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Transaction on Informatics and Data Science
Published by Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Purwokerto
ISSN : -     EISSN : 30641772     DOI : https://doi.org/10.24090/tids
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Transactions on Informatics and Data Science (TIDS), with ISSN: 3064-1772 (online), is a scientific journal that publishes the latest research in the fields of informatics and data science, focusing on both theoretical advances and practical applications. Published by the Department of Informatics, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto, Purwokerto, this journal serves as a platform for researchers, academics, and practitioners to share new ideas and innovations in data science, artificial intelligence, natural language processing, cloud computing, and information technology applications across various domains. It promotes collaboration and deep knowledge exchange within the scientific community, bridging the gap between theory and practice in the rapidly evolving fields of informatics and data science. Aims Transaction on Informatics and Data Science aims to advance the frontiers of informatics and data science knowledge by publishing high-quality research that encompasses theoretical advancements and practical applications. The journal seeks to contribute significantly to the understanding and developing of innovative approaches, methodologies, and technologies in these domains. Scopes The scope of "Transaction on Informatics and Data Science" covers a wide range of topics related to informatics and data science, including but not limited to: - Data analysis and mining - Artificial intelligence and machine learning - Natural language processing and understanding - Cloud computing and big data technologies - Information retrieval and knowledge management - Data-driven decision-making and predictive modelling - Internet of Things (IoT) and data analytics - Cybersecurity and privacy in data science - Informatics and data science applications in various healthcare, finance, education, and other domains. The journal welcomes original research articles, reviews, case studies, and technical notes that contribute significantly to advancing knowledge and practice in informatics and data science. Submissions should demonstrate novelty, tightness, and relevance to the rapidly evolving landscape of information technology and data-driven decision-making processes.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 4 Documents
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Search results for , issue "Vol. 2 No. 1 (2025)" : 4 Documents clear
Determining Promotions at UD. Jakarta Pixel using Web-Based FP-Growth Association Model Potale, Lisa Elisia; Ahmad, Muhammad Sabri; Khairan, Amal
Transactions on Informatics and Data Science Vol. 2 No. 1 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i1.12208

Abstract

In the rapidly advancing digital era, the growth of e-commerce has significantly transformed the retail business paradigm. However, physical stores still play a crucial role in providing direct and personal experiences to customers. UD. Jakarta Pixel, a physical store specializing in photography and electronic products, faces increasingly intense competition from various e-commerce platforms. To remain competitive, sophisticated and effective promotional strategies are required. This research uses the FP-Growth association model as the method to determine effective website-based product promotions at UD. Jakarta Pixel. The research results indicate that the FP-Growth algorithm successfully analyzes customer purchasing patterns, identifies relationships between frequently purchased products, and enables the store to design more targeted promotions according to customer preferences. Implementing this system not only enhances operational efficiency in data analysis but also provides accurate information for strategic decision-making, optimizing product promotion strategies, and ultimately increasing sales. In conclusion, the application of the FP-Growth algorithm at UD. Jakarta Pixel is an innovative solution that strengthens the competitiveness and sustainability of physical stores amid the rapid growth of e-commerce, leveraging transaction data analysis for more effective and efficient promotion planning.
Haar Cascade Classifier and Adaboost Algorithm for Face Detection with the Viola-Jones Method Nidom, Mohammad Saichu
Transactions on Informatics and Data Science Vol. 2 No. 1 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i1.12276

Abstract

Face detection is a significant challenge in image processing and computer vision, with broad security, identity recognition, and human-computer interaction applications. This study explores the effectiveness of the Haar Cascade Classifier method optimized with Adaboost to improve the accuracy and efficiency of face detection in various head covering conditions. In this experiment, two approaches were compared: using the Haar Cascade Classifier independently and in combination with Adaboost, with evaluation based on metrics such as accuracy, precision, sensitivity, and F1-Score. The results showed that the Adaboost combination significantly improved detection accuracy, with the "Hooded" class achieving an accuracy of 99.2% and the average detection time reduced from 14.9 seconds to 1.9 seconds. These findings show that the use of optimization techniques such as Adaboost not only improves detection performance but also overall system efficiency. The conclusion of this study emphasizes the importance of combining methods in developing a more robust and efficient face detection system. The implications of this research can be applied to create more effective security and facial recognition applications and pave the way for further study in optimizing object detection algorithms.
Corpus Development and NER Model for Identification of Legal Entities (Articles, Laws, and Sanctions) in Corruption Court Decisions in Indonesia Subowo, Edy; Bukhori, Imam; warto
Transactions on Informatics and Data Science Vol. 2 No. 1 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i1.13592

Abstract

This study aims to develop an annotated corpus and a deep learning-based Named Entity Recognition (NER) model to identify legal entities in Indonesian corruption court rulings. The corpus was constructed from 450 Supreme Court documents related to the Anti-Corruption Laws (Laws No. 31/1999), collected via web scraping, with semi-automatic annotation (regex) and validation by legal experts. A total of 12,000 entities (Article, Laws, Sanctions) were tagged in IOB format, creating the first specialized dataset for Indonesian corruption laws. The NER model combines the IndoBERT (pre-trained language model) architecture with a CRF layer, fine-tuned to handle legal text complexities such as hierarchical article references (paragraphs, clauses) and amended laws citations (jo.). Evaluation using 10-fold cross-validation revealed that the model achieved an F1-score of 92.3%, outperforming standalone CRF (85.1%) and BiLSTM+CRF (88.7%), particularly in detecting ARTICLE entities (F1: 93.8%). Error analysis highlighted challenges in recognizing SANCTIONS entities (F1: 87.4%) due to sentence structure variability and conjunctions. The model’s implementation could accelerate judicial decision analysis, identify violation patterns, and support sanctions recommendation systems for laws enforcement. This research also provides legal entity annotation guidelines adaptable to other legal domains. Future work should expand to other laws (e.g., ITE Laws, Criminal Code) via transfer learning and integrate knowledge graphs to enhance entity relation detection.
The Role of Color in User Experience: A Systematic Literature Study of User Preferences for Dark and Light Mode Atsani, M. Rifqi; Mukaromah, Iif Alfiatul; Anugerah, M. Haikal Citra
Transactions on Informatics and Data Science Vol. 2 No. 1 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i1.13903

Abstract

The increasing use of dark mode and light mode in user interfaces has prompted many studies to determine their effects on user experience. Findings in the literature show that research is still fragmented and does not provide a comprehensive understanding of the cognitive, emotional, and behavioral aspects of preferences for user interface themes. This study analyzed 25 selected scientific articles from 2020 to 2025. The identification topics were related to user preferences, performance, accessibility, and satisfaction with interface modes. The qualitative thematic analysis approach found 6 main themes, namely Readability and Accessibility, Cognitive and Emotional Responses, Usage Behavior and Preferences, Task Performance and Efficiency, Health and Environmental Impacts, and Interface Design and Satisfaction. The results showed that light mode was better in terms of readability and tasks that require high lighting, dark mode provides better visual comfort than light mode and reduces eye fatigue in low light conditions. User preferences were influenced by several things such as age, device type, environment, and emotional needs. This research emphasizes the development of an adaptive system that allows users to switch between dark and light modes according to their respective conditions is important. This research contributes to the field of Human Computer Interaction (HCI) by demonstrating an understanding of interface theme design as well as future research directions.

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