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[COMPARATIVE ANALYSIS OF PERFORMANCE AND EFFICIENCY OF LOAD BALANCING ALGORITHMS ON INGRESS CONTROLLER ]]>
<![CDATA[Khamdani, Ahmad Rizal]]>;
<![CDATA[Muslikh, Ahmad Rofiqul]]>;
<![CDATA[Affandi, Arif Saivul]]>
<![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.2025.6.1.4040
<![CDATA[Kubernetes has become the dominant container orchestration platform in production environments, with the ingress controller playing a critical role in managing external traffic to services within the cluster. This study aims to provide recommendations for optimal load balancing algorithms for Kubernetes production environments by analyzing and comparing the performance of four algorithms namely round robin, static-rr, least connection, and random on the HAProxy ingress controller. The research method is conducted through observation using k6 and Grafana performance test tools, as well as literature studies, with measurements including total requests, throughput, latency, CPU usage, and memory at various levels of user load. The data was analyzed using descriptive statistical techniques, normality test, homogeneity test, and tests for group differences using one-way ANOVA or Kruskal-Wallis H. The results show that static-rr excels in throughput, total requests, and CPU and memory efficiency at high load, while least connection is more effective for latency at low load. Round robin and random showed stable performance at low load but less optimal at high load. The conclusion of this study is that choosing the right load balancing algorithm depends on the load characteristics and desired performance metrics, to ensure optimal Kubernetes performance under various load scenarios in production environments.]]>
<![CDATA[ENHANCED SPEED AND ACCURACY IN COCOA FRUIT DISEASE IDENTIFICATION USING THE INCEPTION-RESNET CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM ]]>
<![CDATA[Iskandar, Dadang]]>;
<![CDATA[Novanto, Adi]]>;
<![CDATA[Kurniawan, Yogiek Indra]]>
<![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.2025.6.1.4144
<![CDATA[The increase in world cocoa consumption is not accompanied by an increase in production, causing a problem of supply shortages in the world. One of the causes of the stagnation in the increase in cocoa production is due to diseases that attack cocoa fruit. The disease can cause unproductive plants, unusable cocoa fruit, and even cause the spread of epidemics in a cocoa fruit garden. One of the preventions that can be done is to identify diseases in cocoa fruit in order to reduce the spread of the disease. The identification process is usually carried out independently by farmers. Identification of cocoa fruit diseases requires knowledge and experience by farmers, so it can cause misidentification or failure to identify the disease. In addition, other factors can arise such as the number of farmers who check, the area of the cocoa fruit garden, and the urgency of identification. To help overcome these problems, a Convolutional Neural Network (CNN) model was developed with the Inception and ResNet architectures. The data used were images obtained from Davao City, Philippines. The model obtained from the analyzed dataset got the best results of 0.99, a specificity value of 0.99, and an F1-score value of 0.99. The model configuration used is a learning rate value of 0.0001, RMSProp optimization function, initialization function (x) He uniform, initialization function (y) Glorot normal, and a batch size of 32.]]>
<![CDATA[IMPROVING SHOPPING EXPERIENCES AT NTB MALL THROUGH PERSONALIZED PRODUCT RECOMMENDATIONS USING CONTENT-BASED FILTERING ]]>
<![CDATA[Husodo, Ario Yudo]]>;
<![CDATA[Bimantoro, Fitri]]>;
<![CDATA[Agitha, Nadiyasari]]>;
<![CDATA[Grendis, Nuraqilla Waidha Bintang]]>
<![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.2025.6.1.4194
<![CDATA[NTB MALL, an e-commerce platform specializing in unique products from micro, small, and medium enterprises (MSMEs) in West Nusa Tenggara, faces challenges in providing personalized product recommendations due to the diversity of its product categories and consumer preferences. To address this, this study implements a content-based filtering (CBF) approach utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to enhance recommendation accuracy. The system analyzes product attributes and user interaction history to generate tailored suggestions. Experimental results indicate that cosine similarity outperforms Euclidean distance in recommendation precision, achieving an accuracy of 89% and a Mean Reciprocal Rank (MRR) of 95%. Furthermore, user feedback reveals that 93% of users found the recommendations highly relevant, 89% reported increased engagement, and 96% expressed satisfaction with the personalized shopping experience. This research provides a novel application of AI-driven recommendation systems in regional e-commerce marketplaces, demonstrating their potential to improve user experience and foster stronger connections between consumers and local producers.]]>
