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Teknika
Published by Institut Informatika Indonesia Surabaya
ISSN : 25498037     EISSN : 25498045     DOI : https://doi.org/10.34148/teknika
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Teknika is a peer-reviewed journal dedicated to disseminate research articles in Information and Communication Technology (ICT) area. Researchers, lecturers, students, or practitioners are welcomed to submit paper which has topic below: Computer Networks Computer Security Artificial Intelligence Machine Learning Human Computer Interaction Computer Vision Virtual/Augmented Reality Digital Image Processing Data Mining Web Mining Computer Architecture Software Engineering Decision Support System Information System Audit Business Information System Datawarehouse & OLAP And any other topics relevant with Information and Communication Technology (ICT) area
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 356 Documents
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Learning Temporal Graph Representations for Intelligent Control in 3D Endless Runner Games Oddy Virgantara Putra; Daffa Sesa Rabbani; Alvin Fredericco; Arsyapradana Fadlanabil Bahri
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1416

Abstract

Recent advancements in deep learning have significantly enhanced body gesture recognition, enabling real-time interaction between humans and machines through the modeling of spatial–temporal features. However, many existing approaches primarily rely on frame-based or visual feature representations and are often evaluated in offline settings, which limits their stability and responsiveness when applied to real-time 3D game environments that require continuous and dynamic player movement. In this paper, we develop a gesture-controlled endless runner game using a skeleton-based Graph Neural Network–Long Short-Term Memory (GNN–LSTM) model. The proposed system enables real-time interaction without the need for conventional input devices and is directly integrated into a Unity-based game environment. A dataset of 1,000 gesture videos across five classes (Jump In Place, Jump Left, Jump Right, Looking Down, and Still Pose) is processed using MediaPipe Pose to extract 33 body keypoints per frame, which are then normalized and represented as graph structures to capture spatial and temporal motion patterns. Experimental results show that the GNN–LSTM model achieves a validation accuracy of up to 97.5% and a test accuracy of 96%. Although CNN–LSTM attains slightly higher test accuracy, the GNN–LSTM model demonstrates more stable validation performance and robustness by leveraging skeleton-based representations, making it more suitable for real-time gesture control in interactive gameplay. Integrated with Unity, the proposed system allows intuitive and responsive control of character movements during gameplay. These findings highlight the effectiveness of temporal graph-based representations for stable and natural gesture-based human–computer interaction in real-time 3D games.
Usability Evaluation of the SanguPalu E-Government Mobile Application Using PSSUQ and UMUX: a Case Study in Palu Mohammad Hikmal Naki; Deny Wiria Nugraha
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1417

Abstract

The SanguPalu mobile application is a e-government digital service platform developed by the Palu City Government to enhance accessibility, transparency, and efficiency in public service delivery. Despite its comprehensive features, user feedback indicates issues related to interface clarity, navigation efficiency, and system consistency, suggesting potential usability problems. This study aims to evaluate the usability level of the SanguPalu application using a descriptive quantitative approach integrating the Post-Study System Usability Questionnaire and the Usability Metric for User Experience. Data were collected from 118 active users through an online survey. Usability was assessed using average scores of PSSUQ subscales and a global UMUX score. The results show that system usefulness achieved a score of 2.87 (upper limit), while information quality obtained a score of 2.87 (lower limit). Interface quality recorded the highest score of 3.09 (above upper limit), indicating the most critical usability issue. The overall PSSUQ score was 2.93 (upper limit). The UMUX analysis produced a score of 65.20, reflecting a marginal-to-high usability level consistent with the PSSUQ findings. By combining subscale-specific and global usability measurements, this study offers a more nuanced understanding of usability issues in a local government mobile application context. These findings indicate that while SanguPalu adequately supports core service functions, improvements in interface design and navigation structure are essential to strengthen user satisfaction and overall service quality.
Implementation of XGBoost Algorithm in Zang Organs Syndrome Diagnosis and Therapy Recommendations Yosefina Finsensia Riti; Andre Hartanto; Onny Priskila; Benaya Azareel Oentoro
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1430

