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PYTHON WEB SYSTEM TO RESTORE SQL SERVER DATABASE TO DRC WITH ADVANCED INFORMATION RETRIEVAL Rabertra, Devis; Saputra, Irwansyah
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 8 No 1 (2026): Maret 2026
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v8i1.559

Abstract

Disaster Recovery Centers (DRC) play a crucial role in ensuring the availability and continuity of database operations in enterprise environments. The process of restoring databases from production servers to DRCs is often performed manually, which can lead to errors such as selecting incorrect backups, corrupted files, and lengthy search times. The complexity increases with the growing number of databases and the variety of daily backup types.This study develops an automated system based on a Python Web Interface integrated with Advanced Information Retrieval (IR) to improve the accuracy and speed of finding relevant backups before restoration. The system employs Natural Language Processing (NLP) and multi-criteria relevance scoring, evaluating backup suitability based on fuzzy matching of database names, recency, semantic similarity, backup type, and file size.Testing was conducted using 28 backup records from 5 different databases. Results show that Advanced IR can accelerate backup searches in under 2 seconds, with relevance ranking ranging from 38% to 67%. Additionally, the automated restore process via Python achieved an average execution time of 7.49 seconds with a 100% success rate.
Web-Based Guest Lecture Information System for Committee and Student Users at FMIPA UNSRAT Sumakul, Andrea Emailly; Montolalu, Chriestie Ellyane Juliet Clara; Takaendengan, Mahardika Inra; Pinontoan, Benny; Kalengkongan, Wisard Widsli; Lapihu, Dodisutarma
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2026): Volume 7 Number 2 June 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i2.1608

Abstract

Guest lecture management at FMIPA UNSRAT currently suffers from significant fragmentation across WhatsApp, Google Forms, and paper attendance, leading to data duplication, information inconsistencies, and administrative inefficiency. This condition hinders effective decision-making and stakeholder engagement. This study aims to design and develop a centralized Web-Based Guest Lecture Information System to integrate the entire event lifecycle, including publication, registration, digital attendance, and reporting. The system was developed using the Waterfall model with Laravel framework and MySQL database. Comprehensive evaluation involved Black Box Testing, User Acceptance Testing (UAT) with committee members, and User Perception Testing with students. Results indicate a 100% success rate in Black Box Testing across 42 functional scenarios. UAT yielded a 90.4% acceptance rate among committee members, validating operational feasibility and workflow alignment. Furthermore, User Perception Testing achieved a 91.04% satisfaction score among students, with Behavioral Intention reaching 93.6%. These findings demonstrate that the system significantly reduces data fragmentation and improves administrative efficiency compared to manual processes. The system is deemed feasible for immediate deployment, offering a robust solution for centralized academic event management. However, limitations exist regarding financial module integration. Future work should focus on API integration with the central university portal and automated honorarium processing to further enhance scalability and institutional adoption.
Understanding Young Adults’ Mental Health Information-Seeking on Social Media: A Qualitative Exploration Chairil, Augustin Mustika; Alamiyah, Syifa Syarifah; Husna, Arina Himatul
Jurnal PIKMA : Publikasi Ilmu Komunikasi Media Dan Cinema Vol. 8 No. 2 (2026): Maret 2026
Publisher : Fakultas Ekonomi dan Ilmu Sosial Program Studi Ilmu Komunikasi Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/pikma.2026v8i2.2548

Abstract

Along with the rapid growth of social media, information related to mental health is increasingly presented in various forms of content. Social media has transformed into a medium that facilitates users, particularly young adults, in seeking mental health information. By using the concept of information search patterns, the algorithms and the role of social media as a platform, this article analyzes patterns of mental health information seeking among young adults and illustrates how they access social media. Through focus group discussions (FGDs) and in-depth interviews with health stakeholders, this article explores how young adults use social media to seek mental health information, which is largely driven by visually appealing content and often followed by self-diagnosis. Algorithms indirectly shape patterns of mental health content consumption, and content produced by government platforms frequently fails to appear within the algorithmic filters encountered by young adults. This condition poses a challenge for stakeholders in delivering digital literacy, particularly in mental health information content.
Password Security & User Access: Is Human Negligence the Weakest Point in Accounting Information Systems Ahmad Arif Aufar; Widanti Retno Palupi; Rina Tjandrakirana DP
Escalate : Economics and Business Journal Vol. 1 No. 02: Driving Change and Innovation in the Digital Age
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/escalate.v1i02.458

