This paper proposes Blockchain-Enhanced Multi-Factor Authentication (BEMFA) to address the limitations of existing authentication mechanisms in the Industrial Internet of Things (IIoT). BEMFA combines multi-factor authentication (MFA) with blockchain technology to ensure robust, scalable, and tamper-resistant security tailored to IIoT environments. This method dynamically manages roles and permissions, detects malicious devices, and ensures data integrity and authenticity. Our results demonstrate that BEMFA significantly enhances security, addressing critical access control challenges and mitigating risks posed by malicious devices while maintaining data integrity.

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.

This research presents an in-depth analysis of various types of English syllables, including closed, open, vowel-consonant-e, vowel team, r-controlled, and consonant-le syllables. Each type of syllable has unique characteristics that contribute to the structure and pronunciation of words in English. Closed syllables produce short vowel sounds due to the ending consonant, while open syllables result in long vowel sounds due to the ending vowel. Vowel-consonant-e syllables involve a vowel followed by a consonant and a silent 'e,' which lengthens the vowel sound. Vowel team syllables consist of two vowels that together produce a single vowel sound, while r-controlled syllables involve a vowel followed by the letter 'r,' which alters the vowel sound. Consonant-le syllables, typically found at the end of words, provide a distinctive rhythm and flow. This study demonstrates that a theoretical understanding of syllable types has significant implications for phonics instruction and language education. Phonics instruction based on syllable patterns can enhance reading fluency and spelling proficiency. The analysis also reveals phonological diversity in English, reflecting the language's adaptability and complexity. Understanding syllable structures also provides insights into how the brain processes language, which is crucial for developing effective language teaching strategies.

Penelitian ini menganalisis kinerja laporan keuangan PT Bank BNI, yang berdasarkan laporan keuangan periode 2018-2020. Tujuan dari penelitian ini adalah untuk mengetahui kinerja keuangan PT Bank BNI berdasarkan rasio likuiditas, solvabilitas, dan profitabilitas. Subjek penelitian ini adalah PT Bank BNI dan objek penelitian ini adalah laporan keuangan PT Bank BNI periode 2018-2020. Jenis data yang digunakan adalah data sekunder dan sumber data didapatkan dari website resmi Bursa Efek Indonesia. Teknik analisis data yang digunakan dalam penelitian ini adalah analisis deskriptif kuantitatif melalui tabel. Hasil analisis menunjukkan bahwa rasio likuiditas BNI, seperti Quick Ratio dan Loan to Deposit Ratio berada dalam kondisi sehat, meskipun terdapat penurunan dalam Quick Ratio. Rasio solvabilitas yang mencakup Primary Ratio dan Risk Assets Ratio menunjukkan kondisi sehat dengan beberapa fluktuasi. Namun, rasio profitabilitas terutama Return on Assets dan Return on Equity menunjukkan penurunan signifikan, terutama pada tahun 2020 yang terindikasi masalah dalam pengelolaan aset dan modal perusahaan. Meskipun BNI menunjukkan stabilitas dalam likuiditas dan solvabilitas, terdapat tantangan signifikan terhadap profitabilitas yang memerlukan perhatian. Saran untuk BNI yaitu memperbaiki pengelolaan aset dan penurunan beban bunga untuk meningkatkan laba bersih. Perbaikan ini penting untuk memastikan kesehatan keuangan investor di masa depan dan kepercayaan terhadap bank.

Penyakit Kardiovaskular (Cardiovascular Diseases) adalah faktor utama kematian global, dengan jumlah korban mencapai 17,9 juta jiwa setiap tahun atau sekitar 32% dari total kematian global (World Health Organization, 2021). Faktor risiko penyakit kardiovaskular diantaranya faktor usia, semakin bertambahnya usia seseorang, maka semakin tinggi risiko terkena penyakit kardiovaskular. Faktor lain yaitu memiliki riwayat penyakit kardiovaskular dalam keluarga, diabetes, tekanan darah tinggi, obesitas (kegemukan), Pola hidup tidak sehat, dan Stres. (Kemenkes RI, 2021). Masalah pada penelitian ini adalah bagaimana mengetahui model K-Nearest Neighbors (KNN) dengan baik melalui perhitungan accuracy, recall, precision, dan f1-score pada klasifikasi penyakit kardiovaskular. Tujuan penelitin ini adalah mengetahui kinerja K-Nearest Neighbors (KNN) dengan baik melalui perhitungan accuracy, recall, precision, dan f1-score pada klasifikasi penyakit kardiovaskular. Metode K-Nearest Neighbor (KNN) adalah salah satu metode klasifikasi yang memanfaatkan pola-pola data yang ada dalam dataset untuk mengklasifikasi kategori atau kelas dari suatu sampel yang belum diketahui. Hasil klasifikasi data pelatihan menunjukkan akurasi sebesar 85.49%, dengan precision 84,43%, recall 87,04%, dan f1-score 85,71%. Melalui uji coba menggunakan KNN, diperoleh hasil dengan akurasi sebesar 91% dan nilai presisi 90%, recall 93%, dan f1-score 92%. Kesimpulan dari penelitian ini adalah metode K-Nearest Neighbor (KNN) memiliki hasil yang baik untuk melakukan klasifikasi pada penyakit kardiovaskular yaitu akurasinya 91%.

FoMO or Fear of Missing Out behavior is a type of behavior that is very widespread these days. The aim of this study is to analyze and dissect the FoMO behavior. In particular, it concerns shoe fashion, which is widespread among students. The existence of different types of faculties spread across universities with the grouping of saintek and soshum certainly has consequences for students. It provides a perspective on a phenomenon. Of course, there are differences between the two large groups. It is this difference that provides an interesting proposition for why this research was conducted. This research uses a qualitative method with a phenomenological approach. The results of the study showed that FoMO behavior has consequences for students buying or consuming goods, especially shoes with brand value. The authenticity of an item also influences students' shopping interest. They prefer to buy original or genuine shoes, which guarantee quality, rather than branded but fake or imitation (KW) shoes. Thus, it can be concluded that there is still relevance between the theory of consumer society by Jean P. Baudrillard and the condition of contemporary students of Jember University.

This research was conducted to determine the effect of brand awareness, brand association, perceived quality on brand loyalty on OPPO Smartphone products in Jaya Ponsel Payakumbuh. This research method uses quantitative methods. The technique used in collecting data in this research is a questionnaire by distributing questionnaires to 96 respondents using OPPO in Jaya Ponsel Payakumbuh, and the data from the respondents' answers will be processed using SPSS. The results of this study indicate that brand awareness has a positive effect on brand loyalty, brand association has a positive effect on brand loyalty, and perceived quality has a positive effect on brand loyalty.