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
962 Documents
<![CDATA[Improving Semantic Segmentation of Flood Areas Using Rotation and Flipping-Based Feature Augmentation ]]>
<![CDATA[Intizhami, Naili Suri]]>;
<![CDATA[Nuranti, Eka Qadri]]>;
<![CDATA[Bahar, Nur Inaya]]>
<![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.2025.6.3.4564
<![CDATA[Semantic segmentation is one of the powerful methods for analyzing flood video or picture data captured by smartphones. However, achieving accurate semantic segmentation requires the application of several methods. In this work, we address the task of feature augmentation approach using rotation (90°, 180°, 270°) and flipping (horizontal, vertical) to improve semantic segmentation of flood areas in Parepare city using a Fully Convolutional Network (FCN). The experimental results demonstrate that the best augmentation scenario 270° rotation achieved an accuracy of 88% and 90° rotation achieved an mean Intersection over Union (mIoU) of 43%, significantly outperforming the baseline FCN model without augmentation, which achieved 86% accuracy and 35% mIoU. ]]>
<![CDATA[A Hybrid Recommendation System for Pregnancy-Safe Skincare: Integrating Keyword-Based and Rule-Based Classification with Content-Based Filtering ]]>
<![CDATA[Putri, Jihan Syahira Adnanda]]>;
<![CDATA[Haryono, Kholid]]>
<![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.2025.6.3.4646
<![CDATA[Pregnant women struggle to select safe skincare products, often relying on social media and blog searches, and manual ingredient checking. Choosing safe ingredients is essential, as exposure to unsafe substances may lead to teratogenic effects and endocrine disruption, which can result in fetal abnormalities such as retinoic acid embryopathy and neurodevelopmental disorders. Exposure to retinoids, for instance, has been associated with a 20–30% incidence of fetal retinoid syndrome in affected pregnancies. This study develops an integrated recommendation system using three techniques: (1) keyword-based classification with regular expressions to detect 50 unsafe ingredients across 8 categories; (2) rule-based classification using IF-THEN statements matching products with 5 pregnancy-related skin conditions; and (3) content-based filtering utilizing TF-IDF vectorization and cosine similarity for safer alternatives. The system achieved 86.25% accuracy in safety classification, with high recall (97.50%) indicating strong ability to identify safe products. However, moderate precision (79.59%) suggests some unsafe products were misclassified as safe, highlighting need for improvement in safety-critical contexts. Pilot user evaluation using ResQue framework with 10 participants yielded scores of 4.50–4.85 across 8 dimensions, achieving 4.65 overall average. This research demonstrates effective integration of multiple recommendation methods in context-sensitive applications, enabling safer product selection during pregnancy. By providing accessible, personalized, and evidence-based information, the system enables pregnant women to make informed skincare decisions and continue their routines despite limited access to healthcare services.]]>
<![CDATA[Prediction Of Clay Mining Production Value Using Linear Regression Model With Multi-Swarm Particle Swarm Optimization ]]>
<![CDATA[Yuliastuti, Gusti Eka]]>;
<![CDATA[Kurniawan, Muchamad]]>;
<![CDATA[Pratikto, Dimas]]>;
<![CDATA[Moneter, Mochamad Rizky]]>
<![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.2025.6.3.3443
<![CDATA[The progress of a nation or a country can be recognized from its income through various industries inside. Mining refers to one of the most advanced industries in Indonesia. The majority of mining in Indonesia is open-pit mining which is exposed directly to the sky. This study focuses on modeling data from rainfall, working hours, and production yields. It employed the Multi-Swarm Particle Swarm Optimization (MSPSO) algorithm to find multiple linear regression modeling by minimizing the Mean Squared Error (MSE) value. The value for the production results was then predicted using the existing multiple linear regression model. In terms of testing, the best model having an MSE of 288.0656 occurred at the parameters of Npop 180, acceleration coefficient 1 by 0.7, acceleration coefficient 2 by 0.7, acceleration coefficient 3 by 0.7, wmin 4, wmax 9 within 100 iterations.]]>
<![CDATA[Comparative Analysis Retrofit and Ktor Client Performance in Various Internet Speeds Internet on MSMEs Cashier Application ]]>
<![CDATA[Kholik, Muhamad Akbar Abdul]]>;
<![CDATA[Pratomo, Dinar Nugroho]]>
<![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.2025.6.3.3925
<![CDATA[MSMEs (Micro, Small, and Medium Enterprises) in Indonesia face uneven network infrastructure, with more than 20% of smartphone users having download speeds below 10 Mbps. This condition hampers the efficiency of data processing between client and server, while MSMEs need innovations such as digitization of bookkeeping to increase competitiveness. The selection of HTTP networking libraries such as Retrofit and Ktor Client is very important, because both play a role in the process of sending and receiving data from the server. This research aims to analyze the performance of both libraries in the Lulu POS application to determine the most optimal library in supporting MSME operations in various network conditions. The test is conducted in two scenarios: the first scenario uses text data and the second scenario uses text and image data. Each scenario has several test cases that will be tested at six different internet speeds. The results show that Retrofit excels in response time for text data with a performance improvement of 18.85% and network usage of 21.33%. Ktor Client is superior in scenarios involving text and image data, with a response time advantage of 7.20% and network usage of 0.08%. On the other hand, Retrofit is more efficient in memory usage in both scenarios, with an advantage of 16.49% in text data and 4.70% in text and image data. In conclusion, Retrofit is more stable for applications focusing on text data such as Lulu POS, while Ktor Client is more suitable for applications that manage images. These results make MSMEs get cashier applications with optimal libraries for various network conditions, so that operations are smoother and data management efficiency increases.]]>
