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[SYSTEMATIC REVIEW OF EXPERT SYSTEM FOR DETECTING MENTAL HEALTH DISORDERS IN COLLEGE STUDENTS ]]>
<![CDATA[Widyassari, Adhika Pramita]]>;
<![CDATA[Carreon, Jonathan Rante]]>;
<![CDATA[Wahyusari, Retno]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4089
<![CDATA[There is an urgent need to detect and manage mental health disorders among college students, who often face psychological challenges due to academic pressures and significant life changes. In this context, expert systems emerge as a potential tool to assist in the diagnosis and management of mental health problems. The purpose of this study is to present the results of a systematic review of expert systems for detecting mental health disorders in college students through the systematic literature review (SLR) method. By asking four research questions covering types of mental health disorders, methods used, comparisons between methods, and testing techniques, this study limits its review to studies published in the last five years, from 2019 to 2024. This review covers various types of mental health disorders, such as depression, anxiety, stress disorders and other mental health disorders that are often experienced by the college student population. As well as evaluating and comparing methods such as forward chaining, backward chaining, certainty factor and fuzzy logic methods to identify the advantages and disadvantages of each method. Certainty Factor emerged as the most accurate method with an accuracy of 96.09% and the recommendation for combining methods for this study is certainty factor and forward chaining with an accuracy result of 100%. In addition, this study also discusses the testing process to ensure the effectiveness and accuracy of the resulting diagnosis. The findings of this systematic review are expected to provide valuable insights for the development of more effective expert systems in supporting college students' mental health.]]>
<![CDATA[DROUGHT PREDICTION USING LSTM MODEL WITH STANDARDIZED PRECIPITATION INDEX ON THE NORTH COAST OF CENTRAL JAVA ]]>
<![CDATA[Supriyanto, Aji]]>;
<![CDATA[Zuliarso, Eri]]>;
<![CDATA[Suharmanto, Eko Taufiq]]>;
<![CDATA[Amalina, Hana]]>;
<![CDATA[Damaryanti, Fitri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4159
<![CDATA[Fluctuating weather can trigger hydrometeorological disasters, especially affecting farmers and fishermen on the north coast of Central Java. Weather predictions including drought are very important to anticipate drought disasters. Deep learning-based prediction models such as Long Short Term Memory (LSTM) are used in an effort to reduce the impact of drought. The purpose of this study is to prove the level of accuracy of the LSTM model and determine the drought index with the Standardized Precipitation Index (SPI). The LSTM model is used to predict drought based on the SPI, while the SPI acts as a drought index that considers precipitation (rainfall) for a period of 1, 3, and 6 months. Predictions use rainfall data obtained from online data from the Central Java BMKG UPT Indonesia for the period 2010-2023 in the Tegal City and Semarang City station areas. The results of data treatment with LSTM can effectively analyze and capture complex patterns in meteorological data to predict drought events accurately. The effectiveness of the model is shown by the relatively small MAE and RMSE results, namely MAE 0.163 - 0.352 and RMSE 0.247-0.515. The best prediction result is the 3-month SPI in the Semarang area with MAE 0.163 and RMSE 0.274. While the prediction result with the largest error is the 1-month SPI in the Tegal area. Drought modeling using LSTM has been successfully implemented for the northern coast of Central Java using the Streamlit Framework and can process and visualize the drought prediction system well.]]>
<![CDATA[CLASSIFICATION OF TODDLER NUTRITIONAL STATUS USING SUPPORT VECTOR MACHINE AND RANDOM FOREST TECHNIQUES WITH OPTIMAL FEATURE SELECTION ]]>
<![CDATA[Widyawati, Femmi]]>;
<![CDATA[Suhito, Hanif Pandu]]>;
<![CDATA[Yassin, Warusia]]>;
<![CDATA[Agus Santoso, Heru]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4162
<![CDATA[Nutritional problems in toddlers, such as stunting, wasting, being underweight, and obesity, are major challenges in monitoring toddler health in Indonesia because they can hurt toddler growth and development. Therefore, handling nutritional problems comprehensively, including prevention efforts and appropriate dietary interventions, is very important. This study aims to develop a toddler nutritional status classification model based on machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest, by utilizing a toddler dataset obtained from Health Institutions in Indonesia containing 9,735 data. The model was designed using the Recursive Feature Elimination (RFE) technique for selecting relevant features and the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. The results showed that the Random Forest algorithm performed best with 95% accuracy, 77% precision, 87% recall, and 81% f1-score. This study contributes to developing a machine learning-based approach to support a more effective nutritional monitoring system and enable more appropriate dietary interventions to address toddler health problems in Indonesia.]]>
