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.
Articles
1,174 Documents
<![CDATA[Improving Vegetation Encroachment Detection in Powerline Areas Using EfficientNet-Based U-Net Semantic Segmentation ]]>
<![CDATA[Alissa Velia Royhatul Jannah]]>;
<![CDATA[Nanik Suciati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5863
<![CDATA[Vegetation growing beyond safe limits has the potential to pose a threat to safety and the reliability of overhead powerlines, as well as cause financial losses for infrastructure providers. Identifying potential obstructions to overhead powerlines is crucial for addressing these issues. This study proposes the EFF-UNET semantic segmentation technique on the VEPL dataset to identify areas of overlap between vegetation and overhead powerlines by overlaying the two models. Visually, overhead powerlines have a thin pixel structure and are difficult to distinguish from the background or vegetation, whereas the feature extraction process in the U-Net encoder can degrade small objects due to progressive resolution loss. Modifications to the encoder in the baseline U-Net architecture utilize the EfficientNet family by comparing variants B0 through B7 to produce the best model. EfficientNet specifically employs compound scaling to optimize the network’s resolution, depth, and width during feature extraction, thereby preserving information integrity during downsampling. Experimental results demonstrate the superiority of EfficientNetB7 through a measured trade-off compared to other models, where for vegetation segmentation, this model achieves an IoU of 0.9824, Accuracy of 0.9905, Dice of 0.9911, and Loss of 0.0089. Meanwhile, for powerline segmentation, the results show an IoU of 0.9153, Accuracy of 0.9978, Dice of 0.9558, and Loss of 0.0442. Based on these findings, EFF-UNET model successfully addresses the shortcomings of conventional models in preserving feature representation. This model is capable of improving the performance of vegetation and overhead powerlines segmentation to produce precise encroachment areas, thereby enabling accurate on-site infrastructure inspections.]]>
<![CDATA[Bandwidth Prediction in Zoom Meetings: A Mathematical Model Based on Feature Configuration Analysis ]]>
<![CDATA[Andi Cahyono]]>;
<![CDATA[Muhammad Taufiq Nuruzzaman]]>;
<![CDATA[Bambang Sugiantoro]]>;
<![CDATA[Sumarsono Sumarsono]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.4138
<![CDATA[Video conferencing applications such as Zoom Meeting require sufficient and stable bandwidth to maintain communication quality. However, bandwidth needs often vary depending on user configurations, including video resolution, audio bitrate, and content-sharing activity. This study aims to develop a mathematical formula capable of accurately estimating bandwidth requirements for Zoom Meeting sessions. The methodology combines quantitative experiments and numerical simulations by collecting throughput data using Wireshark, analysing feature-based parameter variations, and validating the proposed formula through MATLAB simulation. Data were obtained from multiple Zoom sessions executed under controlled conditions with different feature combinations and replicated twenty times to ensure accuracy. The validation results show that the formula consistently provides realistic and stable estimations when compared with actual throughput measurements and simulation outcomes. The proposed model offers a simple yet effective tool for predicting bandwidth requirements, supporting efficient network capacity planning, and enhancing the overall performance of video conferencing environments.]]>
<![CDATA[Peningkatkan Keamanan ElGamal Menggunakan CNN dan Rolling Hash untuk Generasi Kunci dalam Enkripsi Gambar ]]>
<![CDATA[Achmad Fauzi]]>;
<![CDATA[Teuku Yuliar Arif]]>;
<![CDATA[Yuwaldi Away]]>;
<![CDATA[Roslidar Roslidar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.4484
