Reviewerly/exHarmony huggingface dataset:
Analisis Perilaku Mahasiswa dalam Menemukan Literatur Rujukan Materi Diskusi Tutorial Online

This research analyzes students' behavior in searching for reference literature in online tutorial discussions, highlighting various approaches used in deepening understanding of learning materials. The results indicate that students actively utilize online tutorials as their primary source to enhance understanding and skills related to the courses they are taking, as well as actively participate in online discussions. However, some students exhibit less active participation, indicating variations in student engagement in online learning. Some factors influencing low participation include differences in learning preferences and time constraints. Therefore, it is recommended to develop clear participation guidelines, provide incentives to active students, and develop more interactive learning methods in online tutorials. Additionally, educational institutions should provide strong technical support to students to ensure active participation. These efforts are expected to enhance the quality of learning and interaction among members of the academic community in online learning.
Analisis Kesantunan pada Penggunaan Campur Kode oleh Mahasiswa Prodi Pendidikan Bahasa Jepang (Kajian Pragmatik)

Politeness is essential in interaction to avoid conflict between the speaker and the hearer. This article describes politeness by students and lecturers in academic interaction at the Japanese Program in Undiksha. This study is qualitatively approached using pragmatics design. The data were collected using the technique of observation by listening, writing notes, and participating. The data was then, analyzed using an interactive model from Miles &amp; Huberman. The data were classi?ed according to the face theory of Brown &amp; Levinson i.e. strategy of positive politeness and ?rategy of negative politeness. Data analysis reveals positive politeness strategies and negative politeness strategies students use in academic interaction at the Japanese Program in Undiksha. Both methods contained a constituent such as code-mixing. The reason for using code mixing in speech between lecturer and students is not only from speech situation but most part because of politeness factor. Using code-mixing of the Indonesian language and Japanese, made the speaker comfortable speaking because the code-mixing can make the utterance still in polite mode and formal form.
THE POWERING OF GUESSING GAME IN STUDENTS' WRITING DESCRIPTIVE TEXT: EFL CLASSROOM ACTIVITIE

Enhancing writing skills in descriptive text learning by using the guessing method in class VII SMPN 1 Sugio. The benefit of this research is the guessing game method can improve the writing ability of class VII students of SMPN 1 Sugio, this type of research is Class Action Research which is carried out in cycle stages including action planning, implementation of action, observation and evaluation, and reflection. The subject of this class action research is the seventh grade students of SMPN 1 Sugio. The object of this research is the ability to write with the guessing game method. The instruments used in this study were observation sheets and learning outcomes tests. The data from this study were analyzed descriptive qualitative. The results show that the learning activities of grade VII D SMPN 1 Sugio students who used the guessing method are more active and enthusiastic in learning. In the preliminary results, the average student score is 67.1, only one student managed to reach the KKM. In the first cycle of student learning activities with the criteria of failure, with the results achieved 68.1 of 26 students, students who reached the KKM, 6 students while the second cycle of student learning activities with very good criteria, with the results achieved 76.3 of 26 students. Students who managed to reach the KKM 23 students. Based on the results of the study, it can be concluded that the learning outcomes of seventh grade students of SMPN 1 Sugio using the guessing method have increased.
Influence of School Administrators’ Transformational Leadership on the Innovation Ability of University Teachers

