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[Optimization of Machine Learning Model using Grid and Random Search Algorithms for Predicting Student Dropout ]]>
<![CDATA[M. Faris Al Hakim]]>;
<![CDATA[Siti Wahyuni]]>;
<![CDATA[Kholiq Budiman]]>;
<![CDATA[Aditya Marianti]]>;
<![CDATA[Bambang Eko Susilo]]>;
<![CDATA[Nuni Widiarti]]>;
<![CDATA[Sri Sukaesih]]>;
<![CDATA[Rifaatunnisa Rifaatunnisa]]>
<![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.2026.7.3.5627
<![CDATA[Student dropout is a serious problem that can affect the quality of education and operational efficiency of higher education institutions. Early prediction of potential students who will dropout is essential to develop appropriate intervention strategies, so as to increase graduation rates and reduce the negative impact on academic continuity. A better model for student dropout prediction becomes an objective of this research. The method used in this research is to improve the performance of machine learning models through the selection of optimal hyperparameters. The research methodology consists of several stages, including data preprocessing, handling imbalanced data, model training, and performance evaluation. There are three machine learning models used in this research, namely XGBoost, AdaBoost, and Random Forest. The selection of optimal hyperparameter values is carried out using the Random Search and Grid Search methods. Model evaluation is conducted using k-fold cross-validation and multiple evaluation metrics, including accuracy, precision, recall, and F1-score. As part of the important results, the combination of XGBoost and Random Search produced the best performance with 91.18% accuracy, indicating that hyperparameter optimization significantly improves predictive performance. The findings of this research explicitly contribute to the field of informatics, particularly educational data mining, and provide insights for educational institutions to identify high-risk dropout students more accurately.]]>
<![CDATA[Banana Leaf Disease Classification Using CNN Feature Extraction and Naive Bayes Algorithm ]]>
<![CDATA[Moh. Badri Tamam]]>;
<![CDATA[Januario Freitas Araujo]]>;
<![CDATA[Anwari Anwari]]>
<![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.2026.7.3.5645
<![CDATA[Banana leaf diseases such as Black Sigatoka, Cordana, and Pestalotiopsis significantly reduce productivity and require early, accurate detection to prevent severe yield losses. While Convolutional Neural Networks (CNN) have demonstrated high performance in plant disease classification, most existing approaches rely on computationally intensive end-to-end deep learning models, limiting their deployment on resource-constrained devices. This study proposes a lightweight hybrid classification framework that integrates MobileNetV2-based CNN feature extraction with a Gaussian Naive Bayes classifier. The novelty of this research lies in the systematic transformation of deep 1,280-dimensional feature representations into a probabilistic classification space, enabling competitive accuracy with substantially lower computational complexity. A balanced dataset consisting of 3,200 training images and 1,311 testing images collected from Pamekasan Regency was preprocessed through resizing, normalization, and augmentation. Experimental results show that the end-to-end CNN achieved 98.70% accuracy, while the proposed hybrid CNN–Naive Bayes model attained 95.73% accuracy with F1-scores above 0.90 across all classes. Despite not relying on backpropagation during classification, the hybrid approach maintains strong predictive performance while reducing training time and memory requirements. These findings demonstrate that integrating deep feature extraction with probabilistic learning provides an efficient and deployable solution for edge-based precision agriculture systems.]]>
<![CDATA[Optimization of ShuffleNetV2 Using Self-Knowledge Distillation for Cocoa Fruit Disease Classification ]]>
<![CDATA[H.R Merdu Wira Jasa]]>;
<![CDATA[Anjar Wanto]]>;
<![CDATA[Rizky Khairunnisa Sormin]]>
<![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.2026.7.3.5649
<![CDATA[Timely cocoa fruit disease diagnosis is critical for field management, yet manual inspection is subjective and inconsistent, while many accurate deep learning models remain too computationally demanding for practical on-device use. This study aims to optimize cocoa fruit disease classification by applying self-knowledge distillation (Self-KD) to a lightweight ShuffleNetV2 architecture without increasing inference complexity. Using a three-class dataset (healthy, pod borer, and black pod rot) with preprocessing and class balancing, ShuffleNetV2 was selected as the baseline and trained with Self-KD, improving accuracy from 96.84% to 98.34% along with consistent gains in precision, recall, and F1-score. These results indicate that Self-KD provides a learning-level optimization that enhances robustness and prediction stability in lightweight CNNs, which is especially relevant for edge AI deployment in agricultural environments. Therefore, the proposed approach supports efficient, scalable, and sustainability-oriented AI (Green/Sustainable AI) for smart farming, with potential transferability to other crops that exhibit similar visual symptom patterns.]]>
