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[EFFECTIVENESS HYPERPARAMETER TUNING ON RANDOM FOREST, LINEAR DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION AND NAÏVE BAYES ALGORITHMS FOR DETECTING DOS NETWORK ATTACKS ]]>
<![CDATA[Saputri, Inka]]>;
<![CDATA[Arsi, Primandani]]>;
<![CDATA[Isnaini, Khairunnisak Nur]]>
<![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.2025.6.1.4175
<![CDATA[Denial of Service (DoS) attacks are a major threat to network security, characterized by overwhelming system resources with illegitimate requests. Such attacks can disrupt critical services and cause substantial financial losses. However, there is still a need for a more efficient model to detect DoS attack with high accuracy. The aim of this research is to determine the impact of hyperparameter tuning on the four algorithms to identify the best algorithm for detecting DoS network attacks. The research method involves data preprocessing, feature selection, encoding, balancing using SMOTE (Synthetic Minority Over-Sampling Techinuque) and evaluation using confusion matrix. This research use the NSL-KDD dataset because it is relevant for DoS attack detection and flexible for testing various classification algorithms and utilizing hyperparameter tuning. This study evaluates the effectiveness hyperparameter tuning on several machine learning alghorithms namely Random Forest, Linear Discriminant Analysis (LDA), Logistic Regression and Naïve Bayes in detecting DoS attacks. Results indicate that Random Forest achieves highest accuracy (99,97%) and robust performance across all metrics, demonstrating superior generalization and precision. LDA, Logistic Regression and Naïve Bayes also performed well but fell short of Random Forest in handling complex patterns in the dataset. The utilization of hyperparameter tuning can improve the accuracy, consistency and efficiency of the algorithm so as to optimize the combination of various parameters in detecting DoS attacks. The findings provide valuable insights into selecting suitable algorithms for future implementations in cybersecurity systems.]]>
<![CDATA[PERFORMANCE COMPARISON OF NAIVE BAYES AND BIDIRECTIONAL LSTM ALGORITHMS IN BSI MOBILE REVIEW SENTIMENT ANALYSIS ]]>
<![CDATA[Ma'we, Hannatul]]>;
<![CDATA[Husodo, Ario Yudo]]>;
<![CDATA[Irmawati, Budi]]>
<![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.2024.5.6.4178
<![CDATA[Currently, almost all banks have used mobile banking in conducting banking transactions, one of which is Bank Syariah Indonesia (BSI). BSI mobile is still classified as a new mobile banking application compared to other mobile banking, this certainly still has a low rating and really needs feedback from users which can be seen through reviews on the Google Play Store application. Input in the form of criticism and suggestions from BSI mobile users can be used by BSI mobile as a suggestion for careful supervision and evaluation material in improving its services. This study aims to find the best algorithm to analyze review sentiment on the Google Play Store for the BSI mobile application and provide an overview of the response of application users to application developers based on the results of review data processing. The data mining methodology used in this study is CRISP-DM, using a dataset collected for 6 years (2018-2023) which is annotated into positive and negative labels manually, then modeled using 2 algorithms, namely Naïve Bayes (NB) and Bidirectional LSTM (BiLSTM). The contribution of this study is to test, evaluate and compare the two algorithms (NB and BiLSTM) using the K-Fold Cross Validation (NB) testing model and over-sampling techniques to the minority class (negative) then provide recommendations for the best algorithm. The conclusion of the study is that the BiLSTM algorithm is superior to NB with an accuracy of 94.90 % while the NB algorithm is 94%. In addition, the over-sampling technique is more optimal in increasing the accuracy of the algorithm's performance compared to without over-sampling.]]>
<![CDATA[COMPARISON OF DBSCAN AND K-MEANS CLUSTER ANALYSIS WITH PATH-ANOVA IN CLUSTERING WASTE MANAGEMENT BEHAVIOUR PATTERNS ]]>
<![CDATA[Zuhdi, Muhammad Rizal]]>;
<![CDATA[Al Jauhar, Hafizh Syihabuddin]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Wardhani, Ni Wayan Surya]]>
<![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.2025.6.1.4183