With the development of higher education, the improvement of teachers' innovation ability has become an important issue. This article aims to explore the impact of transformational leadership of school administrators on the innovative ability of college teachers. Through literature review and empirical research, we analyze the difficulties and challenges faced by school administrators in improving teachers' innovation capabilities and propose effective countermeasures. The results show that transformational leadership demonstrates significant value and positive impact in higher education. Its core characteristics include being visionary, inspiring, focusing on individual and team growth, and driving organizational change. Transformational leadership plays a role in stimulating teachers' innovative thinking, promoting organizational changes, creating a positive organizational culture, and establishing effective communication mechanisms, which effectively promotes the improvement of university teachers' innovative capabilities. The composition of teachers' innovative ability involves three aspects: teaching, scientific research and practice, and is affected by multiple factors such as individuals, organizations and society. In order to improve teachers' innovation ability, these factors need to be comprehensively considered and targeted measures and support need to be adopted. There are differences in the perception and identification of transformational leadership among university teachers, which may be related to factors such as personal background, organizational culture and external environment. Strengthening communication and exchanges with teachers, establishing a fair and transparent incentive mechanism and evaluation system, providing sufficient resources and support, and advocating an open and inclusive organizational culture are key measures to enhance perception and recognition. In different subject areas and types of universities, the application of transformational leadership needs to be tailored to local conditions. Develop targeted change strategies based on the different characteristics of science and engineering, humanities and social sciences, and the differences between research-oriented and teaching-oriented universities. Finally, transformational leadership has a positive impact on teachers’ professional development, including promoting professional growth, increasing career satisfaction, and increasing intention to stay. However, during implementation, attention should be paid to possible differences in effects due to differences in context and conditions. Therefore, higher education institutions should strengthen the cultivation and introduction of transformational leadership to help teachers achieve better development and satisfaction at different levels.
Research on the Effectiveness of Metaverse-Based Learning in Business Courses: A Systematic Review

Metaverse-based learning has emerged as an increasingly popular trend in higher education. However, previous studies on the efficiency of business education within the framework of Metaverse-based Learning is insufficient, despite its profound importance in higher education. To address this problem, the PRISMA methodology are employed to examine recent studies conducted over the last five years. The search encompassed two major databases, namely Scopus and Web of Science, supplemented by the inclusion of Google Scholar as a supporting database. A systematic exploration of the literature yielded a total of 14 articles. The review of these articles indicates that the integration of Metaverse technologies in business education offers enhancements in students' learning experiences and practical skills. Furthermore, the majority of students exhibit a positive attitude towards this educational approach. But there still exit some challenges cannot be ignored.
Sounding Sentiments: A Cognitive AI Approach to Speaker-Linked Emotion Analysis

Abstract: Over the past few decades, there has been significant progress in sentiment analysis, primarily focusing on analyzing text. However, the field of sentiment analysis linked to audio remains relatively undeveloped in the scientific community. This study aims to address this gap by introducing sentiment analysis applied to voice transcripts, specifically focusing on distinguishing emotions of individual speakers in conversations. The proposed research article seeks to develop a sentiment analysis system capable of rapidly interacting with multiple users and analyzing the sentiment of each user's audio input. Key components of this approach include speech recognition, Mel-frequency cepstral coefficients (MFCC), dynamic time warping (DTW), sentiment analysis, and speaker recognition.
Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm

Investment is an effort to increase economic growth in Indonesia. A popular investment in the community is gold investment. The value of gold investments tends to increase but is not immune from price fluctuations, therefore it is important to forecast the price of gold in Indonesia. The method that can be used to make this forecast is Support Vector Regression (SVR). SVR is a method that looks for a function that has a deviation of no more than ε to get the target value from all training data. The best SVR model with a linear kernel was obtained from a combination of parameters C=0,0625 and ε=0,001 with a RMSE value of 0,19734 and a value of 0,974112. So, the SVR method is appropriate to use for forecasting gold prices in Indonesia.
Menumbuhkembangkan Keterampilan 4C Melalui Problem Based Learning (PBL) Terintegrasi Role Playing dan Game Quizziz

This research discusses the use of the Problem Based Learning (PBL) learning model which is integrated with the Role Playing method and the Quizziz game platform in improving 4C skills (Critical Thinking, Communication, Collaboration, and Creativity) in 4th grade elementary school students in understanding the rights and obligations of students. Pancasila Education lessons. In the 21st century era of education, the need to develop these essential skills is crucial to prepare students to face an ever-changing environment. PBL, with a focus on solving problems relevant to everyday life, is integrated with role playing and Quizziz games as an innovative solution in learning. The research method used is descriptive qualitative with a focus on an integrated learning approach. The research was conducted at SD Negeri Semarangan 1 involving 25 grade 4 students as research subjects. Data was collected through documentation and observation techniques. The results of the research show that through structured learning steps, such as student orientation towards problems, student organization for learning, individual and group investigation guidance, development and presentation of work results, as well as analysis and evaluation of the problem solving process, 4C skills can grow and develop in student. Students engage in collaboration, communication, creativity, and critical thinking while undergoing these learning stages.&#x0D; &#x0D; Keywords: PBL, role playing, quizziz.&#x0D;
Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis

