseases such as Dengue, chikungunya, and malaria underscores the urgent need for effective mosquito vector monitoring. This study proposes a non-invasive classification system of mosquito species based on wingbeat acoustic signals using a deep learning approach with Bidirectional Long Short-Term Memory (BiLSTM). The audio dataset was collected from the Wingbeats repository, consisting of six major mosquito species. Preprocessing was performed using Discrete Wavelet Transform (DWT) to reduce noise. Feature extraction combined Linear Predictive Coding (LPC) and Mel-Spectrogram to represent spectral and temporal signal characteristics. Each binary model was trained in a one-vs-rest scheme to recognize a target species against others, and a BaggingClassifier was used to fuse predictions from six BiLSTM models. Evaluation showed that the proposed system achieved a final accuracy of 96.85% and F1-score of 95.03%, with confusion matrices showing near-diagonal performance. The results indicate that the hybrid LPC-Mel features and ensemble BiLSTM architecture are effective for mosquito species classification using acoustic signals.]]>
<![CDATA[Visual Interpretation of Machine Learning Models (Random Forest) for Lung Cancer Risk Classification Using Explainable Artificial Intelligence (SHAP & LIME) ]]>
<![CDATA[Rosyid, Irwan Fathur]]>;
<![CDATA[Pramaditya, Himawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4925
<![CDATA[Lung cancer remains one of the most prevalent and burdensome cancers worldwide, with delayed diagnosis being a persistent challenge—particularly in Indonesia, where no national screening program currently exists. In this collaborative study, we aim to develop an interpretable machine learning model for classifying lung cancer risk levels using the Explainable Artificial Intelligence (XAI) approach. The CRISP-DM framework was applied, and the dataset underwent cleaning, feature selection, labeling, and transformation, resulting in 152 valid entries. Tree ensemble algorithms—XGBoost, Random Forest, and LightGBM—were used, with Random Forest achieving the best performance at 97.38% accuracy. SHAP and LIME methods were integrated to provide transparent visual interpretations. A web-based system was developed using Streamlit, incorporating these visualizations and automated narrative summaries generated by a language model to assist non-technical users. A simulated case based on a published pediatric lung cancer report was used to demonstrate its interpretability and illustrate its potential applicability in clinical workflows. The proposed system offers an interpretable and scalable solution for early lung cancer risk classification, which may enhance decision support in primary care and promote trust in AI-assisted diagnostics.]]>
<![CDATA[DEGREE: Development and Validation of a User Experience Model for Digital Educational Games Using Cronbach’s Alpha and Fuzzy Logic ]]>
<![CDATA[Kurniawan, Mei Parwanto]]>;
<![CDATA[Suyanto, M.]]>;
<![CDATA[Utami, Ema]]>;
<![CDATA[Kusrini, Kusrini]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4942
<![CDATA[The rapid growth of digital educational games demands an evaluation model that accurately captures user experience and adopts a human-centred approach. This study introduces DEGREE (Digital Educational Game Review and Evaluation Engine), an enhanced model extending MEEGA+ by incorporating two previously underrepresented dimensions: Control and Feedback. Using a quantitative approach, questionnaires were distributed to high school students who actively use Minecraft and Duolingo, yielding 4800 responses.Reliability analysis via Cronbach’s Alpha revealed that the Player Experience + Control combination achieved the highest score (α = 0.914), while the inclusion of Feedback reduced reliability (α = 0.864), leading to its exclusion in the final model. The DEGREE model consists of two core domains: Usability (Aesthetics, Learnability, Operability, Accessibility) and Player Experience (Focused Attention, Fun, Challenge, Social Interaction, Confidence, Relevance, Satisfaction, Perceived Learning, User Error Protection, Control). Evaluation scores were calculated using the Fuzzy Weighted Average (FWA) method and Mean of Maximum (MoM) defuzzification. The Control dimension emerged as the most influential (0.2735), followed by Fun (0.2664) and Satisfaction (0.2516), highlighting the significance of user agency in digital learning environments. The DEGREE model offers a statistically robust and user-oriented framework for evaluating educational games, delivering actionable insights for developers and educators to design more effective and engaging digital learning experiences. This study contributes a new validated and generalizable evaluation framework that strengthens the theoretical foundation of user experience assessment in educational game design.]]>
<![CDATA[Rainfall Forecasting Using SSA-Based Hybrid Models with LSSVR and LSTM for Disaster Mitigation ]]>
<![CDATA[Ruslana, Zauyik Nana]]>;
<![CDATA[Zuliarso, Eri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4963