<![CDATA[This study aims to compare the effectiveness of DBSCAN and K-Means cluster analysis methods in clustering waste management behaviour patterns in Batu City. The data used is secondary data from previous research with a total of 395 respondents taken using the quota sampling method. DBSCAN classifies data based on density with the main parameters epsilon and MinPts, while K-Means uses the average centroid to determine the cluster. The analysis results show that DBSCAN produces a silhouette index of 0.664, which is higher than K-Means with a value of 0.574. DBSCAN also successfully identified noise as much as 10 data that did not belong to any cluster, while K-Means did not have a similar mechanism. The results of Path-ANOVA show that DBSCAN is the most optimal clustering with a more significant partition difference value. Further tests were conducted to strengthen the validation of clustering results using Path-ANOVA. Both methods produced two main clusters, with the second cluster showing higher quality in terms of maintenance, quality, and ease of use of environmental hygiene facilities. This research emphasises the importance of choosing an appropriate clustering method to ensure optimal clustering results, especially in data with complex characteristics.]]>
<![CDATA[A STUDY OF WORLDWIDE PATTERNS IN ALPHABET SIGN LANGUAGE RECOGNITION USING CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS ]]>
<![CDATA[Rakhmadi, Aris]]>;
<![CDATA[Yudhana, Anton]]>;
<![CDATA[Sunardi, Sunardi]]>
<![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.2025.6.1.4202
<![CDATA[Sign Language Recognition (SLR) has become an essential area of research due to its potential to promote understanding between the deaf and hearing communities through communication. This paper provides an in-depth study of various methodologies and models employed in SLR, focusing on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). We analyze their application to datasets from various sign languages, such as Arabic Sign Language (ArSL), American Sign Language (ASL), and British Sign Language (BSL), and explore how these models improve the recognition of dynamic, multi-dimensional hand gestures. This research not only advances the understanding of deep learning applications in sign language recognition but also addresses critical challenges in data processing and real-time applications, paving the way for inclusive technologies in informatics and human-computer interaction. Despite the progress in applying deep learning techniques to SLR, several challenges remain, particularly in dataset limitations, handling large vocabularies, and ensuring consistent performance across diverse environments and signers. The paper also investigates the broader applications of SLR, such as virtual reality, healthcare, education, and accessibility, and discusses the integration of SLR with human-computer interaction systems. Furthermore, it highlights current limitations in the field, such as difficulties with video data handling, the need for standard datasets, and issues related to training computational models. Finally, the paper outlines future research directions, including developing more robust SLR systems that can function effectively in uncontrolled environments, improving data collection methodologies, and creating real-time, user-friendly applications to assist the community of deaf and hard-of-hearing individuals.]]>
<![CDATA[DESIGN AND IMPLEMENTATION OF A METAVERSE-BASED PLATFORM FOR THEMATIC TOURISM VILLAGES ]]>
<![CDATA[Aditya, Addin]]>;
<![CDATA[Kurniawan, Rahmat]]>;
<![CDATA[Maulana, Fatih]]>;
<![CDATA[Darwanza, Muhammad Alif Nur]]>
<![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.2025.6.1.4209
<![CDATA[This research investigates the integration of metaverse technology into the design of thematic tourism villages to address the declining appeal of such destinations and the limited adoption of digital tools for promotion and education. Using the Kayutangan Heritage Village in Malang as a case study, the study employs a Design Thinking approach to identify challenges, ideate solutions, and develop a metaverse-based platform prototype. This platform leverages immersive 3D environments to enable users to explore cultural heritage and historical narratives in a virtual context. The findings highlight the platform's ability to create resilient, engaging, and educational tourism experiences that attract diverse audiences while supporting local economies. By offering innovative, interactive ways to connect with cultural heritage, the metaverse platform demonstrates its potential as a transformative tool for digital tourism. The research contributes to the broader discourse on sustainable tourism by showcasing the metaverse’s role in preserving cultural heritage and enhancing digital connectivity. Its impact is evident in fostering a deeper understanding of cultural assets and promoting digital tourism innovations that align with contemporary market demands, thus paving the way for the widespread adoption of metaverse technology in similar contexts.]]>
