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[COMPARISON OF ACCURACY OF VARIOUS TEXT CLASSIFICATION METHODS IN SENTIMENT ANALYSIS OF E-STAMPS AT X ]]>
<![CDATA[Bagus Reynaldi, Dimas]]>;
<![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.2025.6.1.3999
<![CDATA[In the rapidly evolving digital era, technological innovations are applied in various fields, including law and administration, to improve the efficiency and effectiveness of processes. One of the latest innovations in Indonesia is the implementation of e-metals, which is designed to facilitate legal and secure electronic transactions, and meet the needs of a society that is increasingly dependent on digital technology. Although e-stamps aim to improve efficiency and security in transactions, there are still various perceptions from the public that reflect their views and experiences regarding the implementation of this technology. In this case, sentiment analysis is an effective method to evaluate public opinion generated from text data, such as user reviews and comments on social media. This research aims to analyze the sentiment towards e-metallocations in X app, using text classification methods to separate positive and negative sentiments. After collecting 3282 datasets and performing preprocessing that includes case folding, data cleaning, tokenizing, and stemming, the evaluation results show that the Naive Bayes (GNB) model achieves 96.65% accuracy on training data and 95.28% on testing data. On the other hand, the Support Vector Machine (SVM) model recorded an accuracy of 98.32% on training data and 96.80% on testing data. Meanwhile, the Random Forest model showed a perfect accuracy of 100% on training data and 99.09% on testing data, making it the highest performing model among the three methods tested.]]>
<![CDATA[SENTIMENT ANALYSIS OF POST-COVID ONLINE EDUCATION AMONG GEN Z WITH VARIOUS CLASSIFICATION METHODS ]]>
<![CDATA[Bakti, Da'i Rahman]]>;
<![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.2025.6.1.4003
<![CDATA[The COVID-19 pandemic has significantly changed the education sector, shifting from traditional learning to online learning. Generation Z's perception of online education is influenced by their experience as “Digital Natives” who have been familiar with technology since childhood. However, this sudden transition brings new challenges, such as screen fatigue, lack of social interaction, and difficulty in maintaining learning motivation. Sentiment analysis is an important tool to evaluate their experiences and views on online learning. This study aims to investigate Generation Z's views on online education after the pandemic, utilizing various classification methods. Data was collected from Twitter through scraping technique with specific keywords, resulting in a total of 4,986 data obtained using the Tweet Harvest library in Python programming language. The dataset then went through a preprocessing stage, including data cleaning, case folding, tokenizing, stopword removal, and stemming. Before applying Random Forest, SVM, and Naïve Bayes methods, the data is divided into two parts, namely, 3988 training data and 998 testing data with a ratio of 80:20. The accuracy results show that Naïve Bayes achieved 95.49% on training data and 76.05% on testing data, SVM recorded 94.77% accuracy on training data and 87.33% on testing data, and Random Forest obtained 99.97% accuracy on training data and 92.21% on testing data. This research provides important insights into Generation Z's perceptions of post-COVID-19 online education and learning platforms to improve the effectiveness of online learning and identify student challenges in the digital era.]]>
<![CDATA[AN ENHANCED MULTI-LAYERED IMAGE ENCRYPTION SCHEME USING 2D HYPERCHAOTIC CROSS-SYSTEM AND LOGISTIC MAP WITH ROUTE TRANSPOSITION ]]>
<![CDATA[Fauzyah, Zahrah Asri Nur]]>;
<![CDATA[Nugraha, Adhitya]]>;
<![CDATA[Luthfiarta, Ardytha]]>;
<![CDATA[Farandi, Muhammad Naufal Erza]]>
<![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.2025.6.1.4007
