Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Articles
962 Documents
<![CDATA[Development of a Church Information Management System Using Scrum at HKBP Sola Gratia Kayu Mas Jakarta ]]>
<![CDATA[Siregar, Master Edison]]>;
<![CDATA[Mayatopani, Hendra]]>;
<![CDATA[Olivia, Deasy]]>;
<![CDATA[Kurniawan, Rido Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.4979
<![CDATA[The rapid growth of the congregation at HKBP Sola gratia Kayu Mas Church in Jakarta has posed challenges in managing member data efficiently and effectively. The previous data management system, which relied on Microsoft Excel, showed significant limitations in data retrieval, family grouping, and presenting birthday or elderly member information. This study aims to develop a web-based church congregation management information system using the Scrum methodology as an iterative and flexible software development approach. The research methodology includes observation, interviews, literature review, and black box testing. The results indicate that the developed system successfully meets user needs, simplifies congregation data management, and enhances the effectiveness of church administrative services. The implementation of Scrum has proven to be effective in accelerating development processes, accommodating changing requirements, and increasing user involvement through continuous evaluation. This system is expected to be replicable in other churches with similar needs as an integrated digital solution for congregation management.]]>
<![CDATA[Sentiment Analysis and Topic Modeling for Discovering Knowledge in Indonesian Mobile Government Applications ]]>
<![CDATA[Hamid, Ricky Bahari]]>;
<![CDATA[Andriansyah, Chandra]]>;
<![CDATA[Sensuse, Dana Indra]]>;
<![CDATA[Lusa, Sofian]]>;
<![CDATA[Elisabeth, Damayanti]]>;
<![CDATA[Safitri, Nadya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.4991
<![CDATA[The accelerated rate of government applications development in Indonesia has introduced new opportunities and challenges in delivering digital public services. While thousands of apps have been developed, systemic issues ranging from usability flaws to authentication failures persist, as reflected in user reviews on platforms like the Google Play Store. This study adopts a knowledge discovery approach to extract actionable insights from more than 17,000 user-generated reviews across three major government applications: Satusehat, Digital Korlantas, and M-Paspor. A hybrid methodology is applied, combining RoBERTa-based sentiment classification, BERTopic-based topic modeling, cosine similarity analysis, and qualitative user validation. The findings reveal recurring issues in authentication, interface design, and system responsiveness that span across organizational boundaries. Cross-app topic correlation highlights critical shared pain points such as login failures and unintuitive UI that undermine user trust in e-government services. Mapping these insights onto the SECI knowledge management model, this research contributes both practical recommendations and a replicable analytical framework for public agencies seeking to institutionalize user feedback. By transforming fragmented digital feedback into organizational knowledge, this study supports continuous service improvement and strengthens the foundation for user-centric e-government.]]>
<![CDATA[Sentiment Analysis Of Indihome Service Based On Geo Location Using The Bert Model On Platform X ]]>
<![CDATA[Siregar, Robiatul Adawiyah]]>;
<![CDATA[Fitriyani, Fitriyani]]>;
<![CDATA[Darfiansa, Lazuardy Syahrul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.4993
<![CDATA[The rapid growth of internet usage in Indonesia has led more people to express their feelings, whether positive or negative, about online services, including IndiHome, through social media platforms such as X (formerly Twitter). This study aims to analyze public sentiment toward IndiHome services based on geographic location using the IndoBERT natural language processing model. The data consists of 3.307 Indonesian tweets that are geo-tagged and categorized into three sentiment groups: good, okay, and bad. The research process involves collecting the data, cleaning it (organizing and splitting words), and testing the IndoBERT model with a confusion matrix and classification scores. The findings reveal that negative feelings are more prevalent in most locations, especially in Java. The IndoBERT model achieved its highest accuracy of 80% in detecting negative sentiment. However, there is still room for improvement in distinguishing between positive and neutral sentiments, possibly due to data imbalance. The study shows how combining sentiment analysis with geo-location can provide strategic insights to service providers. In practical terms, these insights can help IndiHome prioritize infrastructure upgrades, improve customer support in areas with high dissatisfaction, and assist policymakers in promoting fairer digital access across regions. Beyond these practical implications, this study also contributes to the field of informatics by demonstrating the application of a transformer-based NLP model (IndoBERT) combined with geo-tagged data for regional sentiment mapping- a relatively unexplored approach in the Indonesian context. The integration of geospatial analysis with sentiment classification offers methodological advances for NLP-based service evaluation beyond business applications.]]>
<![CDATA[Comparative Analysis of LSTM and GRU for River Water Level Prediction ]]>
<![CDATA[Faris, Fakhri Al]]>;
<![CDATA[Taqwa, Ahmad]]>;
<![CDATA[Handayani, Ade Silvia]]>;
<![CDATA[Husni, Nyayu Latifah]]>;
<![CDATA[Caesarendra, Wahyu]]>;
<![CDATA[Asriyadi, Asriyadi]]>;