Abstract

Currently in the digital era, Accounting Information Systems play an important role in managing confidential financial data. This study evaluates whether human negligence is the main vulnerability in Accounting Information Systems compared to technical factors. Using the Systematic Literature Review method of several reputable national & international journals, it was found that human factors contribute up to 85% to data leaks. Major problems include poor password management, security fatigue, vulnerability to phishing, and internal access abuse. The results of the study confirm that advanced technologies such as encryption are often paralyzed due to user negligence. In conclusion, strengthening SIA security requires a holistic approach that integrates technical solutions such as Role-Based Access Control & Multi-Factor Authentication with cyber awareness training to mitigate the risk of user behavior as the system's weakest point.
UJI PRIVILEGE ESCALATION PADA LAB VULNHUB LIN.SECURITY MENGGUNAKAN TACTIC FRAMEWORK PRIVILEGE ESCALATION MITRE ATT&CK DENGAN METODE INFORMATION SYSTEM SECURITY ASSESSMENT FRAMEWORK (ISSAF) Putra, Muhammad Willdhan Arya; Coastera, Funny Farady; Putri, Tiara Eka
Rekursif: Jurnal Informatika Vol 14 No 1 (2026): Volume 14 Nomor 1 Maret 2026
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v14i1.41738

Abstract

This study aims to identify and evaluate privilege escalation techniques on Linux kernel-based operating systems using the Information System Security Assessment Framework (ISSAF) methodology and MITRE ATT&CK tactics. The research was conducted in the vulnerable VulnHub Lin.Security lab. Phases included planning (VirtualBox configuration), assessment (system enumeration and testing of 7 MITRE ATT&CK tactics: Abuse Elevation Control Mechanism, Account Manipulation, Create or Modify System Process, Escape to Host, Event Triggered Execution, Exploitation for Privilege Escalation, Hijack Execution Flow), and reporting. Results showed all seven tactics were successfully exploited in the lab environment, revealing vulnerabilities such as SetUID/SetGID misconfiguration, sudo issues, SSH key manipulation, systemd misuse, docker SUID exploitation, shell configuration file vulnerabilities, kernel exploits (PwnKit), and LD_PRELOAD hijacking. The main conclusion is that privilege escalation vulnerabilities in Linux systems can be exploited using MITRE ATT&CK tactics, emphasizing the importance of regular security audits and updates for risk mitigation.
Mapping Research Trends of Query Expansion in Information Retrieval: A Bibliometric Analysis Roberto Kaban
JCEIT: Journal of Computer Engineering and Information Technology Vol. 2 No. 2 (2026): JCEIT: Journal of Computer Engineering and Information Technology (March 2026)
Publisher : Karya Techno Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64810/jceit.v2i2.57

