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[SENTIMENT ANALYSIS OF ONLINE DATING APPS USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS ]]>
<![CDATA[Laksono, Urip Hadi]]>;
<![CDATA[Suryono, Ryan Randy]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.2105
<![CDATA[In daily life, the use of digital applications is increasingly widespread, making dating apps increasingly popular and an important part of modern social interaction. This research aims to analyze user sentiment towards online dating apps, specifically Tinder, using Support Vector Machine (SVM) and Naïve Bayes algorithms. The problem underlying the importance of this research is the lack of balance between positive and negative sentiments in Tinder app users, which can affect user experience and the quality of service provided by Tinder. Utilizing the CRISP-DM framework, this research involves six stages, from data collection to evaluation. The results showed a significant imbalance between the number of positive and negative sentiments before optimization, but after the application of the SMOTE technique, there was a balancing between the two sentiment categories. SVM achieved 85% accuracy, while Naïve Bayes achieved 84%, with similar performance in identifying positive and negative sentiments. While both models performed satisfactorily, SVM appeared more stable in recognizing both positive and negative sentiments, suggesting the potential to be a superior choice in the context of dating apps. As such, this research makes an important contribution to the understanding of users' views on Timder apps and provides a basis for further development.]]>
<![CDATA[OPTIMIZATION OF STOCK PRICE PREDICTION WITH RIDGE REGRESSION AND HYPERPARAMETER SELECTIONS ]]>
<![CDATA[Marwa, Adeline Fellita]]>;
<![CDATA[Setiyawan, Sitti Ayuningrum]]>;
<![CDATA[Cahyani, Yonaka Titin Nur]]>;
<![CDATA[Cahyono, Hasan Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.2384
<![CDATA[Stock price prediction is a topic that has garnered significant attention in the investment world and has been the subject of various studies. Despite the massive attention, predicting stock price movements using algorithms remains challenging as the algorithms must be agile and highly adaptive to movement trends. Recent studies using deep learning methods for stock price prediction show that deep learning methods have high reliability. However, their computational complexity limits widespread implementation. This study aims to predict Netflix stock prices using a linear regression model with ridge and hyperparameter optimisation. The research consists of three stages: data preprocessing, building a linear regression model with ridge, and predicting and visualizing results. The dataset used is historical Netflix stock price data from 2017 to 2022. In the preprocessing stage, the data was normalized using MinMaxScaler and split into training and test sets. A ridge regression model was built with hyperparameter alpha optimization using GridSearch. Predictions were compared to stock prices and evaluated using Root Mean Squared Error (RMSE). The ridge regression model with hyperparameter optimization performed best with an RMSE of 13.8082. Although the linear regression model demonstrated the fastest execution time of 0.7717 seconds, the ridge regression model with hyperparameter optimization provided an optimal balance between prediction accuracy and time efficiency.]]>
<![CDATA[SENTIMENT ANALYSIS OF INDONESIA'S CAPITAL RELOCATION USING WORD2VEC AND LONG SHORT-TERM MEMORY METHOD ]]>
<![CDATA[Yanti, Irma]]>;
<![CDATA[Utami, Ema]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.2712
<![CDATA[The relocation of the national capital (IKN) has garnered public attention, triggering various reactions and sentiments among the community. Sentiment analysis is crucial for understanding public perceptions of an issue, particularly on social media platforms like Twitter and YouTube. This study's sentiment analysis employs Word2Vec parameters, including architecture and dimensions. Additionally, hyperparameters such as the Optimizer and activation functions are applied to the Long Short-Term Memory (LSTM) model to analyze their effect on sentiment classification performance related to the IKN relocation. The study aims to compare the influence of Word2Vec parameters on LSTM model hyperparameter performance in sentiment classification. Data on the IKN relocation were gathered from tweets and