Pada penelitian ini, sebuah chatbot untuk penerimaan mahasiswa baru dikembangkan dengan memanfaatkan algoritma Naïve Bayes. Sumber data yang digunakan adalah dialog antara staf penerimaan dan calon mahasiswa, yang didasarkan pada informasi dari brosur penerimaan mahasiswa baru Institut Widya Pratama (d.h. STMIK Widya Pratama) tahun akademik 2024-2025. Data tersebut disusun dalam format JSON, mencakup tiga elemen: struktur pertanyaan, klasifikasi, dan jawaban. Langkah-langkah pra-pemrosesan data meliputi penormalan teks, pengkodean label, vektorisasi, dan word embedding. Algoritma Naïve Bayes dipilih untuk melatih model karena kemampuannya dalam memprediksi respons dari data diskrit. Hasil pengujian menunjukkan bahwa algoritma ini mencapai tingkat akurasi 88%, melampaui kinerja algoritma K-Neighbors-Classifier. Model yang dihasilkan diimplementasikan dalam aplikasi chatbot berbasis desktop yang dijalankan melalui command prompt, menggunakan bahasa pemrograman Python dan beberapa pustaka seperti pickle, string, dan numpy. Aplikasi ini mampu memberikan jawaban atas pertanyaan pengguna dengan mengakses model yang tersimpan dalam file *.pkl. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes efektif dalam memprediksi pertanyaan pengguna, dengan akurasi yang memadai untuk aplikasi chatbot penerimaan mahasiswa baru

Indoor positioning plays an important role in a wide range of applications, such as autonomous mobile robots (AMRs), healthcare smart homes, and industrial automation. Popular positioning methods include received signal strength indicator (RSSI)-based positioning, proximity-based positioning, angle-of-arrival (AoA)-based positioning, and fingerprinting. Each method has specific trade-offs regarding accuracy, cost, and deployment complexity. Recent research has focused on solutions to improve accuracy. These include noise filtering techniques, such as Kalman and particle filters, as well as the use of machine learning to optimize the positioning model. In particular, graph-based positioning methods have demonstrated the ability to improve accuracy by optimizing the spatial relationship between landmarks. In addition, regression-based error estimation helps predict and correct positioning errors, improving system reliability. This paper presents a focused review of state-of-the-art techniques to enhance indoor positioning systems' accuracy. They consist of filtering, hybrid methods, and Artificial Intelligence (AI)-based optimization, offering a practical reference for enhancing system performance in real-world applications. By reinforcing recent developments, this review serves as a foundation for researchers and engineers seeking effective positioning solutions for AMR and smart environments. Looking ahead, hybrid positioning systems integrate multiple technologies. AI and machine learning enhance them. Hybrid positioning systems are expected to offer greater accuracy, flexibility, and scalability. These advancements will be essential in optimizing operations and enhancing user experiences in future automation applications and the Internet of Things(IoT).

This paper presents recent research in the field of human face recognition and detection. Technology development has marked a clear transformation in the implementation of recognition tasks. It is a transition from classical methods to methods with AI and deep learning applications. We have reviewed the latest works in famous journals to evaluate this field. This paper provides a comprehensive taxonomy across detection, identification, and tracking. It offers a comparative analysis of state-of-the-art AI approaches and highlights their strengths, limitations, and practical considerations. We have evaluated the level of compliance, completeness, and methodology of related studies. According to the results of our evaluation, significant improvements and developments have occurred in this field. However, some challenges still need to be focused on, such as improving the efficiency of training models in different environmental conditions. The negative effects of lighting conditions, occlusion of faces, or distortions due to different shooting angles are overcome.

This study examines how the violation of Grice’s cooperative maxims—quantity, quality, relation, and manner—elicits humor in YouTube clips from Steve Harvey’s TV Show. Using a qualitative content analysis approach, this research analyzes social media data from the show’s YouTube channel. The analysis applies a pragmatic identity framework and follows the data analysis techniques outlined by Miles and Huberman (1984). Key findings reveal how different types of maxim violations, influenced by contextual factors, contribute to humor production in a talk show format. This study offers valuable insights into the role of conversational implicature in humor, particularly within contemporary media, and contributes to the broader field of discourse analysis and humor studies. By examining how humor is constructed through maxim flouting, the research highlights its implications for understanding humor in digital media and its cultural significance in global media contexts.

The rapid development of technology has had a significant impact on various aspects of life, one of which is the economic sector. The application of technology in the economic sector involves the use of AI in e-commerce, particularly in the live feature of Shopee, which provides substantial benefits for business actors. However, it can also create imbalances in the market at times. The market imbalance has led to unhealthy business competition practices that are contrary to Law Number 5 of 1999 concerning the Prohibition of Monopolistic Practices and Unfair Business Competition. The purpose of this research is to analyze the utilization of AI in Shopee's live feature, which is indicated to potentially lead to unfair business competition practices based on Competition Law. The method used in this research is normative empirical, combining the implementation of positive law with legal events occurring in society. The utilization of AI in Shopee's live feature causes an imbalance in the market by providing significant advantages to business players who possess multiple AI models, allowing them to dominate up to 50% of the market share for certain products and hindering new competitors from entering the market. The use of AI in Shopee's live feature leads to market dominance and a dominant position by business players, which contradicts Articles 19 and 25 of Law Number 5 of 1999 concerning the Prohibition of Monopoly Practices and Unfair Business Competition.