<![CDATA[Enhancing The Precision Detection and Grading of Diabetic Retinopathy through Digital Retinal Imaging Using 3D Convolutional Neural Networks ]]>
<![CDATA[Allwine, Autho]]>;
<![CDATA[Simanjuntak, Mutiara S]]>;
<![CDATA[Pulungan, Wahyu Aji]]>
<![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.2025.6.3.4387
<![CDATA[Diabetic retinopathy (DR) is a pressing global health issue that affects the retina and is closely linked to diabetes, leading to vision impairment and blindness, particularly in adults. With the rising incidence of diabetes, the need for efficient and accurate DR screening is critical for early intervention and improved patient outcomes. Automated screening solutions can streamline this process, allowing healthcare professionals to focus more on patient care.In this study, we harnessed advanced deep learning techniques, specifically 3D convolutional neural networks (3D-CNNs), to classify DR into binary categories (presence or absence) and five multiclass categories: mild, moderate, no DR, proliferative DR, and severe DR. Our goal was to enhance diagnostic Precision in ophthalmology. To optimize our models, We embraced two methods transformative data augmentation: random shifting and random weak Gaussian blurring, empowering our model to reach new heights,as well as their combination. Our results showed that, for binary classification, the combined augmentation achieved significant success, The multiclass model was trained without any data augmentation excelled in Precision. These findings highlight the importance of large, high-quality research datas in deep learning algorithms. By leveraging advanced methodologies and robust data, we can transform diabetic retinopathy screening, promoting earlier detection and better treatment outcomes for those affected.]]>
<![CDATA[Comparative Analysis of Augmentation and Filtering Methods in VGG19 and DenseNet121 for Breast Cancer Classification ]]>
<![CDATA[Seneng, I Kadek]]>;
<![CDATA[Ayu, Putu Desiana Wulaning]]>;
<![CDATA[Huizen, Roy Rudolf]]>
<![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.2025.6.3.4397
<![CDATA[Breast cancer is one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Mammography plays a crucial role in early detection, yet challenges in manual interpretation have led to the adoption of Convolutional Neural Networks (CNNs) to improve classification accuracy. This study evaluates the performance of Visual Geometry Group (VGG19) and Densely Connected Convolutional Networks (DenseNet121) in mammogram classification. It examines the impact of data augmentation and image enhancement techniques, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), Median Filtering, and Discrete Wavelet Transform (DWT), as well as the influence of varying epochs and learning rates. A novel approach is introduced by assessing data augmentation effectiveness and exploring model adaptations, such as layer incorporation and freezing during training. Classification performance is enhanced through fine-tuning strategies combined with image enhancement techniques, reducing reliance on data augmentation. These findings contribute to medical imaging and computer science by demonstrating how CNN modifications and enhancement methods improve mammogram classification, providing insights for developing robust deep learning-based diagnostic models. The highest performance was achieved using VGG19 with DWT, a learning rate of 0.0001, and 20 epochs, yielding 98.04% accuracy, 98.11% precision, 98% recall, and a 97.99% F1-score. Data augmentation did not consistently enhance results, particularly in clean datasets. Increasing epochs from 10 to 20 improved accuracy, but performance declined at 30 epochs. The confusion matrix showed high accuracy for Benign (100%) and Cancer (99.5%), with more misclassifications in the Normal class (94.5%).]]>
<![CDATA[An Efficient Model for Waste Image Classification Using EfficientNet-B0 ]]>
<![CDATA[Kurniawan, Teofilus]]>;
<![CDATA[Khadijah, Khadijah]]>;
<![CDATA[Kusumaningrum, Retno]]>
<![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.2025.6.3.4417