Abstract

In Traditional Chinese Medicine (TCM), syndrome is primarily categorized into Zang and Fu organ syndromes. Zang organs include the heart, liver, spleen, lungs, and kidneys, and diagnosis is derived from patient complaints, symptoms, and examination findings. However, Zang organ syndrome determination is highly dependent on clinical experience, resulting in diagnostic subjectivity and variability among practitioners. In addition, symptom overlap across different organs further complicates the diagnostic process and challenges standardization. To address these issues, this study proposes an artificial intelligence (AI)-based intelligent system to assist Zang organ syndrome diagnosis and provide acupuncture therapy recommendations. The system applies machine learning to improve diagnostic accuracy and consistency, utilizing the Extreme Gradient Boosting (XGBoost) algorithm due to its effectiveness in classifying complex and nonlinear patterns commonly found in TCM diagnostics.  The dataset was constructed based on standardized TCM references and expert-defined symptom–syndrome relationships, consisting of 39 symptom indicators, 72 Zang organ syndrome classes, and 15,532 data instances. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied during training.  Experimental results show that the proposed model achieved an accuracy of 98.6% and a macro-average F1-score of 83% for both syndrome diagnosis and therapy recommendation tasks.  The results demonstrate that the proposed AI approach effectively supports Zang organ syndrome identification and acupuncture point recommendation, contributing to improved diagnostic efficiency and decision support in TCM practice.
Implementation of BERT-BiLSTM Model for Sentiment Classification of Movie Reviews on Rotten Tomatoes Thoriq Gifari Nurizky; Endah Setyowati
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1431

Abstract

This study aims to analyze movie review sentiment using a Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory (BERT-BiLSTM) hybrid model designed to capture global contextual information and local sequential dependencies in complex texts. The dataset is obtained from Rotten Tomatoes Critic Reviews, consisting of 1,048,576 entries, of which 10,000 samples are randomly selected due to computational limitations. The research methodology includes text preprocessing, tokenization with the BERT tokenizer, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The implementation uses the AdamW optimizer with a learning rate of 2×10⁻⁵ for three epochs on an AMD Ryzen 3 5300U CPU. For comparison, the hybrid model is evaluated alongside a single BERT model and a single BiLSTM model. Experimental results show that the BERT-BiLSTM model achieves the highest performance, obtaining an accuracy, precision, recall, and F1-score of 84%. In contrast, the single BERT model achieved 66% accuracy and an F1 score of 57%, while the single BiLSTM model achieved 63% accuracy and an F1 score of 48%. These results indicate that combining transformer-based contextual representation with bidirectional sequence modeling improves the effectiveness of sentiment classification compared to the single-model approach. Therefore, the BERT-BiLSTM hybrid method proves effective for analyzing complex textual sentiment and has potential applications across various review domains that require deep contextual understanding.
Ontix: Blockchain-Based NFT Decentralized e-Ticketing Development Elizabeth Nathania Witanto; Christopher Andreas; Rudi Limantara; Louis Fernando; Lie Samuel Miracle Kristanto; Richie Reuben Hermanto
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1432

Abstract

Event ticketing systems, such as concerts, festivals, and sports matches, face persistent challenges, including ticket forgery, duplication, resale manipulation, and fraud in secondary markets. Centralized electronic ticketing systems, while digitized, remain vulnerable to identity theft, seller unaccountability, and unfair distribution due to their reliance on intermediaries and a single point of failure. To address these issues, this research introduces Ontix, a decentralized blockchain-based e-ticketing platform utilizing Non-Fungible Tokens (NFTs) compliant with the ERC-721 standard. By leveraging blockchain’s immutability, transparency, and decentralization, Ontix ensures verifiable ownership, tamper-proof ticket issuance, and automated transactions through smart contracts. The system enforces anti-scalping measures, including resale time and price limits, while enabling real-time QR-based validation directly linked to smart contracts. Ontix integrates Layer-2 Optimism Sepolia for scalability and lower gas fees, and employs the InterPlanetary File System (IPFS) via Pinata for decentralized metadata storage, alongside Cloudinary for media management. This hybrid architecture guarantees transparency, security, and operational efficiency. By eliminating intermediaries and automating ticket lifecycle management, Ontix provides an accountable, tamper-resistant, and low-cost e-ticketing ecosystem, as well as a user-centric ticketing ecosystem, representing a significant advancement toward the future of decentralized event management.
Continuance Intention in E-Government Use: A Systematic Literature Review of Global Trends, Models, and Determinants Ahmad Zainur Ridho; Lisana Lisana
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1434