<![CDATA[Novianti, Leni]]>;
<![CDATA[Rahman, M. Arief]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5054
<![CDATA[Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management.]]>
<![CDATA[Brain Tumor Auto Segmentation On 3D MRI Using Deep Neural Network ]]>
<![CDATA[Agarina, Melda]]>;
<![CDATA[Maulana, Muh Royan Fauzi]]>;
<![CDATA[Sutedi, Sutedi]]>;
<![CDATA[Karim, Arman Suryadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5106
<![CDATA[Accurate and automated segmentation of brain tumours from Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and treatment planning, yet it remains a significant challenge due to tumour heterogeneity and data imbalance. This research investigation examines the effectiveness of a 3D UNet architecture for the segmentation of brain tumours utilizing MRI imaging modalities. The research employs the BRATS 2021 dataset, which consists of 675 MRI datasets across four distinct imaging modalities (FLAIR, T1-Weighted, T1-Contrast, and T2-Weighted) and encompasses four distinct segmentation label classes. The employed model integrated soft dice loss and dice coefficient as its loss functions, with the objective of achieving convergence despite the presence of imbalanced data. While constraints related to resources limited the training process, the model yielded promising outcomes, exhibiting high accuracy (99.43%) and specificity (99.5%), The model aids medical professionals in understanding tumor growth and enhances treatment planning via segmentation predictions in surgery. Nevertheless, the sensitivity, particularly concerning non-enhancing tumour classes, persists as a significant challenge, underscoring the necessity for future research to concentrate on data-centric methodologies and enhanced pre-processing techniques to improve model efficacy in critical medical applications such as the segmentation of brain tumours.]]>
<![CDATA[Digital Landscape and Behavior in Indonesia 2024: A National Survey Analysis of Internet Penetration, Cybersecurity Risks, and User Segmentation Using K-Means Clustering and Logistic Regression ]]>
<![CDATA[Aminudin, Nur]]>;
<![CDATA[Hidayat, Nurul]]>;
<![CDATA[Feriyanto, Dwi]]>;
<![CDATA[Septasari, Dita]]>;
<![CDATA[Awaliyani, Ikna]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5117
<![CDATA[Digital transformation in Indonesia reveals significant disparities in internet access, digital behavior, and cybersecurity vulnerabilities. This study analyzes the digital landscape using national survey data from 8,720 respondents across 38 provinces. This research employs a quantitative approach, utilizing chi-square tests, logistic regression for risk analysis, and K-Means clustering for user segmentation, supported by Principal Component Analysis (PCA) for dimensionality reduction. The results show a national internet penetration rate of 79.5%, with significant disparities across regions and socio-economic segments. Logistic regression analysis reveals that higher education, greater income, and the use of fixed broadband are negatively correlated with cybersecurity risks. Furthermore, K-Means clustering identifies three distinct user profiles: 'Digital Savvy', 'Pragmatic Users', and the 'Vulnerable Segment', each with unique characteristics regarding digital access and literacy. This research provides a critical empirical basis for understanding digital transformation in a developing nation. The findings underscore the necessity of data-driven, segmented policies to foster digital inclusion and enhance national cybersecurity, offering actionable insights for policymakers and service providers.]]>
<![CDATA[Hybrid Model for Speech Emotion Recognition using Mel-Frequency Cepstral Coefficients and Machine Learning Algorithms ]]>
<![CDATA[Nurdiawan, Odi]]>;
<![CDATA[Ade Kurnia, Dian]]>;
<![CDATA[Sudrajat, Dadang]]>;
<![CDATA[Pratama, Irfan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5143
<![CDATA[Speech Emotion Recognition (SER) is a subfield of affective computing that focuses on identifying human emotions through voice signals. Accurate emotion classification is essential for developing intelligent systems capable of interacting naturally with users. However, challenges such as background noise, overlapping emotional features, and speaker variability often reduce model performance. This study aims to develop a lightweight hybrid SER model by combining Mel-Frequency Cepstral Coefficients (MFCC) as feature representations with three machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN). The methodology involves audio data preprocessing, MFCC-based feature extraction, and classification using the selected algorithms. The RAVDESS dataset, consisting of 1,440 English-language audio samples across four emotions (happy, angry, sad, neutral), was used with an 80/20 train-test split to ensure class balance.. Experimental results show that the KNN model achieved the highest performance, with an accuracy of 78.26%, precision of 85.09%, recall of 78.26%, and F1-score of 77.06%. The Decision Tree model produced comparable results, while the SVM model performed poorly across all metrics. These findings demonstrate that the proposed hybrid approach is effective for recognizing emotions in speech and offers a computationally efficient alternative to deep learning models. The integration of MFCC features with multiple machine learning classifiers provides a robust framework for real-time emotion recognition applications, especially in environments with limited computing resources.]]>
<![CDATA[Air Quality Index Classification: Feature Selection for Improved Accuracy with Multinomial Logistic Regression ]]>
<![CDATA[Irjayana, Rizky Caesar]]>;
<![CDATA[Fadlil, Abdul]]>;