<![CDATA[In the rapidly evolving digital era, image encryption has become a crucial technique to protect visual data from the threat of information leakage. However, the main challenge in image encryption is improving security against cryptanalysis attacks, such as brute-force and differential attacks, which can compromise the integrity of the encrypted image. Additionally, the creation of efficient and fast encryption schemes that do not degrade image quality remains a significant challenge. This research proposes a multi-layer image encryption scheme that integrates the Logistic Map algorithm, Cross 2D Hyperchaotic (C2HM) system, and Route Transposition techniques. The method aims to enhance the security of digital image encryption by combining chaotic and hyperchaotic systems. The Logistic Map is used to generate a sequence of random values with high chaotic properties, while C2HM contributes to increasing complexity and variability. The Route Transposition technique is applied to scramble pixel positions, further strengthening the encryption’s randomness. The encryption key is derived from a combination of the image hash and user key, which are then used to calculate the initial seed in the chaotic algorithm. Experiments were conducted using standard images with a resolution of 512×512 pixels. The security analysis includes evaluations of NPCR, UACI, histogram analysis, and information entropy. The experimental results show that NPCR consistently exceeds 99.5%, while UACI ranges between 33.23% and 33.56%, indicating high sensitivity to minor changes. Histogram analysis demonstrates an even intensity distribution, and the information entropy value of 7.999 reflects an exceptionally high level of randomness. Robustness tests also indicate that this method can maintain image integrity even when subjected to damage or data loss.]]>
<![CDATA[TEXT CLASSIFICATION OF BULLYING REPORTS USING NLP AND RANDOM FOREST. ]]>
<![CDATA[Aldo, Dasril]]>;
<![CDATA[Paramadini, Adanti Wido]]>;
<![CDATA[Fathoni, M. Yoka]]>
<![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.2025.6.1.4032
<![CDATA[Bullying is a great concern that needs to be dealt with as early as possible, be it in the form of physical, verbal, social or cyber bullying. Using NLP algorithms, this paper intends to classify bullying report using Natural Language Processing in conjunction with Bag of Words. The study employs quantitative methodology. A total of 4671 reports of bullying are in essence categorized into physical, verbal, social, cyber and non-cyber bullying. We split the dataset into 80% training set (3737 reports) and 20% testing set (934 reports). The above model has achieved an accuracy of 94,76%, with good values of recall, precision and F1-score: 94,64%, 95,02% and 94,97% respectively. The dataset is then analyzed using Random Forest algorithm and Report of the Bullying Survey The model is to be effective in automatic Detection of Textual Bullying Reports Automated. While there has been no such effort in our institutions so far, automatic reporting of bullying will prove to be effective. This is because the system will allow a school or institution to have a precise constant monitoring of bullying reports. It will also allow an instantaneous action to be taken to protect the victim without letting the situation escalate.]]>
<![CDATA[PERFORMANCE COMPARISON OF NAIVE BAYES, SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR APPLE VISION PRO SENTIMENT ANALYSIS ]]>
<![CDATA[Pratama, Rangga Rizky]]>;
<![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.2025.6.1.4035
<![CDATA[With the development of spatial computing devices, there arises a need to analyze consumer opinions about products such as the Apple Vision Pro (AVP), a technology that combines augmented reality (AR) and virtual reality (VR). This study aims to analyze consumer opinions on the Apple Vision Pro by utilizing data from the social media platform X. Three algorithms—Random Forest, Support Vector Machine (SVM), and Naïve Bayes—are used in text categorization to identify sentiment trends. Data was collected through a crawling process, resulting in 3,753 entries. After preprocessing and labeling, 2,609 clean data points were obtained, with 1,618 classified as negative and 991 as positive. In sentiment analysis, Random Forest delivered the best performance with an accuracy of 83%, followed by SVM at 80%, and Naïve Bayes at 75%. These results indicate that the Random Forest algorithm is more effective in sentiment categorization related to Apple Vision Pro. This study provides significant contributions to companies in understanding public perceptions and crafting more precise data-driven marketing strategies.]]>
<![CDATA[A TOPIC-BASED APPROACH FOR RECOMMENDING UNDERGRADUATE THESIS SUPERVISOR USING LDA WITH COSINE SIMILARITY ]]>
<![CDATA[Nisa, Laila Rahmatin]]>;
<![CDATA[Luthfiarta, Ardytha]]>;
<![CDATA[Nugraha, Adhitya]]>;
<![CDATA[Hasan, Md. Mahadi]]>;
<![CDATA[Wulandari, Kang, Andini]]>;
<![CDATA[Huda, Alam Muhammad]]>
<![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.2025.6.1.4061