<![CDATA[The large scale exchange of digital images requires security mechanisms that are robust not only at the cryptographic algorithm level but also in the key generation process, which is often the weakest component of the system. In conventional ElGamal schemes, security may degrade due to static entropy sources and predictable key patterns. This study proposes an ElGamal key generation model based on a pipeline of Convolutional Neural Networks (CNNs) and a rolling hash function, utilizing visual image content as an adaptive entropy source. The CNN extracts latent features through a fully connected layer, while the rolling hash enhances diffusion and key sensitivity to minor image variations. The model was evaluated using the CIFAR-10 dataset in PNG, WEBP, and JPG formats. Experimental results show stable key generation times ranging from 0.426 to 0.444 ms, with high entropy values between 7.98 and 7.99 bits, indicating strong randomness and resistance to prediction. Strong diffusion characteristics were also observed (PSNR 5.94 dB, SSIM −0.24, MAE 0.43). During encryption, WEBP achieved the fastest processing time (0.48 ms), followed by PNG (1.01 ms) and JPG (15.39 ms), while PNG demonstrated the highest size efficiency with a reduction of up to 70.6%. Decryption remained highly reliable, with success rates exceeding 97% across all formats. Overall, the results confirm that integrating CNNs and rolling hash significantly enhances ElGamal key generation security without compromising decryption reliability or image quality.]]>
<![CDATA[Classification of Banana Leaf and Ornamental Plant Diseases Using Gray Level Co-occurrence Matrix (GLCM) and Hybrid Random Forest–Support Vector Machine (SVM) ]]>
<![CDATA[Novia Urfiyati]]>;
<![CDATA[Nova Rijati]]>;
<![CDATA[Pujiono Pujiono]]>;
<![CDATA[Arief Soeleman]]>;
<![CDATA[Iqbal Firdaus]]>;
<![CDATA[Yeni Agus Nurhuda]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.4966
<![CDATA[Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.]]>
<![CDATA[Classification of Eyewitness Social Media Messages for Natural Disaster Monitoring using BERT Variants ]]>
<![CDATA[Muhammad Bashir Hanafi]]>;
<![CDATA[Mohammad Reza Faisal]]>;
<![CDATA[Friska Abadi]]>;
<![CDATA[Irwan Budiman]]>;
<![CDATA[Setyo Wahyu Saputro]]>;
<![CDATA[Njideka Nkemdilim Mbeledogu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5317
<![CDATA[The rapid growth of disaster-related social media data demands effective monitoring. However, its real-time source presents challenges due to large volumes of unstructured and noisy data. This study aims to improve effective monitoring with BERT variants to classify eyewitness reports on Twitter/X. Earlier studies have applied machine-learning and deep-learning models to automate the monitoring of eyewitness messages on social media, but these models still have shortcomings. Traditional machine-learning models rely on handcrafted and frequency-based features, limiting their ability to capture contextual semantics. Deep-learning models offer improved performance but still face challenges in modeling long-range dependencies and handling high-volume social media streams. This issue is pronounced in social media streams. This study employs transformer-based models using several BERT variants (BERT, RoBERTa, DistilBERT, ELECTRA, and ALBERT). Each model is pre-trained with the Masked Language Modeling (MLM) objective, and batch-size optimization is applied to boost performance. Experimental results indicate that a batch size of 16 consistently yields the best performance, with the standard BERT model achieving the highest macro-F1 score of 0.762. By disaster type, macro-F1 scores reach 0.744 for hurricane, 0.793 for flood, 0.756 for earthquake, and 0.750 for wildfire. BERT (16) outperforms the other BERT variants and twelve baseline models from prior research. Unlike previous approaches, this study leverages pre-trained Masked Language Models to optimize classification on disaster-related datasets. The findings contribute to the development of transformer-based architectures for text classification in real-time disaster informatics, leading to more accurate situational awareness and reduced delays in emergency decision-making.]]>
<![CDATA[Hybrid Cryptography-Steganography Scheme Based on Camellia-256 and LSB for Enhanced Security and Imperceptibility of Secret Messages ]]>
<![CDATA[Imam Prayogo Pujiono]]>;
<![CDATA[Eko Hari Rachmawanto]]>;
<![CDATA[Christy Atika Sari]]>;
<![CDATA[Said Fachri Ariza]]>;
<![CDATA[Isnaeni Kholifatun]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5323