<![CDATA[the ENHANCE OBJECT TRACKING ON AUGMENTED REALITY USING HYBRID CONVOLUTIONAL NEURAL NETWORK AND FAST CORNER DETECTION ]]>
<![CDATA[nurhadi, nurhadi]]>;
<![CDATA[Winanto, Eko Arip]]>;
<![CDATA[Saparudin]]>
<![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.2025.6.1.4257
<![CDATA[Markerless augmented reality (AR) is utilized in applications that do not require anchoring to the real world and do not require the use of physical markers (fiducial markers). Augmented object displays not only float but also allow for the automatic placement of 3D augmented reality objects on flat surfaces to enhance realism in real time. There are two challenges that need to be addressed in Markerless AR systems: object tracking and registration, as well as the influence of light intensity. Therefore, the objective of this research is to propose the use of Convolutional Neural Networks (CNN) and Features from Accelerated Segment Test (FAST) corner detection for tracking or detecting objects in markerless augmented reality systems. Testing was conducted using three epoch schemes: 10, 50, and 100. The test results were measured using several parameters, including the execution time, testing loss, and testing accuracy. The test results indicated an improvement in the performance of the tested object detection. The accuracy testing results of using the CNN and FAST corner detection methods were superior to those of the CNN-only method and FAST corner detection alone, reaching 98%. However, this method increases the processing time for object detection. Thus, the processing time of the CNN without FAST corner detection was faster.]]>
<![CDATA[AN EVALUATION OF THE SUCCESSFUL IMPLEMENTATION OF THE INFORMATION SYSTEM PLATFORM MERDEKA MENGAJAR USING HUMAN ORGANIZATION TECHNOLOGY FIT MODEL APPROACH ]]>
<![CDATA[Abidin, Uun]]>;
<![CDATA[Hariguna, Taqwa]]>;
<![CDATA[Barkah, Azhari Shouni]]>
<![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.2025.6.1.4282
<![CDATA[The implementation of technology in education has great potential to improve the quality of learning that supports the implementation of the Merdeka curriculum. The Merdeka Mengajar platform (MMP) is designed to help educators by providing various features including self-development, inspiration and teaching. Uneven ICT infrastructure and teachers' personal abilities are problems in the implementation of the MMP, so it is necessary to analyze the success of the implementation of the MMP. The purpose of this study is to analyze the success of the implementation of the information system for the Merdeka Mengajar Platform by adopting the Hot Fit Model by expanding the Technology component with the ICT Infrastructure variable, expanding the Human component with the personal competence variable, expanding the organizational component with the organizational culture variable and the training & learning variable which can affect the successful implementation of the MMP. The data obtained were 328 respondents who were analyzed using SmartPLS 3.2.9. The analysis results obtained the proposed conceptual model has an accuracy of 58.6%. Net benefits are influenced by system use, user satisfaction, personal competence, structure, environment, organizational culture, and training & learning. Service quality, system quality, information quality, and ICT infrastructure have a positive impact on system use and user satisfaction.]]>
<![CDATA[A COMPARATIVE STUDY OF MULTI-MASTER REPLICATION OF NOSQL DATABASE SERVER WITH VARYING DATA FORMATS ]]>
<![CDATA[Wibowo, Dwi Kurnia]]>;
<![CDATA[Darmawan, Agus]]>;
<![CDATA[Nawangnugraeni, Devi Astri]]>
<![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.2025.6.1.4371