Dengue Hemorrhagic Fever (DHF) is a disease transmitted through the bite of the Aedes Aegypti mosquito. Limapuluh Kota Regency BPS stated that the morbidity rate due to dengue fever was 14.40% per 100,000 population, this figure jumped high from the previous year with a morbidity rate of 3.30% per 100,000 population. The main symptoms of dengue fever are fever that lasts for 2-7 days, pain felt in the muscles and joints accompanied by a rash or no rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms, ranging from dengue fever, dengue hemorrhagic fever, to dengue shock syndrome. Based on this, there is a need for a classification method that can help and facilitate early diagnosis of dengue fever. The method used is Naive Bayes by classifying dengue positive and dengue negative patients. The aim of this research is to determine the results of the classification of patients suffering from dengue fever, as well as to determine the level of accuracy using the Naive Bayes method. Based on research that has been carried out, the results of the classification of patients are 58 correct and 14 patients classified incorrectly. The accuracy results obtained in this algorithm were quite high, namely 80%, while the sensitivity was 65% and the specificity was 86.5%.
Study of Robot Path Planning with Improved A* And DWA Algorithm Fusion

For some problems of the traditional A* algorithm in robot path planning, such as inefficient search, many and complex inflection points in the designed and realized paths, as well as the inability to complete real-time dynamic path planning for obstacle avoidance, this paper proposes an improved A algorithm and combines it with Floyd for path rounding, and finally with DWA (Dynamic Window Approach) algorithm to be Fusion. In the improved A* algorithm, a carefully normalized random obstacle environment is used and the search process is optimized by reducing the search direction and redundant nodes. Meanwhile, the evaluation function is adjusted to achieve the optimal global design solution, and path optimization and obstacle avoidance are performed by the DWA algorithm. The experimental results finally show that when the robot is in a complex environment, the fusion algorithm can ensure the completion of the globally optimal route while avoiding obstacles in a timely and effective manner, which realizes more efficient path planning.
Impact of Principals' Informatization Leadership Practices to Advancement in a China Vocational School

This study aims to explore teachers’ evaluations of principals’ information leadership and the relationship between evaluation factors. Through questionnaires and face-to-face interviews with teacher respondents, their views and opinions on principals’ information-based leadership practices were collected. First, principals’ information leadership practices were assessed through the International Society for Technology in Education (ISTE) Standards for Educational Leaders, including Equity and Citizenship Advocate, Visionary Planner, Empowering Leader, Systems Designer, and Connected Learning or other aspects. Secondly, statistical analysis methods were used to compare whether there were significant differences in the average scores of different teacher respondents' evaluation of principals' information leadership, and to explore the relationship between evaluation factors. The results show that there are certain differences in teachers' evaluations of principals' information leadership, with factors such as leadership style and vision, IT investment and resource allocation, IT governance practices and policies receiving focus. In addition, the study found that there is a certain correlation between teachers' evaluation of their supervisor's information leadership practices and influencing factors. Finally, based on the research results, recommendations for management development plans are put forward to further improve the school's informatization level and leadership. This study can provide useful reference for school administrators and promote the development and practice of information leadership.
Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province

In DKI Jakarta Province, the number of fires increases and decreases every year. For this reason, efforts need to be made to prevent and reduce the risk of fire. BPBD DKI Jakarta is responsible for this matter. However, for these efforts to be effective, information is needed regarding fire patterns that frequently occur. Fire patterns can be seen using K-Modes categorical clustering analysis. The data used is fire data in DKI Jakarta in 2018. The optimal number of clusters was obtained as 6 clusters based on the Davies Bouldin Index value with the smallest DBI value is 6,22. Of the six clusters, cluster 3 is the cluster with the highest number of fire cases. Cluster 3 has a centroid, namely that fire cases occurred on Friday, November, in Cakung District, due to an electrical short circuit, burning down residential houses and rarely causing minor injuries, serious injuries or deaths.
Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5

Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at the DR. Drs. M. Hatta Brain Hospital Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.
Clustering-based assessment of solar irradiation and temperature attributes for PV power generation site selection: A case of Indonesia’s Java-Bali region

