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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<![CDATA[ANALYSIS OF THE MOVIE DATABASE FILM RATING PREDICTION WITH ENSEMBLE LEARNING USING RANDOM FOREST REGRESSION METHOD ]]>
<![CDATA[Marpid, Nuravifah Novembriana]]>;
<![CDATA[Kurniawan, Yogiek Indra]]>;
<![CDATA[Rahayu, Swahesti Puspita]]>
<![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.2025.6.1.1563
<![CDATA[The film industry has become a very profitable industry. However, during COVID-19 the film industry experienced an unfavorable impact with the delay in the screening schedule of new films, many cinemas were prohibited from operating so they were completely closed, and it wasn’t easy to obtain permits to carry out the filmmaking process. To survive in this industry from the impact of the pandemic, it is necessary to consider several factors such as targeted promotion methods by using the right selection of predictive decisions with market and trends. Predicting the success of a film is very helpful in determining the success rating and quality of the film to be released. The Random Forest Regression method is used to conduct predictive analysis on films. This study uses the M-estimate encoding technique to handle categorical data into numerical data, and the result shows that the application of M-estimate encoding increases the correlation value between features. In the Random Forest Regression method with 1000 trees, dividing 80% training data and 20% testing data, the R2 performance score was 86%, the MSE score was 12%, the RMSE score was 35% and the MAE score was 22%. The 10-fold cross-validation score in this study was 85%. This shows that the Random Forest Regression method using 80% training data produces the best performance score.]]>
<![CDATA[MODELING INTRUSION DETECTION AND PREVENTION SYSTEM TO DETECT AND PREVENT NETWORK ATTACKS USING WAZUH ]]>
<![CDATA[Pramudya, Otniel Dewangga Divan]]>;
<![CDATA[Hatta, Puspanda]]>;
<![CDATA[Budiyanto, Cucuk Wawan]]>
<![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.2025.6.1.1830
<![CDATA[The rapid development of technology has a positive impact on society. The internet can be easily accessed anytime and anywhere, but with the advancement of internet technology, there are many threats lurking in the security of its users. Criminal activities in the digital world are referred to as cybercrime. Numerous cases of cybercrime have occurred worldwide, ranging from attacks that can disable servers to data theft and illegal access. It is noted that more than 50% of companies do not have a plan to respond to these cybercrimes. This is due to various factors, one of which is the limited availability of freely accessible and easily configurable network security platforms for all users. Therefore, this research aims to provide a solution in the form of an open-source-based Intrusion Detection and Prevention System (IDPS) that can be freely distributed and easily configured, one of which is Wazuh. The study uses the Cisco PPDIOO approach in developing a virtual lab with various scenarios for testing and measuring the Quality of Services (QoS) of Wazuh's performance. From the created test scenarios, Wazuh can detect attacks from both inside and outside the network. Wazuh has proven to be capable of detecting and preventing various types of network attacks and features that can facilitate users in responding to cybercrime, making it a potential solution for organizations that have not planned to respond to cybercrime.]]>
<![CDATA[IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK METHOD IN CLASSIFYING PANDAWA SHADOW PUPPETS ]]>
<![CDATA[Handani, Faisal Akbar Junivo]]>;
<![CDATA[Wijayanti, Esti]]>;
<![CDATA[Fiati, Rina]]>
<![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.2025.6.1.1851
<![CDATA[The rapid development of technology can lead to the neglect of traditional cultural and artistic aspects by humans. Nonetheless, technology has become integral in society's life. While technology facilitates humans in completing tasks, Negative impacts can also arise. One example of traditional art in Indonesia is shadow puppetry, often featuring stories of the Pandavas from the Mahabharata in puppetry performances. Characters in shadow puppetry are grouped based on character, era, and story, with similar shapes and contours. The similarity of these characters makes them difficult to distinguish and remember. Therefore, an application has been developed that can detect and classify Pandawa shadow puppet characters. The method used in this research is the Convolutional Neural Network (CNN), an effective method in deep learning for classifying data based on informational context The hope is that this application will not only introduce Indonesian culture through Pandawa shadow puppet characters but also provide a high level of accuracy in its classification results. Through the conducted training procedure, the developed model showed an accuracy rate of 95.70%. Furthermore, result verification through the use of a confusion matrix confirmed an accuracy level reaching 88%.]]>
<![CDATA[LINE PATH DETECTION ON HIGHWAYS USING THE HOUGH TRANSFORM METHOD ]]>
<![CDATA[Munawir, Munawir]]>;
<![CDATA[Wandini, Amelia]]>;
<![CDATA[Ihsan, Ahmad]]>
<![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.2025.6.1.1955
<![CDATA[Lane line detection on highways is an important problem in the development of intelligent transportation technology or autonomous vehicles. One commonly used method is the Hough Transform method, which is known for its excellent level of accuracy and effectiveness. Line lane detection aims to identify and monitor line lanes on highways, which helps direct and limit vehicle traffic and ensures the safety and efficiency of vehicle movement. This research uses video images from cellphone cameras that have been taken previously. The image is then processed using the Hough Transform algorithm to detect line paths on the highway. The aim of this research is to create a line lane detection system on highways that is able to identify line lanes in various road conditions by utilizing the Hough Transform Algorithm. Apart from that, it also aims to test the ability of the Hough Transform algorithm in the lane line detection system which can provide a warning if the driver is too close to the line lane, increasing safety on the road. Even though there are several obstacles such as poor road conditions, unclear or faded line paths, and busy traffic situations, the results of this research show that the Hough Transform method can be used to detect line paths on highways well, and the level of accuracy is sufficient high namely 83%.]]>
<![CDATA[CLASSIFICATION OF RUPIAH CURRENCY IN THE FORM OF PAPER USING THE MOBILENETV3 LARGE METHOD ]]>
<![CDATA[Wijaya, Anggito Karta]]>;
<![CDATA[Royan, Ando Zamhariro]]>
<![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.2025.6.1.2040
<![CDATA[Money plays an important role in everyday life as a legal tender and a symbol of a country's economic strength. The ability to accurately classify rupiah banknotes has many practical applications such as in automated payment systems, currency exchange, and cash management. However, conventional classification approaches based on digital image processing and image processing techniques are often limited in terms of accuracy and computational efficiency, especially when dealing with a variety of banknote conditions such as wrinkles, stains, or damage. This research aims to propose a new approach by utilising the MobileNetV3 Large architecture, an efficient and lightweight deep learning model, to address the challenges of paper currency classification. The main objective is to improve classification accuracy while minimising computational resources. The dataset used consists of 2873 images of paper rupiah currency of various denominations and conditions from seven classes. These images were processed and trained using the MobileNetV3 Large model that has been customised for this classification task by applying various data augmentation techniques. Experimental results show that the proposed approach is able to achieve 100% classification accuracy on a test dataset with a relatively small model size so that it can be run efficiently on mobile devices or embedded systems. This research makes an important contribution to the development of accurate and efficient rupiah banknote classification techniques for various practical applications in the future.]]>
<![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.]]>
