Artikel jurnal ilmiah terus meningkat setiap tahunnya, sering kali mempersulit pembaca dalam menyaring informasi inti secara efisien. Informasi yang kurang efisien membuat pembaca harus membaca ulang artikel sehingga memerlukan banyak waktu. oleh karena itu, dibutuhkan sebuah alat untuk menemukan inti informasi dari artikel jurnal ilmiah secara cepat dan efisien. Untuk mengatasi masalah ini, peringkasan teks secara otomatis diperlukan, khususnya untuk artikel jurnal ilmiah berbahasa indonesia. Penelitian ini mengembangkan sistem peringkasan teks otomatis menggunakan metode Fuzzy C-Means dan Vector Space Model menggunakan pembobotan kalimat TF-IDF (Term Frequency Invers Document Frequency). Evaluasi sistem menggunakan metriks ROUGE-1 dan ROUGE-2. Hasil pengujian menunjukkan bahwa sistem terbaik, pada tingkat kompresi 30% serta menggunakan stemming memberikan hasil terbaik dan seimbang, dengan rata-rata ROUGE-1 Precission 0.5331, Recall 0.5034, F1-Score 0.4975 dan Accuracy 0.5183. Hasil penelitian ini menunjukkan bahwa model dengan stemming lebih disarankan untuk menghasilkan ringkasan yang lebih relevan dan akurat pada tingkat kompresi yang lebih tinggi.
 
Kata kunci: Fuzzy C-Means, Vector Space Model, Peringkasan Teks

This article discusses the linguistic structure of Indonesian with an emphasis on the importance of morphology as one of the main aspects in learning and understanding language. Morphology, as a branch of linguistics, studies the internal structure of words and the process of their formation through affixation, reduplication, and composition. This study aims to analyze the types of morphemes—including free, bound, derivational, and inflectional morphemes—and to understand the morphological process and grammatical classes in Indonesian systematically. Through this study, it is also explained how affixation (prefix, suffix, infix, and confix), reduplication in various forms, and compound word composition play a role in forming meaning. This study highlights the importance of morphology in language teaching, communication, and development, and emphasizes that understanding morphological structure will improve language skills accurately and effectively, both in formal and informal contexts, and strengthen the sense of familiarity and awareness of the richness of Indonesian.

Kemajuan teknologi membuat berita mudah ditemukan pada media online. Jumlah artikel berita yang tersedia semakin banyak dengan teks yang cukup panjang. Hal ini akan menyulitkan pembaca berita dalam mencari inti informasi dari berita sehingga diperlukan ringkasan teks untuk membantu pengguna memahami inti dari suatu teks tanpa perlu membaca seluruhnya. Metode yang digunakan untuk peringkasan teks yaitu Maximum Marginal Relevance (MMR) dengan menggabungkan dua faktor pemilihan, yaitu relevansi dan keragaman.  Sering ditemukan saat ini bahwa judul berita dalam artikel online belum sepenuhnya mewakili isi berita atau disebut clickbait, untuk menghindari judul yang kurang sesuai, pada penelitian ini peringkasan didasarkan pada kata kunci yang dihasilkan dengan metode Latent Dirichlet Allocation (LDA). Hasil uji coba dengan 2500 data artikel berita menghasilkan nilai rata-rata ROUGE-1 terbaik sebesar 0.488 untuk tingkat kompresi 50% dan 0.462 untuk tingkat kompresi 30%. Nilai ROUGE-1 terendah yaitu 0.453 untuk tingkat kompresi 50% dan 0.435 untuk tingkat kompresi 30%. Hasil tersebut menunjukkan bahwa sistem dapat menghasilkan ringkasan yang cukup relevan dengan menggunakan kata kunci yang diekstrak dari konten berita.
 
Advances in technology make news easy to find on online media. The number of news articles available is increasing with a fairly long text. This will make it difficult for news readers to find the core information from the news so that a text summary is needed to help users understand the essence of a text without the need to read it all. The method used for text summarization is Maximum Marginal Relevance (MMR) by combining two selection factors, namely relevance and diversity.  It is often found today that news titles in online articles do not fully represent the content of the news or called clickbait, to avoid inappropriate titles, in this study the summary is based on keywords generated by the Latent Dirichlet Allocation (LDA) method. The test results with 2500 news article data produced the best average ROUGE-1 value of 0.488 for a compression level of 50% and 0.462 for a compression level of 30%. The lowest ROUGE-1 value is 0.453 for a compression level of 50% and 0.435 for a compression level of 30%. These results show that the system can produce quite relevant summaries using keywords extracted from news content.

In the ever-changing digital world, strong security protocols are essential. As a vital line of defence against unwanted access, blockchain uses several verification techniques to boost security. This article investigates the use of blockchain technology to tackle privacy, security, and usability issues. By reducing the dangers associated with conventional centralised systems, blockchain's decentralised and immutable structure offers a secure platform for storing and verifying authentication credentials. This method increases user trust by using smart contracts to guarantee transparent and unchangeable authentication procedures. The suggested blockchain-based method strengthens security and enhances privacy by removing sources of failure and decreasing dependence on outside verification. Furthermore, user-centric design and expedited procedures improve the system's usability by making secure authentication more approachable and less obtrusive. This paper offers a thorough examination of the suggested system, stressing its benefits, possible drawbacks, and directions for future investigation. The results indicate that blockchain technology presents a viable solution to ensure that digital authentication frameworks combine privacy, security, and usability.