<![CDATA[ENHANCING SENTIMENT ANALYSIS OF THE 2024 INDONESIAN PRESIDENTIAL INAUGURATION ON X USING SMOTE-OPTIMIZED NAIVE BAYES CLASSIFIER ]]>
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Khanza, Muthia]]>;
<![CDATA[Zahira Hasyati, Adila]]>
<![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.2025.6.1.4290
<![CDATA[The inauguration of the President and Vice President of Indonesia for the 2024-2029 period has drawn significant public attention, reflecting widespread political and societal interest. This study aims to optimize sentiment analysis of public opinion on X (formerly Twitter) regarding the inauguration by enhancing the Naïve Bayes Classifier (NBC) with the Synthetic Minority Over-sampling Technique (SMOTE). Addressing the issue of class imbalance in sentiment data, the research demonstrates how SMOTE improves classification robustness. The methodology includes data crawling from X, preprocessing involving tokenization, stemming, and TF-IDF feature extraction, and sentiment labeling using TextBlob. Sentiment classification is conducted with NBC, evaluated under conditions with and without SMOTE. Metrics such as accuracy, precision, recall, and F1-score are utilized to assess performance. Results indicate that the application of SMOTE increases the accuracy of NBC from 98% to 99%, with precision improving from 0.98 to 1 and recall maintaining high levels (0.99). This 1% accuracy enhancement underscores the significance of addressing class imbalance for reliable sentiment analysis. The findings contribute to a better understanding of public sentiment during critical political events and highlight the effectiveness of SMOTE in improving text classification tasks. This research provides valuable insights into leveraging machine learning techniques for analyzing imbalanced datasets, offering implications for both academic and practical applications in sentiment analysis and political studies.]]>
<![CDATA[IMPLEMENTATION OF THE RANDOM FOREST METHOD FOR CLASSIFYING LUNG X-RAY IMAGE ABNORMALITIES ]]>
<![CDATA[Supriyanti, Retno]]>;
<![CDATA[Fadlola, M. Gus Solhan]]>;
<![CDATA[Aliim, M. Syaiful]]>;
<![CDATA[Ramadhani, Yogi]]>
<![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.2025.6.1.4323
<![CDATA[The Covid-19 pandemic has caused a severe global health crisis. Rapid and accurate diagnostics are essential in combating this disease. In this regard, lung X-ray images have become critical for identifying Covid-19 infections. The method used in this study is random forest, a classification method based on ensemble modeling of decision trees. The lung X-ray images used in this study were taken from a datasheet containing images from COVID-19 patients and images from non-Covid-19 patients. The data pre-processing process involves extracting features from the images using image processing techniques and statistical analysis. The random forest model is trained using the processed datasheet to classify the lung X-ray images. The model's performance is evaluated using accuracy, sensitivity, and specificity metrics. In addition, cross-validation is used to measure the reliability and generalization of the model. The study results showed that the random forest method achieved good classification performance in distinguishing COVID-19 lung X-ray images from normal ones. The resulting model provided high accuracy and good sensitivity in identifying Covid-19 cases. These results show the potential of the random forest method in supporting early diagnosis and treatment of COVID-19 disease.]]>
<![CDATA[K-ALLY BASED DYNAMIC FUZZY CLUSTERING FOR GEOPOLITICAL ALLIANCE ANALYSIS: A CASE STUDY INSPIRED BY THE RUSSIAN-UKRAINIAN CONFLICT ]]>
<![CDATA[Munirah]]>;
<![CDATA[Alwi, Aslan]]>;
<![CDATA[Sudarno]]>;
<![CDATA[Triyanto, Andy]]>
<![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.2025.6.1.907