<![CDATA[The thesis is one of the critical factors in determining student graduation. While working on the thesis, students will be guided by a lecturer who has the role and responsibility to ensure that students can prepare the thesis well so that the thesis is ready to be tested and is of good quality. Therefore, selecting a supervisor with the same expertise as the thesis topic is essential in determining students' success in completing their thesis. So far, the selection of thesis supervisors at Dian Nuswantoro University still needs to be done manually by students, so the lack of information about the supervisor can hinder students in determining the supervisor. This study aims to model the topic of lecturer research publications taken from the ResearchGate and Google Scholar platforms so that it is easier for students to choose a thesis supervisor whose research topic is relevant to the student's thesis using the Latent Dirichlet Allocation method. The LDA method will mark each word in the topic in a semi-random distribution. It will calculate the probability of the topic in the dataset and the likelihood of the word against the topic for each iteration. The results of LDA modeling present six main topics of lecturer research with the highest coherence score of 0.764, and then the resulting topics and thesis titles will be compared using cosine similarity. Students can use The highest cosine value as a reference when determining the right thesis topic. Thus, the supervisor selection process will be more focused and in accordance with the student's research interests.]]>
<![CDATA[DETECTION OF BULLYING CONTENT IN ONLINE NEWS USING A COMBINATION OF RoBERTa-BiLSTM ]]>
<![CDATA[Zamroni, Moh. Rosidi]]>;
<![CDATA[Hamid, Rahayu A]]>;
<![CDATA[Mujilahwati, Siti]]>;
<![CDATA[Sholihin, Miftahus]]>;
<![CDATA[Leksana, Dinar Mahdalena]]>
<![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.2025.6.1.4140
<![CDATA[This research aims to build a bullying-themed online news classification system with a combined approach of RoBERTa embedding and BiLSTM. RoBERTa is used to generate context-rich text representations, while BiLSTM captures temporal relationships between words, thereby improving classification performance. The research dataset consisted of news from reputable portals such as Kompas.com, Detik.com, and iNews.com, labeled according to keywords relevant to the theme of bullying. The results of the experiment showed that the model achieved 95.2% accuracy, 98.2% precision, 93.6% recall, and 95.8% F1-score. Although there are few prediction errors (false positives and false negatives), this model shows excellent performance in detecting and classifying bullying-themed news. The main contribution of this research is the development of a new approach that combines RoBERTa and BiLSTM for the classification of complex bullying-themed news. This approach not only improves the accuracy of classification but can also be implemented in automated systems to detect negative content. Thus, this research has the potential to support the creation of a healthier digital space and encourage more responsible media practices.]]>
<![CDATA[FOOTBALL PLAYER TRACKING, TEAM ASSIGNMENT, AND SPEED ESTIMATION USING YOLOV5 AND OPTICAL FLOW ]]>
<![CDATA[Hartono, Matthew Raymond]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Ali, Rabei Raad]]>
<![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.2025.6.1.4165
<![CDATA[Football analysis is indispensable in improving team performance, developing strategy, and assessing the capabilities of players. A powerful system that combines YOLOv5 for object detection with optical flow tracks football players, assigns them to their respective teams, and estimates their speeds accurately. In the most crowded scenarios, the players and the ball are detected by YOLOv5 at 94.8% and 93.7% mAP, respectively. KMeans clustering based on jersey color assigns teams with 92.5% accuracy. Optical flow is estimating the speed with less than 2.3%. The perspective transformation using OpenCV improves trajectory and distance measurement, overcoming the challenges in overlapping players and changing camera angles. Experimental results underlined the system's reliability for capturing player speeds from 3 to 25 km/h and gave insight into the dynamic nature of team possession. However, there is still some challenge: 6% accuracy degradation in high overlap and illuminative changes. The future work involves expanding the dataset for higher robustness and ball tracking, which will comprehensively explain the dynamics of a match. The paper presents a flexible framework for automated football video analysis that paves the way for advanced sports analytics. This would also, in turn, enhance informed decision-making by coaches, analysts, and broadcasters by providing them with actionable metrics during training and competition. The proposed system joins the state-of-the-art YOLOv5 with optical flow and thereby forms the backbone of near-future football analysis.]]>