Abstract

The use of e-government has become a global trend, with government success based on public continuance intention toward useful and reliable services. Systematic literature reviews on continuance intention are still limited in the context of e-government. Therefore, this study was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The literature search included four databases: Google Scholar, ACM (Association for Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), and ScienceDirect, spanning 2021–2025. This yielded 656 initial data points, which were rigorously screened against inclusion and exclusion criteria, leaving 28 articles eligible for analysis. The results show that the global trend in research publications on continuance intention in e-government is relatively stable each year, with the largest contributions coming from China, Indonesia, and Malaysia. Most of the articles were published in reputable Q1 and Q2 journals, demonstrating the high quality and relevance of this topic in the academic world. Among models, the IS Success Model, ECM (Expectation-Confirmation Model), and UTAUT (Unified Theory of Acceptance and Use of Technology) are the most widely used. Meanwhile, a quantitative approach dominates the research method, reflecting the research focus on empirically testing the relationship between variables. Determinant factor analysis shows that Satisfaction, Trust, and System Quality are the most influential variables in the intention to continue using e-government. This finding confirms that the success of e-government implementation depends not only on technology, but also on user experience and trust in digital government services. This research contributes to enriching the literature on e-government by mapping the model used for e-government continuance intention. Practically, it helps the government improve the quality of the system, as well as increase user satisfaction, to continue using government services.
Comparison and Implementation of CNN Facial Emotion Recognition Model with Hyperparameter Analysis on Multiple Datasets Xaviera Valentina Tandianto; Dwi Hosanna Bangkalang; Nina Setiyawati
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1445

Abstract

This study presents a systematic comparison and implementation of a Convolutional Neural Network (CNN) for Facial Emotion Recognition (FER) across multiple public datasets, namely FER-2013, FER+, RAF-DB, and AffectNet. Unlike previous studies that focused on a single dataset or different model architectures, The main contributions of this research consist of three aspects. First, a five-layer integrated CNN architecture is used to enable fair cross-dataset evaluation within a consistent training and testing framework. Second, structured hyperparameter tuning is performed, including variations in learning rate, batch size, filter configuration, and dropout rate, resulting in a stable and reproducible model configuration. Third, an in-depth analysis was conducted to explore the impact of annotation quality and dataset complexity on model performance. The experimental results show that FER+ achieved the highest accuracy and weighted F1 score thanks to better label consistency, followed by RAF-DB, while FER-2013 and AffectNet experienced a decline in performance due to label noise and higher pose and lighting variations. Further confusion matrix analysis shows that happy and neutral expressions are classified more reliably, while negative emotions such as anger, fear, and disgust remain challenging. To validate practical application, the best-performing model was implemented in a webcam-based facial expression recognition prototype using Python and OpenCV, demonstrating reliable frame-level emotion inference under controlled real-time conditions.
Implementation of Random Forest Algorithm with Random Oversampling for Sentiment Analysis of X Users Toward the Sekolah Rakyat Program Deni Agus Hendrawan; Ali Ibrahim; Yadi Utama; Endang Lestari Ruskan; Fathoni
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1446