<![CDATA[UNRAVELING OF MEN'S FRAGRANCE PREFERENCES ON ONLINE MARKETPLACES: A MACHINE LEARNING STUDY USING DBSCAN CLUSTERING AND LINEAR REGRESSION ]]>
<![CDATA[Alkaf, Zakiyyan Zain]]>;
<![CDATA[Fawzi, Muhammad Ihsan]]>;
<![CDATA[Sastyawan, Murti Wisnu Ragil]]>;
<![CDATA[Putera, Radita Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4187
<![CDATA[The perfume industry is undergoing significant growth, driving the need to understand consumer preferences, particularly in men’s fragrances, to optimize business strategies. This study aims to analyze and uncover men’s fragrance preferences, using machine learning techniques. A dataset of approximately 1,000 men's perfume records from Kaggle was utilized, where systematic methodologies were employed. Data preprocessing involved handling missing values, removing duplicates, standardizing categorical entries, and performing feature engineering by extracting geographic information from item locations. Exploratory Data Analysis (EDA) was conducted to uncover data distribution. Clustering analysis using DBSCAN revealed consumer segments. Additionally, regression analysis was used to predict sales based on price and location, employing a linear regression model evaluated with metrics like Mean Squared Error (MSE). The findings indicate that price exhibits a complex relationship with sales; while affordable products drive higher sales volumes, premium-priced items cater to a niche yet impactful market segment. Geographic location plays a pivotal role in sales patterns. Clustering analysis reveals two distinct consumer segments: one driven by price sensitivity and another oriented towards premium preferences, influenced by regional factors. Regression analysis demonstrated a negative correlation between price and sales volume, with a coefficient of -1.81, while availability positively influenced sales with a coefficient of 8.36. Despite a moderate model fit (R² = 0.17), the analysis highlights key market dynamics. These insights emphasize the importance of leveraging data-driven strategies to develop targeted marketing campaigns, optimize inventory management and refine market segmentation.]]>
<![CDATA[ANALYZING STACK OVERFLOW DISCUSSIONS ON C, JAVA, AND PYTHON: A MIXED-METHOD STUDY ON QUESTION TYPES AND TOPICS ]]>
<![CDATA[Nugroho, Yusuf Sulistyo]]>;
<![CDATA[Minalloh, Aldin Nasrun]]>;
<![CDATA[Devi, Keke Rachma]]>;
<![CDATA[Islam, Syful]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4191
<![CDATA[The modern software development characteristic is significantly shaped by the evolution of programming languages. The increasing complexity of these languages demands effective tools and resources for learning and troubleshooting. As a result, forums such as Stack Overflow (SO) have become crucial for addressing technical issues that arise during program execution, especially for novice programmers. Although discussions on SO are common, there hasn't been a clear description of the question types and topics for the three main programming languages, i.e., C, Java, and Python. This gap is problematic as it limits the ability of educators, platform designers, and developers to effectively address the specific needs of users. Without such insights, novice programmers may struggle to find relevant guidance, potentially hindering their learning and slowing the adoption of best practices. To fill this gap, we conducted a qualitative and quantitative study on these three language-related discussions shared on SO. By utilizing a dataset of 4,499,718 questions extracted from SOTorrent, we applied a manual labeling method to classify questions into categories such as “How,” “What,” and “Why.” Furthermore, we implemented Latent Dirichlet Allocation (LDA) for topic modeling to understand the prevalent discussion topics. The results show that “How” questions dominate across all languages, particularly in Python (60.94%), reflecting a high demand for practical implementation guidance. Analysis of discussion topics indicates that C is centered on system programming and low-level operations, while Java discusses more on application development and object-oriented programming. In contrast, Python focuses more on data handling and structures. These insights suggest that while practical support is necessary for learners, a deeper understanding of programming concepts and the need for customized instructional resources to support developers are important. The findings contribute to the community and relevant fields by offering actionable insights to improve the usability of SO as a learning and problem-solving platform.]]>
<![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.]]>
