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
1,048 Documents
<![CDATA[Stacking Ensemble RNN-LSTM Models for Forecasting the IDR/USD Exchange Rate with Nonlinear Volatility ]]>
<![CDATA[Pratiwi, Windy Ayu]]>;
<![CDATA[Sumertajaya , I Made]]>;
<![CDATA[Notodiputro , Khairil Anwar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5057
<![CDATA[Abstract - Predicting exchange rates with high volatility and nonlinear patterns presents a critical challenge in financial analysis. Deep learning models such as RNN and LSTM are widely used for their ability to capture temporal dependencies, yet each has limitations when applied individually. This study aims to enhance the prediction accuracy of the Indonesian Rupiah (IDR) to US Dollar (USD) exchange rate by implementing a stacking ensemble approach that combines RNN and LSTM models. The dataset consists of 522 weekly observations from January 2015 to December 2024, sourced from the official website of Bank Indonesia (bi.go.id). In the proposed framework, RNN and LSTM serve as base learners, while linear regression acts as the meta-learner. Model performance is evaluated using RMSE, MAPE, and MSE. The results indicate that the stacking ensemble consistently outperforms the individual models, achieving an RMSE of 117.91, a MAPE of 0.01, and an MSE of 13,901.67. The model effectively captures historical patterns and delivers stable and accurate predictions. In conclusion, the stacking ensemble approach developed in this study contributes to the advancement of ensemble learning techniques in computer science and offers practical value for financial decision-makers, particularly in managing complex and dynamic exchange rate scenarios.]]>
<![CDATA[Enhancing BERTopic with Neural Network Clustering for Thematic Analysis of U.S. Presidential Speeches ]]>
<![CDATA[Anggai, Sajarwo]]>;
<![CDATA[Zain, Rafi Mahmud]]>;
<![CDATA[Tukiyat, Tukiyat]]>;
<![CDATA[Waskita, Arya Adhyaksa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5090
<![CDATA[Understanding the underlying themes in presidential speeches is critical for analyzing political discourse and determining public policy direction. However, topic modeling in this context presents difficulties, particularly when clustering semantically rich topics from high-dimensional embeddings. This study seeks to improve topic modeling performance by incorporating a Neural Network Clustering (NNC) approach into the BERTopic pipeline. We analyze 2,747 speeches delivered by U.S President Joe Biden (2021-2025) and compare three clustering techniques: HDBSCAN, KMeans, and the proposed Autoencoder-based NNC. The evaluation metrics (UMass, NPMI, Topic Diversity) show that NNC produces the most coherent and diverse topic clusters (UMass = -0.4548, NPMI = 0.0234, Diversity = 0.3950, ). These findings show that NNC can overcome the limitations of density and centroid-based clustering in high-dimensional semantic spaces. The study contributes to the field of Natural Language Processing by demonstrating how neural-based clustering can improve topic modeling, particularly for complex, real-world political corpora.]]>
<![CDATA[Multimodal Biometric Recognition Based on Fusion of Electrocardiogram and Fingerprint Using CNN, LSTM, CNN-LSTM, and DNN Models ]]>
<![CDATA[Agustina, Winda]]>;
<![CDATA[Nugrahadi, Dodon Turianto]]>;
<![CDATA[Faisal, Mohammad Reza]]>;
<![CDATA[Saragih, Triando Hamonangan]]>;
<![CDATA[Farmadi, Andi]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Parenreng, Jumadi Mabe]]>;
<![CDATA[Alkaff, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5098
<![CDATA[Biometric authentication offers a promising solution for enhancing the security of digital systems by leveraging individuals' unique physiological characteristics. This study proposes a multimodal authentication system using deep learning approaches to integrate fingerprint images and electrocardiogram (ECG) signals. The datasets employed include FVC2004 for fingerprint data and ECG-ID for ECG signals. Four deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Deep Neural Network (DNN)—are evaluated to compare their effectiveness in recognizing individual identity based on fused multimodal features. Feature extraction techniques include grayscale conversion, binarization, edge detection, minutiae extraction for fingerprint images, and R-peak–based segmentation for ECG signals. The extracted features are combined using a feature-level