This study examines the cultural politics affecting Indonesian literature in the global literary arena. It analyses how literary politics, standardisation, implicit agendas, and validation systems impact international reception. The study investigates how global literary standards reshape Indonesian narratives to match international preferences, often at the expense of cultural authenticity. In addition, several works by some prominent Indonesian authors, including Toer, Anwar and Kurniawan are being analysed. Using a descriptive-qualitative approach with discourse analysis, interview and case studies, the research reveals three key findings: translation challenges for Indonesian literary works, limited international publication opportunities, and underrepresentation in global literary awards and criticism. Based on these findings, the study advocates for more inclusive literary practices to enhance Indonesian literature’s global visibility while preserving its cultural integrity.

The advancement of information and communication technology has made privacy boundaries thinner.  Various personal data on E-Commerce websites are increasingly easy to spread, which creates risks at every stage of personal data processing. This is important to be given further attention, given the high number of transaction valuations that occur through E-Commerce websites. This research aims to analyze what forms of risks can occur at each stage of personal data processing on E-Commerce websites. The stages of personal data processing described in this research are the stages of personal data processing as stipulated in Law Number 27 Year 2022 on Personal Data Protection. This research utilizes a library research method by focusing on legal materials so that it can be said to be library based. The results show that there are different risks at each stage of personal data processing on E-Commerce websites, both before the storage of personal data, and after the storage of personal data by the personal data controller.  This can be in the form of data collection that is not in accordance with applicable regulations, lack of transparency of data collection, and data collection that is not specific, unlimited, and illegal, unauthorized access, disclosure in an unauthorized manner, unauthorized modification, misuse, destruction, and loss of personal data on the system. To reduce the impact of risks, things that can be done are to update the system regularly, or increase the capacity of human resources who process personal data.

This study aims to identify the most difficult argument structures and argument roles in sentences identified by English as a Foreign Language (EFL) students, as well as the factors that contribute to such difficulties. The study collected data using a descriptive quantitative method, relying on quantitative descriptive data through statistical analysis. focused on 36 fifth-semester students from Class B of the English Education Study Program at Tadulako University, who were selected through purposive sampling with statistical analysis, tests, and questionnaires. The results of the research showed some key findings: (1) the most difficult argument structure for students to identify is the sentence with two arguments, which appears with a total frequency of 60; (2) the most difficult argument role for students to identify is the experiencer; and (3) five main factors affect students' ability to identify roles and arguments in sentences, namely prior knowledge, interest and motivation, learning strategies, teaching methods, and learning environment. Future researchers can discuss thematic roles that have not been addressed in this study.

Currently, there is growing integration on the use of technology. There should be appropriate tools to enhance vocabulary mastery for the learners, especially English learners. Educandy is recognized as an interactive learning platform to support learners' vocabulary mastery. This study adopted a mixed-method approach to provide a more comprehensive understanding by examining both learning outcomes and students’ responses. Its aim is to provide insights into how Educandy can enhance vocabulary learning among seventh-grade students at MTs Arrobiah Azzain Grinting. 60 students were randomly selected and divided into two groups; experimental and control groups. Educandy has improved vocabulary at MTs Arrobiah Azzain, shown by higher post-test scores and positive student feedback from observations and interviews. This finding confirmed that using the Educandy video game effectively enhanced EFL secondary students’ vocabulary acquisition. The implementation of this interactive tool not only improved students’ vocabulary knowledge but also increased their enthusiasm for learning. The students were able to enjoy learning vocabulary using Educandy video game, which made them enthusiastic in learning. The students could easily remember what they have learned in their class by using Educandy video game. Based on the research findings, it was concluded that using the Educandy video game effectively enhanced vocabulary learning for EFL secondary students. This was evident that there was improvement in students’ vocabulary mastery and their increased enthusiasm for learning.

Penelitian ini bertujuan untuk mengembangkan bahan ajar digital interaktif menggunakan Canva pada materi energi untuk peserta didik tunarungu yang valid, praktis, dan efektif dalam meningkatkan hasil belajar peserta didik. Metode penelitian yang digunakan adalah Research and Development dengan model pengembangan 4D (define, design, develop, dan disseminate). Subjek pada penelitian ini yaitu 5 peserta didik kelas XI Tunarungu di SLB Negeri Semarang pada tahun ajaran 2024/2025. Pengembangan bahan ajar digital interaktif menggunakan Canva ini menyesuaikan kondisi anak tunarungu dengan karakteristik yang didasarkan pada konten visual dan bersifat interaktif. Hasil kevalidan dari bahan ajar diperoleh persentase sebesar 86% dengan kategori valid dan hasil kepraktisan diperoleh persentase sebesar 82% oleh guru dan 79% oleh peserta didik dengan kategori praktis. Keefektifan dari bahan ajar yang dianalisis menggunakan effect size memperoleh nilai sebesar 1,78 dengan kategori very large effect. Sehingga dapat disimpulkan bahwa bahan ajar digital interaktif menggunakan Canva pada materi Energi layak dan praktis untuk digunakan dalam proses pembelajaran oleh guru dan peserta didik tunarungu dan memiliki efek yang sangat besar (very large effect) yang ditimbulkan pada hasil belajar peserta didik tunarungu.