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.
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962 Documents
<![CDATA[Geo-Sentiment Analysis of Public Opinion of X Users towards the Documentary Film Dirty Vote using the Bidirectional Long Short-Term Memory Method ]]>
<![CDATA[Salsabila, Syifa]]>;
<![CDATA[Sibaroni, Yuliant]]>;
<![CDATA[Prasetiyowati, Sri Suryani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4195
<![CDATA[Presidential elections held every five years, often generates significant public discourse. The 2024 presidential election saw the release of the documentary Dirty Vote, which raised allegations of electoral fraud and sparked polarized opinions on social media, especially on X. This study aims to analyze public sentiment toward Dirty Vote using geo-sentiment analysis and the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Data were collected from geotagged tweets, with sentiment classified as positive, negative, or neutral. The research explored various data processing techniques, including TF-IDF for feature extraction, FastText for feature expansion, and balancing methods like SMOTE and class weighting to address data imbalance. Results showed that the baseline Bi-LSTM model achieved an accuracy of 71.57% and an F1-Score of 74.05%. When enhanced with TF-IDF and FastText, accuracy increased to 77.07%, though the F1-Score dropped slightly to 72.95%. Applying SMOTE resulted in a decrease in accuracy to 76.45%, but significantly improved the F1-Score to 74.93%. Exploratory data analysis revealed that negative sentiment was most concentrated in Java Island, particularly Jakarta, and peaked during February 2024, coinciding with the documentary's release and the election period. This study significantly contributes to understanding how geographic locations influence public opinion on sensitive political issues. A lack of understanding of geographically-based sentiment patterns can hinder identifying regional needs, leading to poorly targeted policies. By integrating data analysis methods with geographical approaches, this research provides deep insights for designing more effective, data-driven public intervention strategies and supports policymaking that is more responsive to the dynamics of public opinion.]]>
<![CDATA[Depression Detection in Indonesian X Social Media Text using Convolutional Neural Networks and Long Short-Term Memory with TF- IDF and FastText Methods ]]>
<![CDATA[Putri, Karina Khairunnisa]]>;
<![CDATA[Setiawan, Erwin Budi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4206
<![CDATA[Depression is a growing mental health issue in the modern era, with social media offering a unique opportunity for automated detection through text analysis. However, challenges such as unstructured language, ambiguity, and contextual complexity in social media text hinder accurate detection. This research aims to develop and evaluate a hybrid deep learning model to detect depression in Indonesian social media text. A data set of 50523 entries was obtained and cleaned and TF-IDF was used for feature extraction while FastText was used for feature expansion. The classification was done by using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a combination of both CNN and LSTM models and the performance of the models was measured using the accuracy, precision and recall scores. The experimental results showed that the LSTM model gave the best result in terms of accuracy which is 83.58%, the second best was the LSTM-CNN hybrid model with an accuracy of 83.20%. The current study thus provides a new approach for identifying depression in Indonesian language data and can be said to significantly advance the fields of informatics and computer science. It also shows how AI can be utilized in improving mental health practices and in designing better social media environments. The findings of this study contribute to the growing body of research on cross-cultural mental health detection and highlight the importance of developing language-specific machine learning models.]]>
<![CDATA[You Only Look Once v5 and Long Short-Term Memory Implementation for Crowd Anomaly Detection ]]>
<![CDATA[Wardani, Ken Ratri Retno]]>;
<![CDATA[Chrisandy, Nicholas]]>;
<![CDATA[Martina, Inge]]>;
<![CDATA[Heryanto, Hery]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4224
<![CDATA[In Indonesia, 116,000 traffic accidents and 370,747 workplace accidents occurred in 2023, emphasizing the urgent need for effective surveillance systems for monitoring crowded areas such as public sidewalks, roads, workplaces, and school hallways. This study introduces a novel approach combining You Only Look Once v5 (YOLOv5) and Long Short-Term Memory (LSTM) networks for crowd anomaly detection. Unlike traditional methods, this hybrid framework utilizes YOLOv5 for precise feature extraction from video frames and LSTM to capture temporal dependencies for detecting anomalous behaviors. The dataset used includes scenes from the Crowd Anomaly Detection UML Dataset, consisting of a 1-minute and 11-second video extracted into 852 images. Hyperparameter tuning was conducted for epochs and learning rates in the YOLOv5 model, as well as for epochs and units in the LSTM model. The proposed framework achieved remarkable results, with 98% accuracy, 100% precision, and 86% F1-Score. However, improvements in class distribution within the training data could enhance model performance further. These findings demonstrate the potential of the proposed method for real-world applications in improving public safety and effective anomaly detection. This research proves that the proposed method which uses separate feature extraction method before detecting anomaly provides a better result in crowd anomaly detection.]]>
