Pelanggaran Maksim Kesantunan Berbahasa dalam Novel Negeri Para Bedebah Karya Tere Liye

Tujuan penelitian ini untuk mendeskripsikan dan menjelaskan pelanggarann maksim kesantunan berbahasa dan mendeskripsikan implikasi tuturan pelanggaran maksim kesantunan berbahasa dalam novel Negeri Para Bedebah karya Tere Liye. Informasi data diperoleh dengan melihat referensi pendapat ahli sebagai acuan. Penelitian ini merupakan penelitian kualitatif metode deskriptif yang mendeskripsikan dan menguraikan hasil dari penelitian yang dilakukan. Berdasarkan penelitian, data yang didapatkan sebanyak 71 data dengan klasifikasi masing-masing, pertama maksim kebijaksanaan sebanyak 21 data, kedua maksim kedermawanan sebanyak 3 data, ketiga maksim pujian sebanyak 7 data, keempat maksim kerendahan hati sebanyak 7 data, kelima maksim kesepakatan sebanyak 23 data, keenam maksim kesimpatisan sebanyak 10 data. Implikasi dari tuturan pelanggaran maksim kesantunan berbahasa diantaranya tersinggung, membingungkan lawan tutur, menimbulkan emosi, menyudutkan lawan tutur, menyia-nyiakan waktu, takut, membuat pembicaraan menjadi menarik, sedih, kecewa, menciptakan humor, mengubah topik pembicaraan, kegagalan informasi, dan menegaskan

Performance comparison and analysis of mobile ad hoc routing

ABSTRACT A mobile ad hoc network (MANET) is a wireless network that uses multi-hop peer-to-peer routing instea

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Artocarpin, a Promising Compound as Whitening Agent and Anti-skin Cancer

In our search for natural products from wood on cosmetics and drugs purposes and on the basis of melanin biosynthesis assay guided fractionation, artocarpin was isolated from wood of Nangka (Artocarpus heterophyllus). To evaluate the potency as a whitening agent of artocarpin and its anti-skin cancer (cytotoxicity effect), the MTT assay was used to evaluate its cytotoxicity on cells and melanin biosynthesis assays was performed to determine its whitening agents potency. The evaluation of cytotoxicity on B16 melanoma cells of Artocarpin resulted the IC50 was 10.3 µM and melanin biosynthesis assay with IC50 6.7 µM with out cytotoxicity. Based on the results, suggested that artocarpin have a potent to be developed as whitening agent and skin cancer drug

The self-efficacy, self-regulation and academic motivation among students

dThe study aims to identify the self- efficacy, self- regulation and academic motivation among students. Research design for this study is case study which used quantitative method. The study involved 140 respondents randomly selected among students Permata Insan in Islamic Science University of Malaysia from Form 1 until Form 4 respondents involved in this study using stratified sampling method which based on their availability to participate. Instruments used are The Self-Efficacy Questionnaire, The Self- Regulation Questionnaire and Academic Motivation Scale. The design of the study uses a survey method. The data were analyzed using descriptive and inferential statistics, independent sample t test and correlation. Research findings shows there is significant relationship between self- efficacy and academic motivation among students, significant relationship between self- regulation and academic motivation among students and significant difference between self-efficacy, self-regulation and academic motivation based on gender among students. Thus, to improve self-esteem, self-regulation and academic motivation among student, there is need of teachers to make diversify in style of teaching when delivering in classroom to ensure delivering are more effective towards students

Unlocking the Potential of XAI for Improved Alzheimer’s Disease Detection and Classification Using a ViT-GRU Model

Alzheimer’s Disease (AD) is a significant cause of dementia worldwide, and its progression from mild to severe affects an individual’s ability to perform daily activities independently. The accurate and early diagnosis of AD is crucial for effective clinical intervention. However, interpreting AD from medical images can be challenging, even for experienced radiologists. Therefore, there is a need for an automatic diagnosis of AD, and researchers have investigated the potential of utilizing Artificial Intelligence (AI) techniques, particularly deep learning models, to address this challenge. This study proposes a framework that combines a Vision Transformer (ViT) and a Gated Recurrent Unit (GRU) to detect AD characteristics from Magnetic Resonance Imaging (MRI) images accurately and reliably. The ViT identifies crucial features from the input image, and the GRU establishes clear correlations between these features. The proposed model overcomes the class imbalance issue in the MRI image dataset and achieves superior accuracy and performance compared to existing methods. The model was trained on the Alzheimer’s MRI Preprocessed Dataset obtained from Kaggle, achieving notable accuracies of 99.53% for 4-class and 99.69% for binary classification. It also demonstrated a high accuracy of 99.26% for 3-class on the AD Neuroimaging Initiative (ADNI) Baseline Database. These results were validated through a thorough 10-fold cross-validation process. Furthermore, Explainable AI (XAI) techniques were incorporated to make the model interpretable and explainable. This allows clinicians to understand the model’s decision-making process and gain insights into the underlying factors driving the AD diagnosis