YouTube video comments, then processed to form a text corpus used to train the Word2Vec model with Skip-gram and Continuous Bag-of-Words (CBOW) architectures, utilizing different dimension sizes (100 and 300) to enhance word representation in vectors. After obtaining word representations, the LSTM model was applied to classify sentiments using hyperparameters such as activation functions (ReLU, Sigmoid, and Tanh) and two Optimizers (Adam and RMSProp). The results indicate that the Skip-gram architecture tends to yield higher accuracy compared to CBOW, particularly with larger vector dimensions (300), which generally improved model accuracy, especially when using the RMSProp Optimizer and ReLU activation function, achieving an accuracy of 91%. It can be concluded that dimension values and architecture in Word2Vec, as well as the use of Optimizer and activation functions in LSTM, significantly impact model performance.]]>
<![CDATA[USER EXPERIENCE IN METAVERSE BUILDING TRAINING USING PHOENIX-FIRESTORM SOFTWARE ]]>
<![CDATA[Magdalena, Maria]]>;
<![CDATA[Indrajit, Richardus Eko]]>;
<![CDATA[Santoso, Handri]]>;
<![CDATA[Sari, Muh Masri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3447
<![CDATA[This study aims to evaluate the effectiveness of training using Phoenix-Firestorm software in a 3D virtual environment (metaverse) for teachers, lecturers, and students. A total of 49 participants were involved in the online training consisting of seven sessions, facilitated through the Discord platform for voice communication. Each participant was given a virtual area of 35x35 meters for practice, with daily guidance via Discord chat. The training was designed to equip participants with basic skills in building 3D objects, including an understanding of the software and building techniques. After the training, a survey was conducted using a Likert scale of 1-9 to assess participants' understanding of navigation, software customization, virtual communication, and problem-solving. The survey results showed that the majority of participants found Phoenix-Firestorm relatively easy to use, although some challenges were reported regarding the complexity of the interface. These findings will be used as a basis for developing more effective and user-friendly training guidelines in the future, with a focus on improving accessibility and user experience in the context of technology-based learning. This study is in line with previous studies that show the potential of virtual worlds in education, as discussed by Jusuf (2023). Additionally, the use of virtual technology in education is also supported by research on the effectiveness of virtual learning environments, as explained by Wang et al (2022), that digital games contributed to a moderate overall effect size when compared with other instructional methods. These findings are expected to make a significant contribution to the development of innovative training methods in education in the digital era.]]>
<![CDATA[HYBRID METHOD USING NON-NEGATIVE MATRIX FACTORIZATION AND KEYWORD-BASED FILTERING FOR RECOMMENDER SYSTEM IN MOOCS ]]>
<![CDATA[Zuliuskandar, Valleryan Virgil]]>;
<![CDATA[Yusa, Mochammad]]>;
<![CDATA[Purwandari, Endina Putri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3605
<![CDATA[Massive Open Online Courses (MOOCs), introduced by Dave Cormier in 2008, have revolutionized education by providing widespread access to open and participatory online learning. While MOOCs offer broad access and flexibility in learning, users often encounter challenges in selecting appropriate courses. This leads to high dropout rates. To address this issue, this research develops a recommendation system employing the Weighted Hybrid method that combines Non-Negative Matrix Factorization (NMF) and Keyword-Based Filtering (KBF). The primary objective of the research is to enhance the accuracy of course recommendations on MOOCs. The findings of this study demonstrate that the Weighted Hybrid method, integrating NMF and KBF, successfully attained a Mean Average Precision (MAP) of 0.1963. This figure signifies an improvement compared to the MAP value of 0.1855 achieved in prior research. This method effectively addresses challenges such as cold start and sparsity, while also improving scalability. Consequently, the Weighted Hybrid approach holds promise for improving the quality of recommendations, enhancing the user's learning experience, and potentially reducing dropout rates in MOOCs.]]>