<![CDATA[The development of digital communications has increased the risk of message interception and manipulation, necessitating robust and multi-layered security solutions. This research designs, implements, and evaluates a multi-layered security scheme that integrates cryptography and steganography. The proposed method first encrypts the secret message using the Camellia-256 algorithm in Electronic Codebook (ECB) mode with PKCS#7 padding. The resulting ciphertext is then embedded into the cover image using the Least Significant Bit (LSB) steganography technique. From a practical standpoint, this design provides defense-in-depth for covert communication: encryption preserves confidentiality even if the hidden payload is detected, while steganography reduces the likelihood that the encrypted content is flagged during transmission. This combination mitigates LSB’s weakness against statistical steganalysis by encrypting the payload into ciphertext, thereby reducing structured bit patterns that may otherwise facilitate statistical detection. System performance is quantitatively evaluated using two primary metrics: the Avalanche Effect to measure cryptographic strength and the Peak Signal-to-Noise Ratio (PSNR) to measure the visual imperceptibility of the stego-image. The experimental results demonstrate excellent cryptographic strength, evidenced by an average Avalanche Rate of 54.37%, indicating that minimal changes to the input result in significant changes to the output. Furthermore, the scheme exhibits excellent visual imperceptibility with an average PSNR of 75 dB, making the stego-image visually indistinguishable from the original cover image. It is concluded that the proposed hybrid scheme offers a robust and validated solution for secure message communication, combining content confidentiality through cryptography and message obfuscation through steganography, thus providing dual protection against cybersecurity threats.]]>
<![CDATA[Implementation of IndoBERT for Sustainability Impact Assessment in University Collaboration Information Systems ]]>
<![CDATA[Ryan Hamonangan]]>;
<![CDATA[Raditya Danar Dana]]>;
<![CDATA[Yudhistira Arie Wijaya]]>;
<![CDATA[Odi Nurdiawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5330
<![CDATA[University collaboration plays a critical role in enhancing institutional quality and supporting global sustainability agendas. However, many higher education institutions face challenges in managing Memorandum of Understanding (MoU), Memorandum of Agreement (MoA), and Implementation Agreement (IA) documents, particularly in monitoring implementation and assessing their alignment with sustainability goals. This study introduces a University Collaboration Information System enhanced with IndoBERT-based Natural Language Processing (NLP) to automate sustainability impact assessment. A synthetic corpus of 30 annotated collaboration documents was developed, covering multi-label Sustainable Development Goals (SDG) classification and span-level Named Entity Recognition (NER). Two approaches were evaluated: (1) baseline TF-IDF + Support Vector Machine (SVM) for SDG classification and rule-based NER, and (2) fine-tuned IndoBERT for both tasks. Experimental results show that IndoBERT significantly outperforms the baselines, achieving an average F1-score of 0.93 for SDG classification (+16.3%) and 0.96 for NER (+18.5%). The system integrates these models to generate automated entity extraction, sustainability dashboards, and document monitoring features. This work contributes to the advancement of informatics by demonstrating the effectiveness of Transformer-based NLP in processing institutional documents and by providing an integrated information-system framework that strengthens the role of NLP within the field of computer science.]]>
<![CDATA[Detection of Coffee Leaf Diseases Using Deep Learning to Support Digitalization and Smart Agriculture ]]>
<![CDATA[Siti Mutmainah]]>;
<![CDATA[Zumhur Alamin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5338
<![CDATA[Coffee is one of Indonesia's main commodities and an important agricultural sector for the economy. However, one of the challenges in coffee cultivation is disease. Improper and delayed treatment can cause leaf damage and death of coffee plants. This study aims to detect disease types on coffee leaves using a deep learning CNN approach and lightweight CNN architectures such as MobileNet and EfficientNet variants. This study also applies traditional image augmentation and OpenCV. The results of EfficientNetV2S and EfficientNetV2L achieve 95–98% accuracy with stable precision and recall in almost all classes, although minority classes remain challenging. The MobileNetV3 architecture showed optimal results with 99% accuracy in all variants (Small, Large, and Small with OpenCV augmentation). The research model was verified using local coffee leaf images Bumi Pajo. These findings confirm that MobileNetV3 not only excels in terms of accuracy but also has the potential to be applied to mobile device-based or Internet of Things (IoT) coffee leaf disease monitoring systems. With high accuracy and low computational requirements, this model can support real-time disease detection in the field, helping farmers and agricultural practitioners make quick and accurate decisions in disease control.]]>