The Internet of Things (IoT) phenomenon is centered around linking various devices and objects to the Internet, enabling them to communicate, collect, and exchange data [1]. The IoT needs strong, lightweight, and secure authorization schemes to regulate many devices with varying levels of ability. Quantum-resilient federated Multi-Factor Authentication (QRF-MFA) is a solution presented in this paper to address the above-discussed issues. Featuring quantum-resistant cryptographic protocols, high-speed and low-energy Physically Unclonable Functions (PUFs), decentralized identity management, and optimized communication protocols, QRF-MFA provides a complete solution for secure cross-domain device identification and authentication. This is done by leveraging blockchain technology for immutable and transparent management of identities yet limiting on-chain storage overhead. It also provides secure, lightweight communication well-suited for resource constrained IIoT devices, and it is designed for fog and edge computing environments as well. QRF-MFA eliminates the challenges of current methods by combining security, efficiency, and scalability and delivering a resilient and future-ready solution to secure IIoT authentication.  

This paper explores the Time-Based One-Time Password (TOTP) authentication mechanism enhanced with lightweight cryptographic algorithms, presenting it as an advanced solution to the limitations of traditional OTP systems. There are a lot of applications and systems where this mechanism is applied. For example, bank applications, e-commerce websites, access control system, healthcare system, etc. TOTP generates dynamic, time-sensitive passwords using the current time and a secret key processed through a cryptographic hash function, significantly improving security by reducing vulnerabilities to code reused and interception. The adoption of lightweight algorithms ensures that TOTP can be efficiently implemented on resource-constrained devices, such as those on the Internet of Things (IoT) ecosystem. Despite its benefits, TOTP faces challenges including synchronization issues between client devices and servers, and a trade-off between computational efficiency and security strength. This paper discusses the implications of these challenges and evaluates how TOTP, with appropriate design considerations, can provide a robust, secure, and efficient authentication method suitable for various applications, from digital banking to IoT environments.

The rapid advancement of Industrial Internet of Things (IIoT) technology necessitates robust authentication solutions to ensure security, scalability, and efficiency. This project, titled "Enhancing Security in Industrial IoT: Authentication Solutions Leveraging Blockchain," examines various blockchain-based authentication methods for IIoT and identifies their strengths and weaknesses. Despite the enhanced security and decentralized nature of blockchain, issues such as scalability, high latency, and computational load persist. To address these challenges, we propose the integration of Multi-Factor Authentication (MFA) as a supplementary solution. MFA can distribute the authentication load, enhance flexibility and security, and reduce latency by utilizing quick-to-verify factors. Moreover, MFA ensures high availability and scalable storage and processing through cloud services, seamlessly integrating with existing systems to provide a superior user experience. This comprehensive approach not only mitigates the inherent limitations of blockchain technology in IIoT but also reinforces the overall security framework, ensuring resilient and efficient authentication mechanisms. The results demonstrate significant improvements in system performance and user satisfaction, establishing MFA as a viable enhancement to blockchain-based IIoT security solutions.

With the rapid expansion of the Industrial Internet of Things (IIoT), integrating devices, machines, and systems to optimize operations and enable data-driven decision-making, ensuring robust security measures is essential. While blockchain has shown the potential to upgrade traditional authentication methods in IIoT environments, vulnerabilities persist. This paper introduces two innovative methods to enhance blockchain-based authentication in IIoT: first, integrating AI-driven anomaly and threat detection into the blockchain authentication scheme; second, implementing Ethereum smart contracts for enhanced authentication with a two-factor authentication (2FA) system and GFE algorithms. By combining AI for anomaly detection with decentralized smart contracts and blockchain-based 2FA, and leveraging GFE algorithms to enhance blockchain capabilities, the proposed scheme aims to significantly fortify security measures. This integration offers a resilient defense against evolving threats, ensuring transparency, adaptability, and heightened security in IIoT applications.

The Industrial Internet of Things (IIoT) faces various challenges in ensuring secure communication, authentication, and data integrity due to its distributed nature and evolving threat landscape. To address these issues, this paper proposes the integration of blockchain authentication as a robust solution to enhance security and reliability in IIoT networks. By leveraging Federated Learning with blockchain technology, the proposed solution aims to improve authentication mechanisms by training models across multiple edge devices, increasing fault tolerance, and adaptability while reducing the risk of single points of failure. The use of blockchain technology ensures a tamper-proof and transparent ledger for securely storing authentication data and model updates, enhancing security and integrity in IIoT networks. The results and analysis demonstrate that the integration of Federated Learning and blockchain technology effectively addresses interoperability issues, performance optimization concerns, and security vulnerabilities within IIoT networks, offering a more efficient, secure, and scalable authentication alternative.

The Industrial Internet of Things (IIoT) has revolutionized industrial processes, offering automation and data-driven decision-making. However, this interconnectedness brings new security challenges, especially in crucial infrastructure sectors. Traditional security measures are inadequate, leading to the exploration of innovative solutions. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature. This paper proposes a Hybrid Blockchain-Based Authentication Mechanism for IIoT, combining Delegated Proof of Stake (DPoS) and Elliptic Curve Cryptography (ECC). The hybrid architecture utilizes public and private blockchains to ensure scalability, efficiency, and security. Lightweight consensus algorithms, DPoS, are incorporated to optimize performance, while ECC provides efficient cryptographic techniques suitable for IIoT environments. An interoperable framework facilitates seamless integration with existing infrastructure, ensuring regulatory compliance and compatibility. Decentralized identity management further enhances security and privacy. Results and analysis demonstrate the effectiveness of the proposed solution, positioning hybrid blockchain architecture as the most suitable approach for enhancing security in IIoT environments.