<![CDATA[COMPARATIVE STUDY OF CNN-BASED ARCHITECTURES ON EYE DISEASES CLASSIFICATION USING FUNDUS IMAGES TO AID OPHTHALMOLOGIST ]]>
<![CDATA[Yaurentius, Evelyn Callista]]>;
<![CDATA[Saputri, Theresia Ratih Dewi]]>;
<![CDATA[Tanuwijaya, Evan]]>;
<![CDATA[Sutanto, Richard Evan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3699
<![CDATA[Eye health has a significant impact on quality of life, with more than 2.2 billion people experiencing vision problems. Many of these cases can be prevented or treated. The use of AI for eye disease classification helps healthcare professionals provide optimal care. However, the complexity of fundus images challenges classification performance. This study examines various Convolutional Neural Network (CNN) architectures using Transfer Learning and Adam optimization. Fundus images are processed using CLAHE (clip limit and grid size) and the Wiener filter (size) to enhance contrast and reduce noise. Afterward, ResNet-152, EfficientNet, MobileNetV1, and DenseNet-121 are tested to identify the most effective model. The study aims to determine the optimal CNN architecture for eye disease classification, assisting ophthalmologists in diagnosing eye diseases through fundus images. The best CNN model, ResNet-152, achieved an accuracy of 94.82%, outperforming other models by 3.95 - 8.29%.]]>
<![CDATA[APPLICATION OF VGG16 ARCHITECTURE IN WOOD TYPE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK ]]>
<![CDATA[Afiah, Nurul Anggun]]>;
<![CDATA[Syahrullah, Syahrullah]]>;
<![CDATA[Ardiansyah, Rizka]]>;
<![CDATA[Laila, Rahmah]]>;
<![CDATA[Pohontu, Rinianty]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3874
<![CDATA[Wood is an important natural resource in construction and the furniture industry, with various types possessing unique characteristics. The selection of wood types is often done manually, which is prone to errors that can negatively impact the working process, product quality, and the sustainability of the forests that source the wood. Therefore, this research aims to improve classification accuracy through the application of technology. This study utilizes Convolutional Neural Network (CNN) with the VGG16 architecture to process images in analyzing the visual characteristics of wood, with the goal of building a model capable of classifying wood types based on images. The dataset used consists of 1,584 samples of wood images sourced from Kaggle. Four models were tested with variations in the training and validation data splits, as well as the use of Adam and Adamax optimizers, over 100 epochs. Model 1 achieved a training accuracy of 96.68% and a testing accuracy of 98.10%. Model 2, with a training accuracy of 99.47% and a testing accuracy of 98.41%, showed the best performance. Models 3 and 4 also yielded testing accuracies of 97.46% and 97.78%, respectively. The results of this study indicate that the application of CNN with the VGG16 architecture can enhance the effectiveness of wood type classification and contribute to more accurate and efficient wood selection practices.]]>
<![CDATA[LEVERAGING DEEP LEARNING APPROACH FOR ACCURATE ALPHABET RECOGNITION THROUGH HAND GESTURES IN SIGN LANGUAGE ]]>
<![CDATA[Nugroho, Nadiyan Syah Wahyu]]>;
<![CDATA[Putra, Muhammad Pajar Kharisma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3912
<![CDATA[Sign language is one way of communication used by people who cannot speak or hear (deaf and speech impaired), so not everyone can understand sign language. Therefore, to facilitate communication between normal people and deaf and speech-impaired people, many systems have been created to translate gestures and signs in sign language into understandable words. Artificial intelligence and computer vision-based technologies, such as YOLOv9 offer solutions to recognize hand gestures more quickly, accurately, and efficiently. This research aims to develop a hand gesture detection system for alphabetic sign language using YOLOv9 architecture, with the aim of improving the accuracy and speed of hand gesture detection. The data used consists of 6500 sign language alphabet hand gesture images that have been labeled with bounding boxes and processed using image augmentation techniques. The model was trained on the Kaggle platform and evaluated using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Intersection over Union (IoU). The results show that the YOLOv9 model achieves an average detection accuracy of 97%, with precision and recall above 90% for most classes. In addition, YOLOv9 shows advantages over other algorithms such as SSD MobileNet v2 and Faster RCNN, both in terms of speed and accuracy. In conclusion, YOLOv9 proved to be very effective in detecting sign language hand gestures, thereby speeding up and facilitating communication. This research is expected to contribute to the development of more inclusive technologies in various fields, such as education, public services, and employment opportunities, which support better communication between sign language users and the general public.]]>