This study presents clustering-based assessments of solar attributes for locating potential solar photovoltaic (PV) power plant sites using k-means and density-based spatial clustering of applications with noise (DBSCAN) by examining the yearly average single-attribute and three-attribute clustering on a dataset of long-term hourly-based direct and diffuse irradiation, ambient temperature, and solar PV power output from 2005 to 2022. Three-attribute clustering enables stakeholders to better understand the characteristics of a cluster by collectively identifying three solar attributes and the magnitude of each attribute in an area or cluster. The presence of this information, which constitutes the clusters, suggests that these attributes have different effects on solar PV output power in different clusters. Although k-means is an effective method for investigating potential locations for PV power plant placements, DBSCAN offers users an alternative method for accomplishing a similar goal. In the case of three-attribute clustering of direct irradiation with k-means and DBSCAN, the 18-year mean value of clusters with the highest yearly average value is achieved at very similar values of 0.305 kW/m2 and 0.310 kW/m2, respectively. It turns out that only six years of direct irradiation had an annual mean value of less than 0.305 kW/m2. This finding implies that in the long run, the solar resources in terms of direct irradiation will typically surpass 0.3 kW/m2/MW installed capacity over all areas suitable for PV power plants. While focusing on the Java-Bali region, Indonesia, the findings, and methods appear to be of broader interest to policymakers, particularly in developing countries where solar PV is considered an option for sustainable energy generation.
Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Preserving a robust online community poses a significant difficulty due to the unrestricted flexibility members have in expressing themselves and behaving. This issue can be remedied through the implementation of user behavior monitoring and analysis, followed by the implementation of suitable actions. The objective of this research is to develop an intelligent dictionary using the genetic algorithm to identify and categorize Twitter posts related to criminal activities by collecting data from tweets. Once the data is preserved, graph analysis techniques are employed to evaluate interactions between users. Next, the user behavior is analyzed using metadata analysis, whereby the chronology associated with each user profile is obtained. Furthermore, the study analyzes the behavioral patterns of users over time. Afterward, a method based on rules is used to create a structure for aspect-based analysis of sentiment. This method assesses the subjectivity of the input text, distinguishing between factual information and personal opinions. Additionally, transformer-based sentiment analysis determines whether the tweet evokes positive or negative sentiment. Furthermore, the task involves constructing a model that can accurately categorize a tweet based on its relevance to criminal activity. Ultimately, the intelligent dictionary is utilized to identify and isolate anomalous behavior by selecting provocative profiles. A Twitter profile exhibiting significant similarity with criminal cases presents a perilous risk to society.
Breast Cancer Classification using XGBoost

Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary classification (normal/abnormal). Artificial intelligence (AI) has been employed to diagnose breast tumors swiftly and accurately at an early stage. This study employs the Extreme Gradient Boosting (XGBoost) machine learning technique for the detection and analysis of breast cancer. XGBoost provides an accuracy of 94.74% and recall of 95.24% on Wisconsin breast cancer Wisconsin (diagnostic) dataset.
The Effectiveness of Discovery Learning Model on Students’ Writing Skills at Vocational High School Diponegoro Losari

This study investigates the impact of the discovery learning model on the writing skills of eleventh-grade students at Vocational High School Diponegoro Losari. The research used a One Group Pretest-Posttest Design and analyzed performance test scores before and after the implementation of the model. The results showed that students with the discovery learning model scored significantly higher in their analytical exposition text writing skills, with an average score of 76.66 compared to 36-45% before the model. The study also found that the use of the discovery learning model significantly improved the students' analytical exposition text writing skills.
Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression

Modern massive data with enormous sample size and tremendous dimensionality are usually impossible to process with a single machine. They are typically stored and processed in a distributed manner. In this paper, we propose a distributed bootstrap simultaneous inference for a high-dimensional quantile regression model using massive data. Meanwhile, a communication-efficient (CE) distributed learning algorithm is developed via the CE surrogate likelihood framework and ADMM procedure, which can handle the non-smoothness of the quantile regression loss and the Lasso penalty. We theoretically prove the convergence of the algorithm and establish a lower bound on the number of communication rounds ιmin that warrant statistical accuracy and efficiency. The distributed bootstrap validity and efficiency are corroborated by an extensive simulation study.