Abstract

Social media site X has emerged as a significant platform for voicing public views on government initiatives, such as the Sekolah Rakyat Program. Nevertheless, utilizing social media information for sentiment analysis often faces challenges due to class imbalance, which may result in skewed predictions from models. This research seeks to examine public sentiment and assess how well the Random Forest algorithm performs when paired with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction and Random Oversampling (ROS) methods to mitigate class imbalance. A dataset comprising 8,623 tweets was gathered and split into training and testing sets using an 80:20 ratio. The results of the experiments indicate that the suggested method demonstrates robust and realistic classification performance, achieving an accuracy of 80.99%, along with a weighted average score in precision, recall, and F1-score of 0.81. Additionally, the sentiment analysis indicates that the majority of public opinions are largely positive, with roughly 69.4% of the testing data reflecting a favorable outlook toward free education access and school improvement efforts. These findings suggest that the proposed model provides reliable performance in capturing public sentiment patterns.
Modification of YOLOv11 Architecture with Convolutional Block Attention Module Integration for Batik Besurek Motif Detection Reksi Hendra Pratama; Ernawati; Widhia Oktoeberza KZ
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1449

Abstract

Batik Besurek is a distinctive cultural heritage textile from Bengkulu, characterized by the combination of Arabic calligraphy, floral, and faunal motifs. However, public recognition of these motifs continues to decline, particularly among younger generations, creating an urgent need for digital preservation through modern computer vision techniques. This study aims to develop an enhanced object detection model for identifying Batik Besurek motifs by integrating the Convolutional Block Attention Module (CBAM) into the Neck (feature fusion layers) of the YOLOv11 architecture. A total of 3,876 images were collected from online repositories and field documentation in Bengkulu, followed by annotation and data augmentation using the Roboflow platform. Specifically, Mosaic augmentation combined with a close-mosaic strategy proved most significant in stabilizing the detection of dense and minority motifs. The research employs an experimental methodology by constructing two models, standard YOLOv11 as the baseline and YOLOv11+CBAM as the proposed model, trained under identical hyperparameter configurations. Model performance was evaluated using standard object detection metrics, namely precision, recall, mAP@50, and mAP@50–95. The experimental results demonstrate that the integration of CBAM significantly improves feature extraction capability, enabling the model to better capture dense, small, and visually complex batik motifs. The proposed model outperforms the baseline across all metrics, achieving a mAP@50 of 0.988, mAP@50-95 of 0.914, Recall of 0.971, and Precision of 0.959. These results indicate superior sensitivity and localization accuracy, particularly for minority motif classes, confirming that incorporating attention mechanisms into YOLOv11 provides a robust solution for the digital preservation of Besurek patterns.
Implementing Validated UI/UX Design into a Real System: An Agile Kanban-Based Development Evaluation in an Educational Information System Indah Lestari; M. Fakhri Salman; Anggy Trisnadoli; Muhammad Mahrus Zain
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1450

Abstract

UI/UX research often ends at the prototype validation, while the implementation process remains limited in empirical studies. This study implements UI/UX designs validated in previous research into an information system and evaluates the role of the Agile Kanban approach in bridging the gap between design and real system implementation. The research method uses an implementation-based evaluation that includes system requirements alignment, Agile Kanban-based iterative development, and post-implementation evaluation. The results show that of the total 34 screens developed, 6 were implemented according to the initial design, 8 were adjusted during development, and 20 additional screens were developed to meet operational stakeholder needs. Development performance reached 135%, calculated as the ratio between completed and initially planned features. The development process was conducted through 4 Kanban cycles that allowed for controlled adjustments to business logic and feature additions. This study identified 5 main gaps in the implementation of the design in the real system. Post-implementation evaluation showed that all 79 functional requirements were successfully implemented with a total of 27 functional features, exceeding the initial plan of 20 features. The system was declared fully accepted through User Acceptance Testing and achieved a System Usability Scale score of 78.3 from 60 respondents, which is in the Acceptable category. These results indicate that Agile Kanban is effective in bridging the gap between UI/UX design validation and real system implementation, and is able to maintain usability quality in operational systems. This provides practical direction for organisations that seek to operationalise validated designs within dynamic development settings.

Page 33 of 36 | Total Record : 356

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