<![CDATA[Sentiment Analysis Of E-Commerce Reviews Using Fine-Tuned Indobert With Class Weights Strategy ]]>
<![CDATA[Abdan Syakura]]>;
<![CDATA[Dewi Soyusiawaty]]>
<![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.2026.7.3.5654
<![CDATA[MSMEs in the e-commerce sector face difficulties in converting large volumes of unstructured customer review data into actionable business insights. This challenge is exacerbated by the ambiguity of star ratings, which often do not align with the content of the reviews, making automated sentiment analysis of the text essential. This study implements a systematic sentiment analysis workflow on a case study of 15,278 customer reviews of Toko Pasar Stan Jogja. The method used is fine-tuning a pre-trained Transformer model, namely IndoBERT, which is optimized with class weighting techniques to handle unbalanced datasets. The model's performance was comprehensively evaluated using Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and word cloud visualization metrics. The test results showed that the developed model had very high performance, achieving an overall accuracy of 96.99% and an average F1-Score of 0.97 on the test data. Qualitative analysis also successfully identified that product quality (“fresh”) and logistics efficiency (‘fast’) were the main drivers of satisfaction, while the main complaints centered on the condition of the product upon arrival (“damaged,” “rotten”). This research proves that the optimized Transformer model is not only effective for sentiment classification, but also serves as a strategic tool for extracting concrete business insights.]]>
<![CDATA[Impact of Contrast Limited Adaptive Histogram Equalization and Image Upscaling on Cataract Classification Using Deep Learning Models: Inception-ResNetV2, EfficientNetB0, and ResNet-50 ]]>
<![CDATA[Ismi Dwi Junianti]]>;
<![CDATA[Ulva Nuha Muvidah]]>;
<![CDATA[Christian Sri Kusuma Aditya]]>
<![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.2026.7.3.5658
<![CDATA[Cataract is one of the leading causes of visual impairment worldwide, and its detection using retinal images remains a critical challenge in medical image analysis due to variations in image quality and subjectivity in clinical assessment. This study aims to evaluate the impact of image preprocessing techniques, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) and image upscaling, on the performance and interpretability of deep learning–based cataract classification models. Three convolutional neural network architectures—Inception-ResNetV2, EfficientNetB0, and ResNet-50—were assessed using a balanced dataset of 2,000 retinal images under two experimental settings: raw images and enhanced images. The models were evaluated using accuracy, precision, recall, and F1-score, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to analyze model interpretability. Experimental results show that EfficientNetB0 achieved the highest accuracy on raw images (96%), followed by ResNet-50 (94%) and Inception-ResNetV2 (92%). After applying CLAHE and upscaling, ResNet-50 exhibited improved performance, reaching 95% accuracy, whereas EfficientNetB0 and InceptionResNetV2 experienced a decrease in accuracy to 83%. Grad-CAM visualizations indicate that all models consistently focused on clinically relevant regions associated with cataract characteristics. These findings demonstrate that image enhancement techniques do not universally improve classification performance and that their effectiveness is highly dependent on the underlying CNN architecture. The study provides practical insights for selecting appropriate preprocessing–model combinations to develop accurate, interpretable, and robust deep learning–based cataract classification systems for medical decision-support applications.]]>
<![CDATA[Development of a Hybrid Machine Learning-Based E-Commerce Chatbot Using Jaccard Similarity and K-Nearest Neighbor for Accurate Intent Classification ]]>
<![CDATA[Andrian Sah]]>;
<![CDATA[Andi Ilham]]>;
<![CDATA[Rasna Rasna]]>;
<![CDATA[Siti Nurhayati]]>
<![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.2026.7.3.5659