<![CDATA[DYNAMIC WEIGHT ALLOCATION IN MODIFIED MULTI-ATRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS WITH SYMMETRY POINT FOR REAL-TIME DECISION SUPPORT ]]>
<![CDATA[Hadad, Sitna Hajar]]>;
<![CDATA[Chandra, Iryanto]]>;
<![CDATA[Wang, Junhai]]>;
<![CDATA[Megawaty, Dyah Ayu]]>;
<![CDATA[Setiawansyah, Setiawansyah]]>;
<![CDATA[Yudhistira, Aditia]]>
<![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.2025.6.1.4170
<![CDATA[Decision Support Systems (DSS) have a crucial role in real-time decision-making, especially in the digital era that demands high speed and accuracy. Managing criterion weights in a dynamic environment presents significant challenges due to rapid and unpredictable changes in conditions. However, determining an accurate weight becomes difficult due to uncertainty, incomplete data, and subjective factors from decision-makers. In addition, changes in the external environment, such as market trends, regulations, or customer preferences, can affect the relevance of each criterion, thus requiring a real-time weight adjustment mechanism. The purpose of this study is to develop and explore the dynamic weight allocation method in symmetry point- multi-attributive ideal-real comparative analysis (S-MAIRCA) to support more accurate and responsive real-time decision-making in a dynamic environment. This research contributes to the understanding of how the weights of criteria can be adjusted automatically and responsively to changing conditions or new data, which increases the relevance and accuracy of decisions in a dynamic environment. The urgency of S-MAIRCA research is important because it often involves real-time, dynamic, and complex data. This development not only improves the adaptability of the S-MAIRCA method, but also contributes significantly to creating computer science-based applications that are more intelligent, flexible, and relevant to the evolving needs of the system. The results of the alternative ranking comparison using the CRITIC-MAIRCA, LOPCOW-MAIRCA, ROC-MAIRCA, and S-MAIRCA methods showed variations in the ranking order generated for each alternative using spearman correlation. The results of the correlation value of CRITIC-MAIRCA and LOPCOW-MAIRCA have a very high correlation of 0.993, which shows that these two methods provide almost identical rankings in alternative evaluation. Likewise, CRITIC-MAIRCA and S-MAIRCA had a high correlation of 0.979, signaling a strong similarity in ranking results despite slight differences in the approaches used by the two methods. The results of the application of the MAIRCA-S method in the development of DSS based on real-time data have a significant impact on improving the speed, accuracy, and adaptability of decisions. MAIRCA-S strengthens the validity of decision results by considering a variety of attributes on a more comprehensive scale, providing added value in the development of DSS for various industrial sectors.]]>
<![CDATA[PERFORMANCE ANALYSIS OF EXTRACT, TRANSFORM, AND LOAD METHODS FOR BUSINESS INTELLIGENCE IN E-LEARNING SYSTEMS USING PENTAHO DATA INTEGRATION ]]>
<![CDATA[Saputra, Aulia Kukuh]]>;
<![CDATA[Laksitowening, Kusuma Ayu]]>;
<![CDATA[Herdiani, Anisa]]>
<![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.2025.6.1.4173
<![CDATA[The rapid adoption of Learning Management Systems (LMS) in higher education has resulted in the generation of large and complex datasets, posing significant challenges for efficient data integration and analysis. The urgency to address these challenges is driven by the growing demand for real-time analytics and data-driven decision-making in educational institutions. This study advances the field of computer science by evaluating and comparing the performance of three Extract, Transform, and Load (ETL) methods—Table Output, Sync After Merge, and Switch Case—using Pentaho Data Integration (PDI). The study introduces novel insights into ETL optimization techniques, focusing on execution time as the primary metric, critical for ensuring timely and reliable insights in Business Intelligence (BI) systems. Performance testing was conducted with synthetic datasets ranging from 150 to 1,000,000 records across five scenarios: data addition, synchronization, insertion, deletion, and combined operations. Results reveal that Sync After Merge outperformed other methods, achieving up to 35% faster execution times, particularly with large datasets. These findings contribute significantly to the advancement of data integration techniques in computer science, enabling institutions to optimize their BI systems, enhance system responsiveness, and support data-driven decision-making processes effectively. The research provides valuable insights for developing scalable ETL solutions in educational technology systems.]]>