<![CDATA[NoSQL Databases are currently an effective solution for managing large data sets distributed across many Servers. NoSQL Database design is usually based on its usability. Specifically related to the system or application to be built. This research aims to measure the Transfer Rate, CPU usage, Memory usage, query execution time for Create, Insert, Delete and remote replication query bandwidth in the Multi-Master Server replication process using two document stored NoSQL Database applications namely CouchBase and CouchDB by entering three different data models namely JSON, XML and CSV. The experimental results show that the Transfer Rate with CSV data format on CouchBase has the lowest value with an average of 111.41 kbps. CPU usage with XML data format on CouchBase has the lowest value with an average of 13.89%. Memory usage with JSON data format on CouchBase has the lowest value with an average of 1.68%. Query Execution Time Create with XML data format on CouchBase has the lowest value with an average of 1.16 seconds. Query Execution Time Insert on CouchBase with CSV data format has the lowest value with an average of 33.28 seconds. Bandwidth Query Execution Time Insert with CSV data format on CouchBase has the lowest value with an average of 24.78 mb. Query Execution Time Delete with JSON, XML and CSV data formats on CouchDB has the lowest value with an average of 1.5 seconds. Further research recommendations are to test Multi-Master Server Replication using other data formats and parameters or test the performance of data migration to other Databases with different data formats.]]>
<![CDATA[IMPLEMENTATION OF TEXT MINING ON SONG LYRICS FOR SONG CLASSIFICATION BASED ON EMOTION USING WEBSITE-BASED LOGISTIC REGRESSION ]]>
<![CDATA[Rahayu, Swahesti Puspita]]>;
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Yunindar, Galih Arditiya]]>
<![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.2025.6.1.4429
<![CDATA[Music has become an essential medium for expressing emotions and enriching human social experiences. However, the manual interpretation of emotions in song lyrics is often inaccurate and time-consuming, especially for complex or ambiguous lyrics. This creates a need for an automated system that can improve the accuracy and efficiency of emotion classification in song lyrics. Various algorithms, such as K-Nearest Neighbor (K-NN), Naive Bayes Classifier, and Support Vector Machine (SVM), have been applied for emotion classification in song lyrics. Previous research has shown that SVM combined with Particle Swarm Optimization (PSO) achieves an accuracy of up to 90%, while K-NN with feature selection produces the highest f-measure of 66.93%, and Naive Bayes achieves an accuracy of up to 45%. In this study, the Logistic Regression algorithm, supported by the Term Frequency-Inverse Document Frequency (TF-IDF) method, is applied to enhance the accuracy of emotion classification. Evaluation results indicate that the model with figurative language transformation achieves a higher accuracy (93.52%) compared to the model without figurative language transformation (92.31%), demonstrating that figurative language contributes to the richness of emotional expression recognized by the model. This model shows competitive results and can be compared to SVM using PSO while providing better performance than K-NN and Naive Bayes. The system implementation is web-based using the Streamlit framework, allowing users to input lyrics and obtain interactive emotion predictions. This research contributes to the analysis of music emotions and offers an efficient and more accessible alternative for emotion classification in song lyrics.]]>
<![CDATA[Evaluation of K-Means, DBSCAN, and Hierarchical Clustering for Strategic Segmentation of Tourism SMEs in Rembang, Indonesia ]]>
<![CDATA[Ramadhan, Ardiansyah]]>;
<![CDATA[Achmad, Fandi]]>;
<![CDATA[Zulkarnain, Ibnu]]>;
<![CDATA[Aritsugi, Masayoshi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4602
<![CDATA[Small and Medium Enterprises (SMEs) play a crucial role in job creation, regional competitiveness, and economic equity. In the tourism sector, particularly in ecotourism and cultural tourism, clustering SMEs presents challenges due to complex and interrelated data variables. This study aims to evaluate the effectiveness of three clustering algorithms—K-Means, DBSCAN, and Hierarchical Clustering—in segmenting SMEs based on real-world tourism datasets. A purposive sampling method was applied to 203 valid respondents from SMEs in Rembang Regency, Central Java. Clustering performance was assessed using the Silhouette Coefficient and Davies-Bouldin Index, while computational efficiency and scalability were analyzed through execution time and memory usage. The results show that DBSCAN achieved the best clustering quality (Silhouette Coefficient: 0.5496, Davies-Bouldin Index: 0.3298), effectively managing noise and irregular cluster shapes. Hierarchical clustering offered moderate quality and helped reveal relationships between SMEs. In