<![CDATA[The advancement of technology in the e-commerce industry requires fast and accurate information services, particularly through the use of Natural Language Processing (NLP)-based chatbots. However, many existing chatbots rely on a single method, which often limits their ability to understand user question contexts effectively. This study proposes a hybrid approach integrating Jaccard Similarity and K-Nearest Neighbor (K-NN) to improve answer retrieval accuracy and intent classification in e-commerce chatbot systems. Jaccard Similarity is employed to measure the similarity between user queries and Frequently Asked Questions (FAQ) data, while K-NN is used to determine intent based on the nearest neighbor with the highest similarity values. The dataset, consisting of FAQ questions and answers, is preprocessed through case folding, tokenization, stopword removal, and stemming. System performance is evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that Jaccard Similarity effectively selects relevant answer candidates, achieving similarity values of up to 66%, while K-NN produces stable intent classification results. The proposed hybrid model achieved an accuracy of 87%, precision of 86%, recall of 85%, and an F1-score of 85%, outperforming single-method implementations. Furthermore, confidence score analysis indicates that most chatbot responses fall into the high confidence category (>0.70). Rule-based NLP evaluation also provides insights into unclassified inputs, which can be used as a basis for future dataset development. The implementation results demonstrate that the chatbot system can be operated effectively on both customer and admin sides and monitored through analytical features. Overall, the proposed hybrid approach enhances the reliability, relevance, and stability of chatbot responses, making it a practical and effective solution for real-time intent classification and FAQ retrieval in e-commerce customer service environments.]]>
<![CDATA[Artificial Intelligence-Based Aircraft Detection for Enhanced Aviation Safety and Air Traffic Management ]]>
<![CDATA[Astika Ayuningtyas]]>;
<![CDATA[Saomi Novelia Gunawan]]>;
<![CDATA[Puspa Ira Candra Dewi Wulan]]>;
<![CDATA[Rully Medianto]]>;
<![CDATA[Sri Winiarti]]>;
<![CDATA[Aris Rakhmadi]]>
<![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.2026.7.3.5661
<![CDATA[The rapid growth of international air traffic has made maintaining aviation safety and managing air traffic efficiently increasingly complex, particularly in identifying aircraft in constantly changing airspace. Traditional monitoring systems such as radar and Automatic Dependent Surveillance-Broadcast (ADS-B) have limitations in operating at low altitudes, in adverse weather, and in overcrowded environments, which can reduce the ability to understand surrounding conditions. This research proposes an artificial intelligence-based visual detection system aimed at enhancing real-time aircraft identification and improving air traffic monitoring. The system uses a YOLO-based deep learning model enhanced with a special attention mechanism and data augmentation to increase accuracy, flexibility, and operational resilience. The dataset used covers various flight situations, such as variations in light, viewing angles, and background complexity, to train the model. The model's test results show that it can correctly identify 95.24% of passenger planes, 92.4% of blimps, and 90% of fighter planes. The average overall precision (mAP) is over 90%. This system is also capable of real-time inference with precision and recall consistently above 85% under various conditions. Compared with conventional vision-based detection methods, this system demonstrates superior localization capabilities and robustness, making it suitable for use in real-world flight surveillance and air traffic management. In conclusion, this AI-based framework provides a practical and scalable solution that can improve flight safety and promote smarter air traffic management.]]>
<![CDATA[Improving RoBERTa Performance through Hyperparameter Optimization for Sentiment Analysis of Indonesian Tourism Reviews ]]>
<![CDATA[Imamah Imamah]]>;
<![CDATA[Myo Thida]]>;
<![CDATA[Fika Hastarita Rachman]]>;
<![CDATA[Budi Dwi Satoto]]>;
<![CDATA[Sri Herawati]]>;
<![CDATA[Yeni Kustiyahningsih]]>;
<![CDATA[Eka Mala Sari Rochman]]>;
<![CDATA[Meita Lailatuz Zakiyah]]>
<![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.2026.7.3.5672