<![CDATA[Waste management remains a significant challenge, particularly in developing countries. To address this issue, artificial intelligence can be leveraged to develop a waste image classifier that facilitates automatic waste sorting. Previous studies have explored the use of Convolutional Neural Networks (CNNs) for waste image classification. However, CNNs typically require a large number of parameters, leading to increased computational time. For practical applications, a waste image classifier must not only achieve high accuracy but also operate efficiently. Therefore, this study aims to develop an accurate and computationally efficient waste image classification model using EfficientNet-B0. EfficientNet-B0 is a CNN architecture designed to achieve high accuracy while maintaining an efficient number of parameters. This study utilized the publicly available TrashNet dataset and investigated the impact of image augmentation in addressing imbalance data issues. The highest performance was achieved by the model trained on the unbalanced dataset with the addition of a Dense(32) layer, a dropout rate of 0.3, and a learning rate of 1e-4. This configuration achieved an accuracy of 0.885 and an F1-score of 0.87. These results indicate that the inclusion of a Dense(32) layer prior to the output layer consistently improves model performance, whereas image augmentation does not yield a significant enhancement. Furthermore, our proposed model achieved the highest accuracy while maintaining a significantly lower number of parameters compared to other CNN architectures with comparable accuracy, such as ResNet-50 and Xception. The resulting waste classification model can then be further implemented to build an automatic waste sorter.]]>
<![CDATA[Machine Learning Models for Metabolic Syndrome Identification with Explainable AI ]]>
<![CDATA[Asoka, Egga]]>;
<![CDATA[Fathoni, Fathoni]]>;
<![CDATA[Primanita, Anggina]]>;
<![CDATA[Isa, Indra Griha Tofik]]>
<![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.2025.6.3.4430
<![CDATA[Metabolic syndrome (MetS) is a cluster of interrelated risk factors, including hypertension, dyslipidemia, central obesity, and insulin resistance, significantly increasing the likelihood of cardiovascular diseases and type 2 diabetes. Early identification of hypertension, a key component of MetS, is essential for timely intervention and effective disease management. This research aims to develop a hybrid machine learning model that integrates XGBoost classification with K-Means clustering to enhance or strengthening of hypertension prediction and identify distinct patient subgroups based on metabolic risk factors. The dataset consists of 1,878 patient records with metabolic parameters such as systolic and diastolic blood pressure, fasting glucose, cholesterol levels, and anthropometric measurements. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed XGBoost model achieved an outstanding classification performance with 98% accuracy, 98% precision, 98% recall, 98% F1-score, and an ROC-AUC of 1.00. K-Means clustering further identified five distinct patient subgroups with varying metabolic risk profiles. The findings underscore the potential of machine learning-driven decision support systems in improving hypertension diagnosis and MetS management.]]>
<![CDATA[Hybrid Neural Network-Based Road Damage Detection Using CNN-RNN and CNN-MLP Models ]]>
<![CDATA[Rahajoe, Ani Dijah]]>;
<![CDATA[Suriansyah, Muhammad]]>;
<![CDATA[Jr, Angelo A. Beltran]]>
<![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.2025.6.3.4435
<![CDATA[Currently, there are many applications of image processing in various fields. One of them is the recognition of paved road images. Detection through images helps in handling infrastructure development roads. With the advancement of technology, especially in the field of deep learning, the process of detecting road damage can be done automatically and more efficiently. The road damage detection system can be integrated into the smart city system to monitor infrastructure conditions in real time. This study will use a combined deep learning algorithm between Convolutional Neural Network- Recurrent Neural Network (CNN-RNN) and as a comparison using Convolutional Neural Network- MultiLayer Perceptrons (CNN-MLP). The study aims to analyze the accuracy of using the CNN-RNN and CNN-MLP algorithms for detecting paved roads that have categories of undamaged roads, damaged roads, and damaged roads with holes. The detection of paved roads has complex details so an algorithm that has good performance with high accuracy is needed. The results of the study showed that the CNN-RNN hybrid had a better accuracy of 96.59 percent than the CNN-MLP hybrid model of 95.9 percent. ]]>
<![CDATA[Optimizing Indonesian Banking Stock Predictions with DBSCAN and LSTM ]]>
<![CDATA[Purwandhani, Septiannisa Alya Shinta]]>;
<![CDATA[Sajiatmoko, Aletta Agigia Novta]]>;
<![CDATA[Aditya, Christian Sri Kusuma]]>
<![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.2025.6.3.4439
<![CDATA[Investing in the stock market is challenged by high volatility, which often leads to inaccurate price predictions. Prediction models often struggle to handle the fluctuation phenomenon and produce unstable forecasts. This study aims to predict stock prices in three banks, namely PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) using Long Short-Term Memory (LSTM) with the integration of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for anomaly detection. DBSCAN is applied with an epsilon (ε) of 0.5 and a minimum of 5 samples using Euclidean distance. The LSTM model consists of two hidden layers with 50 units, optimized using Adam, and applying the Mean Squared Error (MSE) loss function. The results show that DBSCAN improves prediction accuracy under several conditions. For BBCA stock, the lowest MSE was 0.003 at the 2nd fold with DBSCAN compared to 0.006 without DBSCAN. For BMRI stock achieved an MSE of 0.003 at the 4th fold with DBSCAN, while the 5th fold without DBSCAN obtained 0.000. For BBRI stock showed the best MSE of 0.003 at the 2nd fold with DBSCAN and the 5th fold without DBSCAN. These results show that the integration of DBSCAN can improve prediction especially when extreme price fluctuations occur. This research contributes to the development of stock price prediction methods that can be one of the benchmarks for investors before making decisions so that they do not experience losses.]]>