contrast, K-Means demonstrated the lowest quality (Silhouette Coefficient: 0.2321) due to its limitation in handling non-spherical clusters. For computational efficiency, Hierarchical Clustering required the least memory (0.14 MB) and shortest execution time (5.73 seconds), while K-Means took the longest time (26.00 seconds). DBSCAN consumed more memory due to density-based processing. K-Means was the most stable in scalability testing with increasing dataset sizes, whereas Hierarchical Clustering showed inefficiency. The findings support selecting appropriate clustering methods based on data complexity and size. This study enhances data-driven tourism development strategies and advances clustering methodology for applied informatics. Future work may explore hybrid clustering and predictive models for deeper insights.]]>
<![CDATA[Multi-Class Mangrove Classification Using Transfer Learning with MobileNet-V3 on Multi-Organ Images ]]>
<![CDATA[Sudrajat, Ari]]>;
<![CDATA[Apnena, Riri Damayanti]]>;
<![CDATA[Rahayu, Ayu Hendrati]]>;
<![CDATA[Iqtait, Musab]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4683
<![CDATA[Mangrove ecosystems are important for coastal protection, biodiversity conservation, and climate change mitigation. However, the accurate identification of mangrove species is very challenging due to the morphological similarities between different species, especially when the species are analyzed based on limited plant organs like leaves or stems. Manual identification methods have traditionally been time-consuming, error-prone, and require expert knowledge. Addressing these issues, this research suggests an automatic classification system based on Deep Learning techniques by leveraging the MobileNet-V3 architecture. The system is based on images of three different plant organs—leaves, stems, and seeds—of five mangrove species: Avicennia marina, Avicennia officinalis, Avicennia rumphiana, Rhizophora mucronata, and Sonneratia alba. Data augmentation techniques such as rotation, shifting, and flipping, as well as sharpness enhancement, were applied in the preprocessing step to enhance data variability and ease model generalization. The model was trained with a carefully selected set of hyperparameters and extensively validated through training and testing steps. The experiment results demonstrated outstanding performance with a training accuracy of 99.88% and perfect precision, recall, and F1-score values of 100%. Furthermore, testing with unseen data confirmed the robustness of the model since all test samples were correctly identified. This research concludes that the MobileNet-V3 architecture offers an effective approach to mangrove species classification and suggests that future work should involve larger and more varied datasets, real-world field environments, and the investigation of ensemble models to further extend the adaptability and scalability of mangrove monitoring systems.]]>
<![CDATA[Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images ]]>
<![CDATA[Habie, Khairul Fathan]]>;
<![CDATA[Murinto, Murinto]]>;
<![CDATA[Sunardi, Sunardi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4685
<![CDATA[Automatic detection of skin diseases using digital images is a growing field in the application of deep learning in the medical world, especially to help the early diagnosis process. One of the most widely used models is MobileNetV1 because it is lightweight and efficient in image processing. However, the performance of the model is greatly affected by the training configuration, including the type of optimizer used. This study aims to compare the effectiveness of six types of optimizers, namely SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, and Nadam in training MobileNetV1 models for human skin disease image classification. The model was trained on annotated skin image dataset with predetermined training parameters: batch size 32, learning rate of 0.0001, and 10 epochs. Performance evaluation was performed using accuracy metrics. The results obtained demonstrate that RMSprop performs best, with 99.10% accuracy, 99.14% precision, 99.10% recall, and a 99.10% F1-score. Adadelta showed the lowest performance consistently, with only 22.22% accuracy, 20.34% precision, 22.22% recall, and 18.42% F1-score. This finding confirms that the type of optimizer affects the effectiveness of model training, especially in medical image classification tasks. This research provides empirical insights that are useful in selecting the optimal optimizer for MobileNetV1 model implementation in the healthcare domain.]]>