<![CDATA[PERFORMANCE EVALUATION OF YOLOV8 IN REAL-TIME VEHICLE DETECTION IN VARIOUS ENVIRONMENTAL CONDITIONS ]]>
<![CDATA[Marcelleno, Derit Junio]]>;
<![CDATA[Putra, Muhammad Pajar Kharisma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3916
<![CDATA[This research focuses on assessing and developing a real-time detection system using the YOLOv8 algorithm. Accurate and fast vehicle detection is a big challenge in modern traffic management, especially in various environmental conditions such as bad weather, low lighting, and high traffic density. The aim of this study was to evaluate the performance of YOLOv8 under these conditions and identify potential improvements. The dataset used consists of 16,990 vehicle images with various variations and environmental conditions. After being trained, the model is evaluated using metrics such as precision, recall, and F1-score, as well as Intersection over Union (IoU) with a threshold of 0.8 on IoU. The results show that YOLOv8 is superior with a fairly high detection accuracy of 78%, with precision of 82% and recall above 90%, and is able to detect vehicles in real-time conditions. However, the challenge of detecting small objects or irregularly shaped vehicles such as tractors still needs to be optimized. This research also compared the performance of YOLOv8 with the SSD (Single Shot Detector) algorithm, where YOLOv8 was proven to be superior in terms of accuracy, precision, recall and F1-score. The research results obtained provide valuable insights for the development of traffic management systems based on deep learning technology. The main contribution of this research is to provide a more efficient and effective vehicle detection solution, which can be applied in modern traffic management systems. Thus, it is hoped that the results of this research can increase the efficiency of traffic management and have a positive impact on the development of intelligent transportation systems in the future.]]>
<![CDATA[YOLOv9 – BASED TRAFFIC SIGN DETECTION UNDER VARYING LIGHTING CONDITIONS ]]>
<![CDATA[Pangestu, Akbar]]>;
<![CDATA[Putra, Muhammad Pajar Kharisma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.1.3917
<![CDATA[Traffic signs are an important element that functions as a guide, regulator and safety supervisor for road users. In Indonesia, there are various types of traffic signs, including recommendation, prohibition, warning, command, and direction signs, which use numbers, letters, symbols, or a combination of the three to convey clear information to drivers. Based on data from the Indonesian National Police, 148,575 cases of traffic accidents were recorded in 2023, which continues to increase every day due to human error, poor road conditions, and lack of clarity and completeness of signs. This research aims to develop traffic sign detection technology using the YOLOv9 algorithm, starting with collecting 7,980 images from the Roboflow platform, which are then labeled and trained, and evaluated using metrics such as Accuracy, Precision, Recall, F1-Score, and Intersection over Union (IoU ). Then the model was tested to detect traffic signs in various media, such as images and videos. The results of this research show that the YOLO v9 model has the best performance compared to SSD MobileNet v2 and Faster RCNN. The YOLOv9 model achieved an accuracy of 94%, while SSD MobileNet v2 only had an accuracy of 43%, and Faster RCNN had an accuracy of 57%. From the research, it can be concluded that the YOLOv9 model is optimal enough to detect traffic signs in various lighting conditions, because the model has the best performance compared to the other two models, especially in terms of accuracy and balance between precision and recall. This research is expected to support the development of safer autonomous vehicles and intelligent transportation systems through optimal traffic sign detection.]]>