fusion strategy to form a unified representation. Experimental results indicate that the CNN model achieves the highest classification accuracy at 96.25%, followed by LSTM and DNN at 93.75%, while CNN-LSTM performs the lowest at 11.25%. Minutiae-based features consistently yield superior results across different models, highlighting the importance of local feature descriptors in fingerprint-based identification tasks. This research advances biometric authentication by demonstrating the effectiveness of feature-level fusion and CNN architecture for accurate and robust identity recognition. The proposed system shows strong potential for secure and adaptive biometric authentication in modern digital applications.]]>
<![CDATA[Classification Of Sea Wave Heights On The North Coast Of Central Java Using Random Forest ]]>
<![CDATA[Supriyanto, Aji]]>;
<![CDATA[Diartonor, Dwi Agus]]>;
<![CDATA[Hartono, Budi]]>;
<![CDATA[Jananto, Arief]]>;
<![CDATA[Afandi, Afandi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5108
<![CDATA[Global climate change has triggered an increase in the occurrence of significant wave heights (SWH) and sea level rise (SLR) in coastal areas, including the northern coast of Central Java, Indonesia (Pantura). These phenomena directly impact maritime activities, coastal erosion, and tidal flooding. This study aims to classify and predict significant wave height (SWH) and sea level rise (SLR) trends using a machine learning approach based on the Random Forest (RF) algorithm. Daily meteorological and oceanographic observation data from 2019 to 2024, provided by BMKG, serve as the main dataset. The dataset includes wind speed, ocean current velocity, air pressure, and wave direction. SWH is categorized into three classes: Calm, Low, and Moderate. The classification model achieved excellent performance with an accuracy of 98.54%, a macro F1-score of 0.942, and maintained strong accuracy even for the minority class (Moderate) despite data imbalance. The RF Regressor for SWH prediction yielded an R² of 0.864, MAE of 0.067, and RMSE of 0.109 m. Visualizations such as scatter plots, boxplots, and heatmaps supported the conclusion that ocean current speed and wave period are key factors influencing SWH. The study concludes that Random Forest is effective for classifying and predicting sea conditions in tropical regions like Pantura, and it is feasible for implementation in data-driven early warning systems to mitigate coastal risks. This contributes to marine safety and coastal risk mitigation planning.]]>
<![CDATA[Herbal Plant Classification Using EfficientNetV2B0 Model and CRISP-DM Approach ]]>
<![CDATA[Sonita, Anisya]]>;
<![CDATA[Anggriani, Kurnia]]>;
<![CDATA[Vatresia, Arie]]>;
<![CDATA[Putri, Tiara Eka]]>;
<![CDATA[Darnita , Yulia]]>;
<![CDATA[Zahra, Syakira Az]]>;
<![CDATA[Aprilia, Vilda]]>;
<![CDATA[Aziz, Dzakwan Ammar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5141
<![CDATA[Herbal remedies have long been utilized by Indonesian communities as part of traditional medicine. However, identification of these natural resources is often challenging due to the morphological similarities among various species, which demand expert knowledge to differentiate. This study aims to implement the EfficientNetV2B0 model architecture for classifying medicinal leaves through an Android-based application designed to support recognition tasks. The dataset was composed of augmented images of plant foliage. The model was trained using the TensorFlow framework and evaluated to measure classification performance. Results demonstrate that EfficientNetV2B0 achieves excellent accuracy, with validation scores exceeding 97%, outperforming several other deep learning models. The resulting application allows the general public to identify local medicinal species more easily. This study contributes to the field of computer vision by providing an accurate and efficient classification framework, particularly beneficial for health-related informatics in biodiversity-rich regions.]]>
<![CDATA[A User-Driven E-Audit System for Improving Transparency and Efficiency in Regional Government Supervision ]]>
<![CDATA[Aminudin, Nur]]>;
<![CDATA[Hidayat, Nurul]]>;
<![CDATA[Feriyanto, Dwi]]>;
<![CDATA[Mukaromah, Hafsah]]>;
<![CDATA[Septasari, Dita]]>;
<![CDATA[Awaliyani, Ikna]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5145