<![CDATA[Improving Sentiment Classification of Kredit Pintar Reviews Using IndoBERT, SMOTE, and Stacking Ensemble ]]>
<![CDATA[Ayu Safitri]]>;
<![CDATA[Muhammad Risaldi]]>;
<![CDATA[Muh Naufal Ramadhani Alwi]]>;
<![CDATA[Dewi Fatmarani Surianto]]>;
<![CDATA[Nur Fadilah]]>;
<![CDATA[Jumadi M Parenreng]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5342
<![CDATA[Kredit Pintar is one of the most widely used fintech applications in Indonesia, generating millions of user reviews on the Google Play Store that reflect diverse user experiences. These reviews provide valuable insights into application performance; however, extracting sentiment from such unstructured and imbalanced textual data remains a challenging task. This study aims to improve sentiment classification of Kredit Pintar user reviews by proposing a hybrid approach that integrates IndoBERT, SMOTE (Synthetic Minority Over-Sampling Technique), and a stacking ensemble model. From 2020 to 2024, 2,278 user reviews were classified into positive, neutral, and negative categories based on star ratings. SMOTE was employed to rectify class imbalance, whereas IndoBERT gathered contextual representations of the Indonesian language. Furthermore, a stacking ensemble combining IndoBERT, Random Forest, and SVM (Support Vector Machine) was implemented to enhance classification performance. Experimental results show that IndoBERT without data balancing achieved an accuracy of 84%, whereas the proposed combination of IndoBERT, SMOTE, and stacking ensemble consistently produced superior performance, achieving 92% accuracy, precision, recall, and F1-score. The findings demonstrate that integrating language-specific transformer models with data balancing and ensemble techniques effectively improves sentiment classification. This study contributes to the advancement of Indonesian-language natural language processing in the fintech domain and provides practical insights for fintech developers in understanding user perceptions and improving digital financial services.]]>
<![CDATA[Image Cryptography Process Using Arnold’s Cat Map And Henon Map Algorithms ]]>
<![CDATA[Moch Azhar Al Ghifari]]>;
<![CDATA[Bayu Surarso]]>;
<![CDATA[Aris Sugiharto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5354
<![CDATA[The security of digital image data is a crucial aspect in various fields, such as communications, medicine, and the military. The inherent characteristics of digital images—namely high pixel correlation and large data size—render conventional encryption methods less optimal. This study aims to evaluate the encryption quality of images using the Arnold’s Cat Map (ACM) and Henon Map algorithms, both individually and in combination (ACM-Henon and Henon-ACM). ACM is utilized to rearrange pixel positions to create a confusion effect, while the Henon Map is employed to randomly alter pixel values (diffusion). The implementation is carried out using the Python programming language within the Visual Studio Code development environment. Encryption quality is assessed using parameters such as Avalanche Effect (AE), Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and correlation coefficient. Experimental results show that the combined chaos-based methods significantly enhance security compared to the individual algorithms, particularly by analyzing the impact of algorithm order on encryption quality. The best performance was achieved by the Henon→ACM combination, producing NPCR ≈ 99.44%, UACI ≈ 19.93%, entropy ≈ 7.9874, and AE ≈ 50.12%, indicating strong randomness and resistance to differential attacks. This research demonstrates that combining confusion and diffusion mechanisms yields more secure cipher images than using either method alone. The main contribution of this study lies in providing a systematic comparative evaluation of single and combined chaos-based encryption schemes, including order-sensitive analysis across different image characteristics, rather than proposing a new encryption algorithm. However, the encryption performance is influenced by image size, parameter selection, and iteration count, which may limit consistency across different image characteristics. Future work may explore adaptive parameter optimization and improved diffusion mechanisms for higher UACI values.]]>