Abstract

This study aims to analyze the development of research on query expansion in the field of information retrieval using a bibliometric approach to understand research trends, distribution, and current research focus. The data were obtained from 676 publications indexed in Scopus during the period from 2020 to February 2026. The research method involves quantitative analysis of annual publication trends, distribution of subject areas, document types, and keyword analysis using VOSviewer to map keyword relationships through co-occurrence analysis, overlay visualization to identify keyword trends, and density visualization to observe the concentration of research topics. The results show fluctuations in the number of publications with a peak occurring in 2025 with 141 publications. The research is dominated by the Computer Science field with 596 publications, and the majority of documents are conference papers with 369 publications. Keyword analysis identifies core topics such as information retrieval with 483 occurrences, query expansion with 354 occurrences, and search engines with 221 occurrences. Recent research trends include large language models, word embedding, and retrieval-augmented generation. The keyword network visualization indicates a shift from traditional methods such as relevance feedback toward modern approaches based on artificial intelligence and machine learning, which are increasingly relevant for improving the effectiveness of information retrieval systems. These findings provide both quantitative and qualitative insights into the evolution of query expansion research. The results also highlight the integration of modern technologies in retrieval practices and provide a foundation for new researchers to identify trends, research gaps, and opportunities for future innovation. REFERENCES Ahmed, M. (2024). Bibliometrix: An Easy Yet Powerful Approach for Quantitative and Qualitative Analyses of Scholarly Literature. Information Research Communications, 1(1), 43–45. https://doi.org/10.5530/irc.1.1.7 Al-Lahham, Y. (2024). Improved Arabic Query Expansion using Word Embedding. https://doi.org/10.21203/rs.3.rs-4065010/v1 Allahim, A., Cherif, A., & Imine, A. (2025). Semantic approaches for query expansion: Taxonomy, challenges, and future research directions. PeerJ Computer Science, 11, e2664. https://doi.org/10.7717/peerj-cs.2664 Baumann, O., & Schoenfeld, M. (2024). PSQE: Personalized Semantic Query Expansion for user-centric query disambiguation. https://doi.org/10.21203/rs.3.rs-4178030/v1 Bernard, N., & Balog, K. (2025). A Systematic Review of Fairness, Accountability, Transparency, and Ethics in Information Retrieval. ACM Computing Surveys, 57(6), 1–29. https://doi.org/10.1145/3637211 Breuer, T., Frihat, S., Fuhr, N., Lewandowski, D., Schaer, P., & Schenkel, R. (2025). Large Language Models for Information Retrieval: Challenges and Chances. Datenbank-Spektrum, 25(2), 71–81. https://doi.org/10.1007/s13222-025-00503-x Ganti, L., Persaud, N. A., & Stead, T. S. (2025). Bibliometric analysis methods for the medical literature. Academic Medicine & Surgery. https://doi.org/10.62186/001c.129134 Hambarde, K. A., & Proença, H. (2023). Information Retrieval: Recent Advances and Beyond. IEEE Access, 11, 76581–76604. https://doi.org/10.1109/ACCESS.2023.3295776 Hidri, M. (2024). Learning-Based Models for Building User Profiles for Personalized Information Access. Interdisciplinary Journal of Information, Knowledge, and Management, 19, 010. https://doi.org/10.28945/5275 Kaban, R., Sihombing, P., Efendi, S., & Lydia, M. S. (2025a). Enhancing Retrieval Performance in Social Media Using Corpus-Based Query Expansion. 2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 1–6. https://doi.org/10.1109/AIMS66189.2025.11229497 Kaban, R., Sihombing, P., Efendi, S., & Lydia, M. S. (2025b). Enhancing retrieval performance in social media with corpus-based query expansion using bidirectional encoder representations from transformers. Eastern-European Journal of Enterprise Technologies, 5(2 (137)), 70–83. https://doi.org/10.15587/1729-4061.2025.340258 Kumar, R. (2025). Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence. Spectrum of Engineering and Management Sciences, 3(1), 45–62. https://doi.org/10.31181/sems31202535k Meliukh, V., Potapova, E., Nalyvaichuk, M., & Dychka, A. (2025). Query expansion based on context-dependent sentiment analysis in databases with domain-specific filtering. Eastern-European Journal of Enterprise Technologies, 1(2 (133)), 6–17. https://doi.org/10.15587/1729-4061.2025.322120 Naamha, E. Q., & Abdulmunim, M. E. (2024). Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12(1), 29–40. https://doi.org/10.14500/aro.11397 Pan, M., Liu, Y., Chen, J., Huang, E. A., & Huang, J. X. (2024). A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval. Scientific Reports, 14(1), 31806. https://doi.org/10.1038/s41598-024-82871-0 Pan, M., Xiong, W., Zhou, S., Gao, M., & Chen, J. (2025). LLM-Based Query Expansion with Gaussian Kernel Semantic Enhancement for Dense Retrieval. Electronics, 14(9), 1744. https://doi.org/10.3390/electronics14091744 Patel, V., Hiran, D., & Dangarwala, K. (2024). Recent Trends of Information Retrieval System: Review Based on IR Models and Applications. In V. K. Gunjan & J. M. Zurada (Eds.), Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (Vol. 873, pp. 619–629). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-9442-7_51 Peikos, G., & Pasi, G. (2024). A systematic review of multidimensional relevance estimation in information retrieval. WIREs Data Mining and Knowledge Discovery, 14(5), e1541. https://doi.org/10.1002/widm.1541 Raj, G. D., Mukherjee, S., Robin, C. R. R., & Jasmine, R. L. (2025). An Intelligent Feature Concatenation Process-Based Effective Query Expansion for Patent Retrieval Approach Using Optimal Bi-clustering and Enhanced Social Engineering Optimizer. International Journal of Computational Intelligence Systems, 18(1), 259. https://doi.org/10.1007/s44196-025-00963-9 Roberts, K. (2024). Information Retrieval. In H. Xu & D. Demner Fushman (Eds.), Natural Language Processing in Biomedicine (pp. 195–230). Springer International Publishing. https://doi.org/10.1007/978-3-031-55865-8_8 Stathopoulos, E. A., Karageorgiadis, A. I., Kokkalas, A., Diplaris, S., Vrochidis, S., & Kompatsiaris, I. (2023). A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics? Computers, 12(6), 119. https://doi.org/10.3390/computers12060119 Venkatachalam, C., & Venkatachalam, S. (2023). Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics. In Review. https://doi.org/10.21203/rs.3.rs-2545685/v1 Vishwakarma, D., & Kumar, S. (2025). Fine-Tuned BERT Algorithm-Based Automatic Query Expansion for Enhancing Document Retrieval System. Cognitive Computation, 17(1), 23. https://doi.org/10.1007/s12559-024-10354-5 Vladlenov, D. (2023). MODERN METHODS OF APPLYING SCIENTIFIC THEORIES. Proceedings of the X International Scientific and Practical Conference, 1–481. https://doi.org/10.46299/ISG.P.2023.1.10 Wang, L., Yang, N., & Wei, F. (2023). Query2doc: Query Expansion with Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 9414–9423. https://doi.org/10.18653/v1/2023.emnlp-main.585 Wang, Z., & Pei, Q. (2024). Dense Retrieval Systems with LLM-Based Query Expansion. 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 682–686. https://doi.org/10.1109/WI-IAT62293.2024.00110 Ye, F., Fang, M., Li, S., & Yilmaz, E. (2023). Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting. Findings of the Association for Computational Linguistics: EMNLP 2023, 5985–6006. https://doi.org/10.18653/v1/2023.findings-emnlp.398 Yıldız, M., & Karakuş, T. (2024). Bibliometric Analysis in Scientific Research Using R: A Review of Scopus and Web of Science Databases. Journal of Data Applications, 0(2), 31–46. https://doi.org/10.26650/JODA.1462396 Zahhar, S., Mellouli, N., & Rodrigues, C. (2025). Leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval. In M. Barhamgi, H. Wang, X. Wang, E. Aïmeur, M. Mrissa, B. Chikhaoui, K. Boukadi, R. Grati, & Z. Maamar (Eds.), Web Information Systems Engineering – WISE 2024 PhD Symposium, Demos and Workshops (Vol. 15463, pp. 101–110). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-1483-7_8 Zhang, L., Wu, Y., Yang, Q., & Nie, J.-Y. (2024). Exploring the Best Practices of Query Expansion with Large Language Models (arXiv:2401.06311). arXiv. https://doi.org/10.48550/arXiv.2401.06311
Rider Payroll Information System at Mini Station Ninja Xpress Sidoarjo Rizal, Arif Muhammad; Junaedi, Lukman
Journal of Science Technology (JoSTec) Vol. 8 No. 1 (2026): Journal of Science Technology (JoSTec)
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/jostec.v8i1.1849