<![CDATA[Umar, Rusydi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5155
<![CDATA[Air pollution is a major public health concern, creating the need for accurate and interpretable Air Quality Index (AQI) classification models. This study aims to classify AQI into three categories—Good, Moderate, and Unhealthy—using Multinomial Logistic Regression (MLR) with feature selection. The dataset, obtained from public monitoring stations in Jakarta between 2021 and 2024, initially contained 4,620 daily records. After cleaning and outlier removal, 3,586 valid samples remained, from which 900 balanced records (300 per class) were selected for modeling. Key features included PM₁₀, PM₂.₅, SO₂, CO, O₃, and NO₂, which were standardized using Max Normalization to ensure uniform feature scaling. The classification process applied k-fold cross-validation (k = 2–5), and performance was assessed using accuracy and Macro F1-score. Results show that including PM₂.₅ improves performance by about 10%, with the best outcome at k = 5 (accuracy = 91.67%, Macro F1 = 91.45%). These findings confirm PM₂.₅ as a decisive feature for AQI prediction and demonstrate that MLR provides a lightweight, transparent, and computationally efficient solution. Beyond environmental health, the contribution of this work lies in advancing data-driven decision support systems in Informatics, particularly for real-time monitoring and policy applications.]]>
<![CDATA[Automated Video Recognition of Traditional Indonesian Dance Using Hyperparameter-Tuned Convolutional Neural Network ]]>
<![CDATA[Purwaningrum, Santi]]>;
<![CDATA[Susanto, Agus]]>;
<![CDATA[Susanti, Hera]]>;
<![CDATA[Alkhafaji, Mohammed Ayad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5157
<![CDATA[Traditional Indonesian dances serve as a vital expression of cultural identity and regional heritage, yet their preservation through intelligent video recognition remains limited due to technical challenges in motion complexity, costume variation, and the lack of annotated datasets. Prior research commonly employed Convolutional Neural Networks (CNNs) with manually defined hyperparameters, which often resulted in overfitting and poor adaptability when applied to dynamic and real-world video inputs. To overcome these limitations, this study proposes a robust and adaptive classification framework utilizing a hyperparameter-tuned CNN model. The approach automatically optimizes key training parameters such as learning rate, batch size, optimizer type, and epoch count through iterative experimentation, thereby maximizing the model’s ability to generalize across both static and temporal data domains. The model was trained using image datasets representing three traditional dances (Gambyong, Remo, and Topeng), and subsequently tested on segmented frames extracted from YouTube videos. Results indicate strong model performance, achieving 99.67% accuracy on the training set and 100% accuracy, precision, recall, and F1-score across all testing videos. The proposed method successfully bridges the gap between still-image learning and real-world motion recognition, making it suitable for practical applications in digital archiving and cultural documentation. This study’s contribution lies not only in the model’s technical effectiveness but also in its support for preserving intangible cultural assets through intelligent and automated video-based recognition. Future work may incorporate temporal modelling or multi-camera perspectives to further enrich motion understanding and extend the system to broader performance domains.]]>
<![CDATA[Forecasting Bitcoin Price Prediction with Long Short-Term Memory Networks: Implementation and Applications Using Streamlit ]]>
<![CDATA[Fawzi, Muhammad Ihsan]]>;
<![CDATA[Ganesha, Taufik]]>;
<![CDATA[Anugrah, Priandika Ratmadani]]>;
<![CDATA[Zhahran, Maulana]]>;
<![CDATA[Abimanyu, Faris Akbar]]>;
<![CDATA[Bimantoro, Haryo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5168
<![CDATA[The rapid growth of cryptocurrency markets, particularly Bitcoin, has highlighted the need for accurate price prediction models to support informed decision-making. While existing studies primarily evaluate machine learning models for price forecasting, few have implemented these models in real-world applications. This paper addresses this gap by developing a Bitcoin price prediction system using Long Short-Term Memory (LSTM) networks, integrated into a user-friendly web-based application powered by Streamlit. The model forecasts Bitcoin prices at 5-minute, 1-hour, and 1-day intervals, demonstrating strong predictive performance. For the 5-minute interval, the model achieved a Mean Squared Error (MSE) of 53,479.86, Mean Absolute Error (MAE) of 150.58, Root Mean Squared Error (RMSE) of 231.26, and Mean Absolute Percentage Error (MAPE) of 0.144%. At the 1-hour interval, errors increased moderately with an MSE of 423,198.24, MAE of 499.93, RMSE of 650.54, and MAPE of 0.505%. For the 1-day interval, the model faced greater variability, reflected in an MSE of 3,089,699.07, MAE of 1,058.88, RMSE of 1,757.75, and MAPE of 2.027%. These results indicate that while predictive precision decreases over longer horizons, the model maintains strong performance across all timeframes. By embedding LSTM predictions into an interactive, real-time forecasting platform, this study demonstrates the practical integration of deep learning into complex financial systems. Beyond cryptocurrency, the approach highlights the potential of intelligent computational models to enhance decision-making processes in data-intensive domains, reinforcing the role of informatics in bridging advanced algorithms with usable technological solutions.]]>