<![CDATA[Internal audit processes in regional government institutions often face challenges such as time inefficiency, low transparency, and poorly digitized documentation. This study aims to develop an E-Audit system to enhance the effectiveness of internal supervision in a regional inspectorate environment. Employing a user-centered design approach and a structured system development methodology, this research involved key roles—auditors, technical controllers, and follow-up teams—throughout the design and testing stages. The developed system integrates three core phases of the audit process—planning, reporting, and follow-up—into a single, modular, and interactive digital platform. Implementation results indicate a significant improvement in audit efficiency, with a reduction of more than 50% in process duration compared to manual methods. The system also enhances documentation consistency through digital audit trails, role-based dashboards, and automatic reporting features. User acceptance testing revealed a high level of satisfaction, with users highlighting the system’s ease of use, increased accuracy, and alignment with daily audit tasks. Additionally, user feedback emphasized the need for integrated notification features and inter-unit communication tools, indicating readiness for more advanced digital transformation. Overall, this study provides practical value as a model for digital audit implementation at the regional government level while contributing to the advancement of Computer Science through the application of software engineering principles and information systems to support digital government oversight. The developed E-Audit model can serve as a reference for designing real-time collaborative public auditing systems relevant to the development of information systems engineering and computational governance.]]>
<![CDATA[Automated Classification of Mungkus Fish Freshness Based on Eye and Gill Images Using the Naive Bayes Algorithm ]]>
<![CDATA[Darnita, Yulia]]>;
<![CDATA[Toyib, Rozali]]>;
<![CDATA[Sonita, Anisya]]>;
<![CDATA[Putra, Andika]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5146
<![CDATA[The problem of assessing the freshness of fish, especially Mungkus fish, is usually directed at several physical indicators, such as eye appearance, gill condition, meat quality, and odor. This traditional method is often considered inaccurate and requires certain expertise, therefore a more effective and objective method is needed to assess the freshness level of Mungkus fish, which in turn can provide benefits for both fishermen and the public in general. The solution to this problem by using the Naïve Bayes method in classifying the freshness level of Mungkus fish based on eye and gill images has proven to be a fairly efficient approach. The Naïve Bayes method itself is a simple but very effective algorithm in the field of machine learning, and operates based on Bayes' Theorem with the assumption that features are independent of each other. This method can be applied in the initial stage of classification by utilizing basic features taken from images of fish eyes and gills. Based on testing 30 new data sets, the clustering system demonstrated an accuracy rate of 66.67%, indicating that 20 data sets were correctly classified according to their actual conditions. On the other hand, 10 data sets, or 33.33%, could not be categorized correctly. Of the 30 old data sets tested, the system was able to correctly classify 19 (63.33%), while 11 (36.67%) still had errors in their classification predictions. Overall, the system successfully performed data clustering with 65% accuracy, with the remaining 35% still showing errors in the classification process. ]]>
<![CDATA[From Monoliths to Microservices: Designing a Scalable Super App Architecture for Academic Services at Universitas Jenderal Soedirman ]]>
<![CDATA[Wijayanto, Bangun]]>;
<![CDATA[Iskandar, Dadang]]>;
<![CDATA[Rahayu, Swahesti Puspita]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5237