Abstract

Information system products are needed in the current era of technological development. The existence of an information system can replace manual calculation systems that have been used for many years, one example being the payroll information system. Although payroll systems have been implemented in several large companies in Indonesia, PT. Andiarta Muzizat (Ninja Xpress), which operates in package delivery services, has not fully implemented a payroll information system, especially for riders (couriers). Therefore, this study aims to develop a payroll information system that can calculate rider salaries quickly and in detail. The system is designed to minimize discrepancies in salary calculations and provide transparent payroll information. This research was conducted at Mini Station Ninja Xpress Sidoarjo. The system development uses the Extreme Programming (XP) method, which includes planning, design, coding, and testing stages. The result of this research is a web-based payroll information system built using PHP and MySQL that is capable of calculating rider income, including gross salary, bonuses, and net salary received each month. The implementation of this system is expected to improve accuracy, efficiency, and transparency in payroll processes and reduce disputes related to salary discrepancies among riders.
Contextual Data Fusion and Explainable Analytics for Supporting Strategic Decision Making in Smart Information Systems Environments Priyo Wibowo; Rudolf Sinaga
International Journal of Computer Technology and Science Vol. 1 No. 1 (2024): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i1.357

Abstract

The increasing complexity and heterogeneity of data in Smart Information Systems pose significant challenges for effective decision-making. While data fusion techniques have been widely adopted to integrate multiple data sources, traditional fusion approaches often fail to consider contextual information, resulting in limited interpretability and reduced decision relevance. This study proposes a contextual data fusion approach that integrates heterogeneous data sources with contextual attributes, including temporal, spatial, and operational context, to enhance decision accuracy and robustness. The research employs a computational and experimental methodology involving data preprocessing, context encoding, multi-level data fusion, and performance evaluation. Experimental results demonstrate that the proposed approach outperforms single-source analysis and non-contextual data fusion in terms of accuracy, precision, recall, and F1-score, with only a marginal increase in computational cost. The findings confirm that incorporating context into the data fusion process significantly improves the quality and reliability of analytical outcomes. This study contributes to the development of intelligent and data-driven systems by highlighting the critical role of contextual awareness in supporting transparent and effective decision-making in Smart Information Systems.
Determination of Technopreneurship, Work Motivation, Digital Literacy on the Work Readiness of Information Technology Students Yogi Irdes Putra; Ali Idrus; Firman; Sofyan
Jurnal Penelitian Pendidikan IPA Vol 12 No 3 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i3.14407