<![CDATA[Analyzing Public Sentiment on the Relocation of Indonesia's Capital to Kalimantan as the Ibu Kota Nusantara Using Logistic Regression ]]>
<![CDATA[Maharani, Warih]]>;
<![CDATA[Latifa, Agisni Zahra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4230
<![CDATA[The Ibu Kota Nusantara (IKN) relocation project aims to equalize economic development and reduce the burden on Jakarta, but has elicited mixed reactions from the public, including both support and opposition. Therefore, this study applies machine learning-based sentiment analysis, using Logistic Regression to explore public opinion on the relocation, and leveraging social media data from platform X to gain insights into information, opinions, and public reactions. The Textblob, VADER, and SentiWordNet labeling methods employ a majority vote of the three labels to determine the final label. In order to achieve data balance, SMOTE is employed in this study. Moreover, this study applies a combination of preprocessing, N-gram, and TF-IDF to illuminate the impact of this combination on model performance. The results indicate that the combination of preprocessing Scenario 3 with unigram, bigram, trigram, and TF-IDF feature extraction yields the best performance, achieving a precision of 0.7641, recall of 0.7767, F1-score of 0.7634, and accuracy of 0.7641. This research demonstrates the efficacy of proper preprocessing and feature extraction in enhancing the performance of the Logistic Regression model for sentiment classification, thereby contributing to the analysis of public opinion on IKN policy regarding other issues in the future.]]>
<![CDATA[Improved Micro-expression Recognition: An Apex Frame-Based Approach Feature Tracking and KLT ]]>
<![CDATA[Choirina, Priska]]>;
<![CDATA[Fitriani, Indah Martha]]>;
<![CDATA[Rosiani, Ulla Delfana]]>;
<![CDATA[Mufti, Muhammad Nabil]]>;
<![CDATA[Arsistawa, Firmanda Ahmadani]]>;
<![CDATA[Darajat, Pangestuti Prima]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4235
<![CDATA[This research develops a real-time facial micro-expression recognition system, focusing on analyzing the onset and apex phases of micro-expression on the Spontaneous Activity and Micro-Movements (SAMM) dataset. Micro- expressions are very brief (0.04 - 0.2 seconds) facial muscle movements that often occur when a person is trying to hide emotions. The developed system aims to improve computation time efficiency and micro-expression recognition accuracy by optimizing feature extraction techniques and selecting more specific facial areas, including facial components such as eyebrows, eyes, and mouth. This research successfully improved the computation time efficiency by 51.96%, almost half the time required by the previous method. In addition, this study shows an increase in efficiency compared to previous studies, with an increase of 34.3% for SVM with Manual Sampling technique and 32.6% for MLP-Backpropagation. In the Random Sampling technique, SVM efficiency increased by 6.1%, but MLP-Backpropagation accuracy decreased by 4.8%. This method achieved 77.9% accuracy for MLP- Backpropagation, which is higher than the previous method. This research contributes to accelerating micro- expression recognition systems and improving accuracy, which opens opportunities for real-time emotion analysis applications such as lie detection or human behavior monitoring in a broader context.]]>
<![CDATA[YOLOv9-Based Object Detection Model For Pig Feces On Pig SKIN: Improving Biosecurity In Automated Cleaning Systems ]]>
<![CDATA[Andika, I Gede Irvan Pramanta]]>;
<![CDATA[Sudarma, I Made]]>;
<![CDATA[Swamardika, Ida Bagus Alit]]>;
<![CDATA[Sedana, I Made Bagus Ambara]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4240
<![CDATA[This study developed an object detection model using YOLOv9 to identify pig feces on pig skin, addressing challenges in automating pig cleaning systems and reducing the spread of African Swine Fever (ASF). The aim was to enhance biosecurity measures by minimizing human-pig contact through automation. A specialized dataset comprising 5,404 images was collected from Nyoman Farm in Bali, Indonesia, under various lighting and cleanliness conditions. These images were annotated into two classes, namely 'feces' and 'pig,' following strict criteria to ensure clarity and distinction. YOLOv9 was chosen as it is an advanced update of YOLOv8 with enhanced object detection capabilities. The model was iteratively trained and optimized to achieve the best performance. The results achieved a mAP_0.5 of 70.5%, precision of 70.6%, and recall of 72.1%. However, the model faced challenges in distinguishing pig skin patterns from feces and managing false positives caused by similar-looking objects in the barn environment. Despite these challenges, integrating this model into an automated cleaning system can reduce human-pig contact by up to 76%, which is expected to significantly lower the risk of ASF transmission. This study contributes to automated farming technology, demonstrating how well YOLOv9 can detect complex objects in agricultural settings and providing practical solutions to enhance biosecurity in pig farming while improving productivity.]]>
<![CDATA[Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence ]]>
<![CDATA[Rusdah, Rusdah]]>;
<![CDATA[Painem, Painem]]>;
<![CDATA[Kusumaningsih, Dewi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4263
<![CDATA[Tuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019.]]>
<![CDATA[Comparative Analysis of Data Balancing Techniques for Machine Learning Classification on Imbalanced Student Perception Datasets ]]>
<![CDATA[Saekhu, Ahmad]]>;
<![CDATA[Berlilana, Berlilana]]>;
<![CDATA[Saputra, Dhanar Intan Surya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4286