<![CDATA[Accurate rainfall forecasting is crucial for addressing the increasing risk of hydrometeorological disasters, particularly in tropical regions such as Semarang City, Indonesia. However, conventional forecasting models often struggle with inaccurate data and observations. This study proposes a novel hybrid combination of SSA-NMF with LSSVR and LSTM, offering high-resolution rainfall forecasting over multiple monitoring stations, to predict daily rainfall. As a preprocessing step, 15 years of daily rainfall data from six observation stations were denoised and decomposed using Singular Spectrum Analysis (SSA) combined with Non-Negative Matrix Factorization (NMF). This approach effectively handled data with many zero values, identified seasonal patterns or high-rainfall locations, and extracted key patterns. The prediction models were trained and validated using parameters optimized through RandomizedSearchCV for LSSVR and Keras Tuner for LSTM. Model performance was evaluated using MSE, RMSE, MAE, and Nash-Sutcliffe Efficiency (NSE). The results showed that the SSA-LSTM model consistently outperformed SSA-LSSVR model, with the highest average NSE value being 0.9 across six monitoring locations in Semarang City. Furthermore, the predicted rainfall values were spatially visualized using Inverse Distance Weighting (IDW) interpolation within a Geographic Information System (GIS) environment, producing informative rainfall distribution maps that support early warning systems and disaster mitigation efforts. In conclusion, the hybrid approach combining SSA-NMF preprocessing with LSTM-based deep learning significantly improves the accuracy and reliability of daily rainfall forecasting. This novel SSA‑NMF + LSSVR/LSTM framework delivers high‑resolution, reliable rainfall forecasts that directly empower disaster risk reduction systems and readily transfer to similar climatic regions.]]>
<![CDATA[Comparison of LightGBM With XGBoost Algorithms in Determining Arrhythmia Classification in Students ]]>
<![CDATA[Sitanggang, Delima]]>;
<![CDATA[Wilbert Solo, Eddrick]]>;
<![CDATA[Immanuel Sinaga, Ferdy]]>;
<![CDATA[Jorgi L.Tobing, Stefanus]]>;
<![CDATA[Hutasoit, Feliks Daniel]]>;
<![CDATA[Prabowo, Agung]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5015
<![CDATA[Arrhythmia is a heart rhythm disorder that may occur unpredictably with life-threatening risk if it were not treated immediately. This heart disorder generally affects the elderly, but symptoms of this disorder can also arise in children and adolescents, especially for those with heart problems or are often under stress. The implementation of this research is aimed at analyzing the symptoms of early arrhythmia in adolescent children using electrocardiogram signals. In order to obtain the best possible results in determining the higher performing algorithm, two machine learning methods were used to predict the classification of arrhythmia which will be compared for their accuracy. The subjects of this study included 106 students from SMK Swasta Teladan Sumatera Utara 2 located in the city of Medan, of which 72 final subject data were used to train the capability of both models used to predict arrhythmia classification categorized into four categories, namely normal, abnormal, potential of arrhythmia, and high potential of arrhythmia. The LightGBM model outperformed the XGBoost model, with 95.11% accuracy and 95.03% F1 Score, and although the loss value of the LightGBM model is higher than the loss value of the XGBoost model, the difference between these two values is negligible and the loss value of LightGBM can be considered as excellent with a value of 0.1503. This research contributes to the advancement of digital health by demonstrating the potential of machine learning-based ECG analysis for highly accurate early arrhythmia detection in adolescent, non-clinical populations.]]>
<![CDATA[Comparison of ANOVA and Chi-Square Feature Selection Methods to Improve Machine Learning Performance in Anemia Classification ]]>
<![CDATA[Annisa, Tiko Nur]]>;
<![CDATA[Jasmir , Jasmir]]>;
<![CDATA[Nurhadi , Nurhadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5017
<![CDATA[Anemia is a prevalent hematological condition marked by decreased hemoglobin concentration in the blood, which can lead to serious health complications if undetected. Although machine learning has shown potential in supporting early diagnosis, its effectiveness is often hindered by irrelevant or excessive features. This study investigates the impact of ANOVA and Chi-Square feature selection methods in improving the effectiveness of three distinct machine learning models algorithms, Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) for anemia classification. Using a Kaggle dataset consisting of 15,300 instances and 25 features, the evaluation of each model was conducted with reference to its accuracy, precision, recall, and F1-score, both before and after applying feature selection. Experimental results show a substantial improvement in classification performance after feature selection, with the SVM + ANOVA combination achieving the highest accuracy of 94.61%. In contrast, models without feature selection performed below 90%, highlighting the need for appropriate feature reduction techniques. This study contributes a comparative analysis framework for medical data classification, emphasizing the role of statistical feature selection in optimizing model accuracy. Its novelty lies in demonstrating consistent performance improvement across algorithms using real-world anemia data and providing evidence that ANOVA and Chi-Square can significantly enhance model generalization in medical diagnostic contexts.]]>