<![CDATA[Geopolitical alliances are often based on a combination of factors such as geographic proximity, military strength, and strategic interests. In this research, we introduce the K-Ally algorithm based on Dynamic Fuzzy Clustering to dynamically analyze alliance patterns between countries. Using fuzzy logic and adaptive thresholds, this algorithm evaluates the potential benefits of alliances based on key attributes, such as geographic distance and power differences. This study is inspired by the allied dynamics that emerged in the Russian-Ukrainian war, where changes in strategy and international relations were key to the continuation of the conflict. The paper also compare this algorithm with the K-Means method commonly used in geopolitical data analysis. Experimental results show that K-Ally based on Dynamic Fuzzy Clustering is able to capture alliance dynamics better than K-Means, especially in conditions of uncertainty or attribute imbalance between countries. This research contributes to the development of new analytical tools for the study of geopolitics and international conflict.]]>
<![CDATA[SYSTEMATIC LITERATURE REVIEW OF DOCUMENTS SIMILARITY DETECTION IN THE LEGAL FIELD: TREND, IMPLEMENTATION, OPPORTUNITIES AND CHALLENGE USING THE KITCHENHAM METHOD ]]>
<![CDATA[Nazuli, Muhammad Furqan]]>;
<![CDATA[Walhidayah, Irfan]]>;
<![CDATA[Akhyar, Amany]]>;
<![CDATA[Saptawati Soekidjo, Gusti Ayu Putri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2444
<![CDATA[This research conducted a Systematic Literature Review (SLR) to observe the application of graph mining techniques in detecting document law similarities. Graph mining, where nodes and edges represent entities and relations respectively, has proven effective in identifying patterns within legal documents. This review encompasses 93 relevant studies published over the past five years. Despite its potential, graph mining in the legal domain faces challenges, such as the complexity of implementation and the necessity for high-quality data. There is a need to better understand how these techniques can be optimized and applied effectively to address these challenges. This SLR utilized a comprehensive approach to identify and analyze trends, implementations, and popular domains related to graph mining in legal documents. The study reviewed trends in the number of studies, categorized the implementations, and evaluated the prevalent techniques employed. The review reveals a growing trend in the use of graph mining techniques, with a noticeable increase in the number of studies year by year. The implementation of these techniques is the most popular category, with applications predominantly in legal domains such as laws, legal documents, and case law. The most frequently used graph mining techniques involve Natural Language Processing (NLP), Information Retrieval, and Deep Learning. Although challenges persist, including complex implementation and the need for quality data, graph mining remains a promising approach for developing future information systems in law.]]>
<![CDATA[IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK USING MOBILENETV2 TO DISTINGUISH HUMAN AND ARTIFICIAL INTELLIGENCE PAINTING ]]>
<![CDATA[Santosa, Dwi Bagia]]>;
<![CDATA[Wahana, Agung]]>;
<![CDATA[Uriawan, Wisnu]]>
<![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.2025.6.1.3827
<![CDATA[The advancement of artificial intelligence technology has had a significant impact on various fields, including painting. Artificial intelligence is now able to create works of art that resemble paintings produced by humans with a high level of detail and complexity. However, this progress has also created new problems in the world of painting, namely the difficulty in distinguishing between works produced by humans and those created by artificial intelligence. This problem has an impact on the originality of the artwork and has implications for aspects of ethics and creativity. This study aims to develop a deep learning model that can classify human and artificial intelligence paintings, and overcome the challenges in distinguishing between the two. The methodology used is the Cross Industry Standard Process for Data Mining (CRISP-DM), with a dataset consisting of 1,000 painting images. The architecture used is MobileNetV2, implemented using TensorFlow to build a Convolutional Neural Network (CNN). Techniques such as data preparation, data labeling, data splitting, resizing, and data augmentation are applied to improve model performance. Six test scenarios were carried out with variations in the learning rate, number of epochs, and freeze or unfreeze configurations on the base model. The results showed that the best model with a learning rate of 0.0001, base model unfreeze, and 5 epochs managed to achieve an accuracy of 97%, without any indication of overfitting or underfitting. This model was then implemented on an Android application in TFLite format, which can predict image classes with a confidence level of 89.98%.]]>