<![CDATA[Class imbalance is a common challenge in machine learning classification tasks, often leading to biased predictions toward the majority class. This study evaluates the effectiveness of various machine learning algorithms combined with advanced data balancing techniques in addressing class imbalance in a dataset collected from Class XI students of SMK Ma'arif 1 Kebumen. The dataset, comprising 300 instances and 36 features, includes textual attributes, demographic information, and sentiment labels categorized as Positive, Neutral, and Negative. Preprocessing steps included text cleaning, target encoding, handling missing data, and vectorization. Four sampling techniques—SMOTE, SMOTE + Tomek Links, ADASYN, and SMOTE + ENN—were applied to the training data to create balanced datasets. Nine machine learning algorithms, including CatBoost, Extra Trees, Random Forest, Gradient Boosting, and others, were evaluated using four train-test splits (60:40, 70:30, 80:20, and 90:10). Model performance was assessed using metrics such as accuracy, precision, recall, F1-score, and AUC- ROC. The results demonstrate that SMOTE + Tomek Links is the most effective balancing technique, achieving the highest accuracy when paired with ensemble algorithms like Extra Trees and Random Forest. CatBoost also delivered competitive performance, showcasing its adaptability in imbalanced scenarios. The 90:10 train-test split consistently yielded the best results, emphasizing the importance of adequate training data for model generalization. This study highlights the critical role of data balancing techniques and robust algorithms in optimizing classification performance for imbalanced datasets and provides a framework for future research in similar contexts.]]>
<![CDATA[Enhancing Clustering Performance through Benchmarking of Dimensionality Reduction Techniques on Educational Data ]]>
<![CDATA[Priyanto, Eko]]>;
<![CDATA[Berlilana, Berlilana]]>;
<![CDATA[Tahyudin, Imam]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4297
<![CDATA[This study evaluates the effectiveness of dimensionality reduction techniques in enhancing clustering performance using a tracer study dataset of 500 alumni from UMNU Kebumen, containing 58 variables. The objective was to identify the optimal combination of dimensionality reduction and clustering methods for uncovering patterns in alumni profiles, job search strategies, and employment outcomes. Principal Component Analysis (PCA), Non- Negative Matrix Factorization (NMF), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were applied, followed by clustering using K-Means, DBSCAN, and Hierarchical Clustering. The findings revealed that NMF achieved the highest clustering quality, particularly with K- Means and Hierarchical Clustering, outperforming PCA. NMF also demonstrated superior compactness with a Calinski-Harabasz Index of 287.96, compared to 125.88 for PCA. While t-SNE and UMAP delivered competitive results, their computational times of 245.8 and 76.5 seconds, respectively, made them less practical for large datasets. The novelty of this study lies in its comprehensive evaluation of dimensionality reduction techniques and the integration of diverse clustering algorithms to assess their interplay. The results provide actionable insights, recommending NMF for accuracy-critical tasks and PCA for time-sensitive applications. Given the increasing volume of high-dimensional educational data, this study highlights the critical need for efficient clustering strategies to extract meaningful insights, ultimately supporting data-driven decision-making in education and workforce planning. Addressing these challenges is essential to optimizing institutional strategies, improving student employability, and enhancing workforce alignment with industry demands.]]>
<![CDATA[Comparing Orientation Position in Close-Range Photogrammetry for the Documentation of Waruga Cultural Heritage as 3D Objects ]]>
<![CDATA[Salaki, Deiby Tineke]]>;
<![CDATA[Latumakulita, Luther Aleander]]>;
<![CDATA[Sintaro, Sanriomi]]>;
<![CDATA[Islam, Noorul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4305
<![CDATA[Waruga is a distinctive cultural artifact found exclusively in the Minahasa region. Despite its historical and cultural significance, efforts to preserve Waruga remain inadequate. Many structures have been left neglected, covered in fungi, or even damaged over time. Additionally, government-led relocation initiatives have contributed to the loss of their original form, further threatening this invaluable Minahasa cultural heritage. This study aims to examine the impact of photographic orientation in the creation of 3D models using close-range photogrammetry techniques. The resulting 3D models will be displayed on a digital platform to support the preservation and promotion of Minahasa culture. The photography process was divided into two categories: point-of-view shots and high-angle shots. Findings indicate that the optimal angle for point-of-view shots is 15 degrees downward, while for high-angle shots, it is 30 degrees downward. Furthermore, comparative analysis of Waruga structures with varying shapes demonstrates that portrait orientation yields 3D models that more accurately resemble the original objects compared to landscape orientation when using the same number of images. The study concludes that portrait orientation is the most effective approach for 3D reconstruction of Waruga, offering advantages such as faster processing times and reduced file sizes. In contrast, landscape orientation presents challenges, including difficulties in capturing intricate details, increased processing time, and larger file sizes. These findings provide valuable insights into optimizing digital preservation techniques for Waruga and other cultural heritage artifacts.]]>