<![CDATA[The performance of transformer models such as RoBERTa in sentiment classification is influenced by hyperparameter settings, especially the epoch and batch sizes. However, no previous study has examined the impact of changes in the number of epochs and batch sizes on the performance of each class in classification tasks, especially in Indonesian-language sentiment analysis of tourism reviews. Therefore, this study aims to fill this gap by analyzing the performance of RoBERTa and the impact of various hyperparameter settings on sentiment for each class. The dataset consists of 3,875 reviews from visitors to Lake Sarangan on Google Maps. The batch sizes used in this study are 8 and 16, and the epoch range is 2 to 4. There are three classes of sentiment: negative, neutral, and positive. The results demonstrate that increasing the batch size from 8 to 16 does not linearly improve model performance. The optimal combination of epoch=4 and batch size=8 achieved 91% accuracy, with significant improvements in recall and F1-score across all classes, especially in positive sentiment classification. This research offers valuable insights into fine-tuning RoBERTa for sentiment analysis in Indonesian contexts, providing recommendations for future sentiment analysis tasks in natural language processing.]]>
<![CDATA[Face Gender Classification for Public Facility Access Control using EfficientNet with Penalized-Entropy Loss ]]>
<![CDATA[Sabrina Adinda Sari]]>;
<![CDATA[Faidhil Nugrah Ramadhan Ahmad]]>;
<![CDATA[Miftahul Adnan Rasyid]]>;
<![CDATA[I Gede Manggala Putra]]>;
<![CDATA[Fauzan Ramadhan]]>
<![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.2026.7.3.5675
<![CDATA[Access to public facilities that are restricted based on gender, such as toilets and changing rooms, requires a strict security system because there are still many cases of abuse by irresponsible parties if only gender signs are relied upon. CCTV integrated with facial recognition is becoming more sophisticated every day, but it is limited if the face is covered by attributes such as masks. This is because the less visible the area is, the more difficult it is for the model to determine the label. To overcome this, this study proposes a gender classification approach for faces that may be covered by accessories such as masks, by adding Penalized Entropy loss as a loss function to the EfficientNet-B0 model. This loss function adds a penalty for incorrect predictions even if they are fairly accurate. The evaluation results show that the proposed model, with a penalty weight of 0.5, improved the accuracy by 3% from 90% to 93%. The experimental results show that the determination of the penalty weight has a significant impact on model performance, where a weight of 0.5 produces optimal performance because it provides a balance between penalizing overconfident predictions and the model's ability to maintain relevant feature discrimination; too small a weight does not sufficiently suppress overconfidence, while too large a weight actually reduces classification ability. The proposed method has demonstrated improvements in generalization and reduced overconfidence in gender classification systems. This method contributes to the development of reliable biometric systems suitable for uncontrolled real-world environments.]]>
<![CDATA[Comparative Analysis of Baseline IndoBERT, Class-Weighted IndoBERT, and SMOTE with Support Vector Machine for Handling Imbalanced Sentiment Classification in Indonesian ]]>
<![CDATA[Riya Widayanti]]>;
<![CDATA[Fitriana Cendra Kasih]]>
<![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.2026.7.3.5692
<![CDATA[Imbalanced data distribution is a common issue in Indonesian sentiment classification and significantly affects the performance of classification models. This study investigates three approaches, namely SMOTE combined with Support Vector Machine (SMOTE + SVM), Baseline IndoBERT, and Class-Weighted IndoBERT. The dataset consists of Google Maps reviews, which are categorized into positive, neutral, and negative sentiments. Prior to model training, the data undergo preprocessing steps including cleaning, normalization, and tokenization. Model performance is evaluated using confusion matrix analysis and macro-averaged F1-score. The results show that Baseline IndoBERT achieves a macro F1-score of 0.598, followed by Class-Weighted IndoBERT with 0.582, while SMOTE + SVM obtains the lowest performance at 0.545. Despite having slightly lower overall performance, Class-Weighted IndoBERT demonstrates a more balanced capability in recognizing minority classes. These findings indicate that incorporating class-weighting mechanisms into transformer-based models can help mitigate bias toward majority classes and improve minority class recognition. From a scientific perspective, this study provides empirical evidence on how imbalance-aware learning strategies influence the behavior of transformer-based models in imbalanced text classification tasks. Furthermore, this study highlights the importance of using macro-averaged evaluation metrics to ensure a more comprehensive and fair assessment of model performance, particularly in low-resource and imbalanced language settings.]]>