Abstract

The rapid advancement of digital technology has created both opportunities and challenges for Information Technology graduates, particularly regarding their work readiness in facing industry demands. This study aims to analyze the determinants of technopreneurship, work motivation, and digital literacy on the work readiness of Information Technology students in private universities in Jambi Province. Using a quantitative survey approach, data were collected from 307 active IT students selected through proportional random sampling. Data were gathered using a Likert-scale questionnaire and analyzed using Structural Equation Modeling (SEM) with Partial Least Squares (PLS) via SmartPLS 4.1.14. The findings reveal that all three independent variables have a positive and significant effect on students' work readiness, with digital literacy emerging as the strongest determinant (β = 0.341, p < 0.05), followed by technopreneurship (β = 0.312, p < 0.05) and work motivation (β = 0.278, p < 0.05). The model's R² value of 0.67 indicates that 67% of the variance in work readiness is explained by the three predictors. These results highlight the critical role of integrating technopreneurship and digital literacy within higher education curricula to enhance graduate employability in the digital economy era. The practical implication emphasizes the need for project-based learning, startup incubators, and adaptive digital training programs. The originality of this research lies in its simultaneous examination of technopreneurship, work motivation, and digital literacy as predictors of IT students' work readiness within an Indonesian regional higher education context, an area rarely explored in prior studies
CONSUMER EXPERIENCE WITH TEMPEH LABEL INFORMATION: A LONGITUDINAL STUDY OF NON-GMO VS. UNLABELED PRODUCT CHARACTERISTICS AND TECHNOLOGICAL IMPLICATIONS Sobiyanto; Wiratama, Jansen; Putra, Heru Soetanto; Fitrianto, Adreas Sigit
Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Vol. 9 No. 2 (2025): Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan (IN PRESS)
Publisher : Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/5vs3x826

Abstract

Kemasan produk pangan berperan sebagai sumber informasi utama, menyajikan detail esensial seperti status Organisme Termodifikasi Genetik (GMO), harga, dan tanggal kedaluwarsa, yang secara signifikan memengaruhi pengalaman dan kepercayaan konsumen. Penelitian ini bertujuan menyelidiki korelasi antara informasi yang tersedia pada kemasan pangan dengan atribut produk yang diobservasi konsumen, dengan membandingkan tempe berlabel non-GMO dan tidak berlabel (diasumsikan GMO). Menggunakan metodologi studi kasus observasional longitudinal kualitatif, seorang peneliti sekaligus konsumen melakukan evaluasi harian sistematis selama 120 hari. Data yang dikumpulkan mencakup informasi label, harga beli, dan karakteristik produk yang dialami (masa simpan aktual, perubahan tekstur seperti kelembapan dan kekencangan, serta profil aroma) pada kondisi penyimpanan rumah tangga yang konsisten. Hasil utama memperlihatkan tempe tidak berlabel (harga lebih rendah) memiliki masa simpan aktual lebih lama dan integritas tekstur lebih superior dibandingkan tempe non-GMO premium. Tempe non- GMO mengalami degradasi tekstur lebih awal (lembab), meskipun kedua produk umumnya sesuai dengan tanggal kedaluwarsa masing-masing yang berbeda. Penandaan "non-GMO" dan harga premiumnya menciptakan ekspektasi konsumen terhadap durabilitas produk secara keseluruhan yang tidak sepenuhnya terpenuhi oleh kinerja aktual tempe non-GMO. Penelitian ini menyimpulkan bahwa informasi pada kemasan saat ini, meski menyajikan detail spesifik, mungkin belum memadai mengomunikasikan keseluruhan nuansa kinerja produk akibat teknologi produksi pangan yang beragam, yang berpotensi memengaruhi kepercayaan konsumen. Sistem informasi yang lebih canggih (misalnya, pelabelan cerdas, platform transparansi) berpotensi meningkatkan penyampaian informasi, mengelola ekspektasi konsumen lebih efektif, dan memperkaya pengalaman konsumen dengan menjembatani kesenjangan antara informasi yang disajikan dan atribut produk aktual.

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