<![CDATA[Jenderal Soedirman (Unsoed) currently operates more than 30 monolithic information systems built with heterogeneous technology stacks, resulting in duplicate functionality, inconsistent user experience, and high maintenance costs. This study designs a modular, microservices‑based Super App architecture that integrates core academic services (KRS/KHS, transcript, student & lecturer attendance, lecturer activity log) and a parent/guardian monitoring feature. Using the Design Science Research (DSR) method, we (1) identified problems via a technology audit and problem–objective matrix; (2) designed the artifact with Domain‑Driven Design, C4 modelling, and API‑first contracts; (3) demonstrated a working prototype with API Gateway, SSO, and event‑driven notifications; (4) evaluated performance (<300 ms latency for 500–1000 concurrent users) and stakeholder impact; and (5) communicated results through this paper. The proposed architecture reduces integration complexity, supports zero‑downtime deployment, and enhances transparency for parents without violating consent and privacy. The validated blueprint provides a roadmap for transforming legacy campus systems into a scalable, observable, and governable Super App.]]>
<![CDATA[Utility-Based Buffer Management for Enhancing DTN Emergency Alert Dissemination in Jakarta's Urban Rail Systems ]]>
<![CDATA[Agussalim, Agussalim]]>;
<![CDATA[Viet Ha, Nguyen]]>;
<![CDATA[Putra, Handie Pramana]]>;
<![CDATA[Adila, Ma’ratul]]>;
<![CDATA[Diyasa, I Gede Susrama Mas]]>;
<![CDATA[Rahmat, Basuki]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5241
<![CDATA[The efficiency of emergency alert dissemination in highly populated and densely urban transport networks, such as Jakarta's integrated rail system, is undermined by sporadic connectivity and limited network resources. In this environment, an initial comparison of baseline Delay-Tolerant Network (DTN) routing protocols revealed that flooding-based routers, such as Epidemic, while achieving above-average delivery rates, suffered from high overhead and poor buffer utilization. This paper fills this gap by proposing the Combined Utility Router, a novel buffer management policy that overcomes the limitations of naive strategies, such as Drop-Oldest. Our approach holistically evaluates a message's value by assigning a weighted utility function based on its Time-To-Live (TTL), estimated total replicas, message size, and a user-defined priority. The router maintains high-value messages by discarding the message deemed the lowest utility score under the buffer constraint. Utility-based simulations in The ONE simulator demonstrate that applying our approach to Epidemic routing improves delivery probability, reduces average latency in high network congestion scenarios, while maintaining overhead rates. This work confirms that, in the context of developing reliable and efficient emergency communication systems for challenging urban topographies, optimizing buffer management extends beyond simply selecting the appropriate protocol.]]>
<![CDATA[Implementation of K-Means on Packaged Coffee Sales Data for Simulating Goods Entry in Sole Proprietorship Businesses ]]>
<![CDATA[Triansyah, Agri]]>;
<![CDATA[Wijayanto, Bangun]]>;
<![CDATA[Paramestuti, Ayu Anjar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5245
<![CDATA[In retail businesses operating under the sole proprietorship structure, decision-making regarding partnerships with beverage distributors—especially those offering packaged coffee—remains a challenge. Store owners often face uncertainty about the profitability of accepting product offerings, which can lead to suboptimal inventory decisions. This study addresses that issue by simulating goods entry scenarios and applying clustering techniques to historical packaged coffee sales data, enabling data-driven insights into product performance and distributor value. Studies focusing on clustering within retail include segmenting customer behaviour and stock management strategies, yet many lacked specific application to single owner businesses and product-centric simulations. This research is novel in its contextual focus on packaged coffee distribution within sole proprietorship environments, integrating real sales metrics and clustering algorithms to empower store owners with actionable evaluation tools. Results demonstrate that clustering reveals patterns of profitable product categories and distributor consistency, offering scalable insights for micro-retail optimization. The findings provide a framework that differs from prior studies by emphasizing the intersection between small business dynamics and algorithmic decision support. Ultimately, this research contributes to the advancement of informatics by demonstrating how clustering-based simulations can enhance decision-making in micro-retail environments through practical, data-driven methodologies. ]]>
