Reviewerly/exHarmony huggingface dataset:
THE EFFECTIVENESS OF USING THE DORATOON ANIMATION MAKER LEARNING MEDIA IN SEMANTIC COURSES IN INDONESIAN LANGUAGE AND LITERATURE EDUCATION STUDENTS FBS UNIMED

This study discusses the effectiveness of learning Semantics courses using learning media based on Doratoon Animation Maker for students in the Indonesian Language and Literature Education Study Program. The purpose of this study was to see the effectiveness of Doratoon Animation Maker's teaching materials in the utilization of the learning process in Semantics courses. This population is all fourth semester students of the 2022/2023 academic year of the Indonesian Language and Literature Education Study Program. The sample of this research is the fourth semester students of regular class A in the 2022/2023 academic year of the Indonesian language and literature study program. The research instruments used were observation, questionnaires, and special tests. This research uses the Research and Development method. The results of this study, namely before using Doratoon Animation Maker learning media in Semantic learning get an average score of 67, 3 and after using Doratoon Animation Maker based learning media get an average of 85, 5.
HUBUNGAN SELF-EFFICACY DAN EMOTIONAL INTELLIGENCE DENGAN PRESTASI BELAJAR MATEMATIKA SISWA SMA NEGERI 6 BALIKPAPAN

This study aims to examine the relationship between self-efficacy and emotional intelligence with student achievement in mathematics. This type of research is correlation research. The research population is SMA Negeri 6 Balikpapan students for the 2021/2022 academic year. Sampling in class XII IPA was carried out using cluster random sampling technique. The sample in this study amounted to 123 students. Data collection techniques in this study are questionnaires and documentation. The research data collected was then analyzed using the product moment correlation test and multiple correlation analysis. The results of data analysis showed that there was a significant relationship between self-efficacy and student achievement in mathematics, with the correlation coefficient r=0.414 or the level of the relationship was classified as moderate. Furthermore, it was found that there was a significant relationship between emotional intelligence and students' mathematics learning achievement, with a correlation coefficient value of r=0.312 or the level of the relationship was classified as weak. In addition, the results of data analysis also show that together, there is a significant relationship between self-efficacy and emotional intelligence with students' mathematics learning achievement, with a correlation coefficient of r=0.430 or a moderate level of relationship.
Teachers’ Perspectives on the Education of Deaf and Hearing Difficulty Students in Indonesia: Research at SLB-B Negeri Cicendo Bandung

Deaf and hard-of-hearing students are a group of people who need special education, but that has not been fulfilled in the Indonesian education system. This research is field research conducted at SLB-B Negeri Cicendo Bandung, the oldest deaf school in Indonesia, located in Bandung. This research was conducted by observing the activities of students in the classroom and in-depth interviews with their teachers; this was done to initiate discussions about various aspects of their education. Problems with language and speech mastery, teaching methods, students' dialectical relationships with technology, and experiences of stigma are used to bring out their life experiences. The study underscores the need to develop and improve the ability to master spoken and spoken language and academic content development. This will increase the access of deaf students to a wide range of disciplines and allow them to go beyond the basic level of attainment.
Automatic Short Answer Grading Using Paragraph Vectors and Transfer Learning Embeddings

&#x0D; Grading questions is one of the most duties that consume the time and efforts of instructors. Among different questions formats, short answers measure students’ depth of understanding. Several types of research have been done to grade short answers automatically. Recent approaches attempt to solve this problem using semantic similarity and deep learning models. Correspondingly, paragraph embedding models and the transfer learning models have shown promising results in text-similarity tasks considering the context of the text. This study investigates distributional semantics and deep learning models applied to the task of short answers grading by computing the semantic similarity between students submitted answers and a key answer. We analyze the effect of training two different models on the domain-specific corpus. We suggest to train the models on a domain-specific corpus instead of using pre-trained models. In the first experiment, paragraph vectors are trained on a domain-specific corpus and in the second one, transfer learning selected models were fine-tuned on the domain-specific corpus. The best accuracy achieved by fine-tuning the (roberta-large) masked language model on domain-specific corpus is 0.620 for correlation coefficient, and 0.777 for RMSE.&#x0D; &#x0D; &#x0D; We compare the achieved results against baseline models and the results of former studies. We conclude that pre-trained paragraph vectors achieve better semantic similarity than training paragraph vectors on a domain-specific corpus. On the contrary, fine-tuning transfer learning models on a domain-specific corpus improve the performance using the pre-trained masked language models.&#x0D;
Structural Types of Settlement Names Referring to the Natural Environment

In this paper I study the structural types of settlement names referring to the natural environment and highlight what kind of semantic and lexical- morphological models characterize the particular name structures and when and in what proportion they appeared in sources of the Old Hungarian Era. Among the basic name structural types of settlement names referring to the natural environment, more than half of the name corpus is made up by single-component settlement names without a formant (56 %, e. g. Kökényér
From proper names to common nouns Italian ‑ismo/‑ista and Ancient Greek ‑ismós/‑istḗs formations

This study aims to investigate if there is a specific grammar for proper names, in particular in the field of morphology, or more precisely, in nominal derivation. We will concentrate on a class of derived nouns in a language with an open corpus, i.e. Italian (from now on It.), and in a language with a closed corpus, i.e. Ancient Greek (from now on AG). At stake here are the nouns formed from proper names with the suffixes -ismo/‑ista in Italian and respectively with ‑ismós/‑istḗs in AG. In both languages, these suffixes are highly productive (see below Section 3). Furthermore, they combine not only with proper names but also with common nouns, adjectives and other lexical categories.
Multi-granularity active learning based on the three-way decision

The reliance on data and the high cost of data labelling are the main problems facing deep learning today. Active learning aims to make the best model with as few training samples as possible. Previous query strategies for active learning have mainly used the uncertainty and diversity criteria, and have not considered the data distribution's multi-granularity. To extract more valid information from the samples, we use three-way decisions to select uncertain samples and propose a multi-granularity active learning method (MGAL). The model divides the unlabeled samples into three parts: positive, negative and boundary region. Through active iterative training samples, the decision delay of the boundary domain can reduce the decision cost. We validated the model on five UCI datasets and the CIFAR10 dataset. The experimental results show that the cost of three-way decisions is lower than that of two-way decisions. The multi-granularity active learning achieves good classification results, which validates the model. In this case study, the reader can learn about the ideas and methods of the three-way decision theory applied to deep learning.
THE ABANDONMENT OF TEACHING POSITION BY LOCAL L2 TEACHERS IN SECONDARY SCHOOLS: A CASE STUDY OF LOCAL CHINESE TEACHERS IN CAMEROON

In recent years, Cameroon has trained many excellent local Chinese teachers. However, With the increasing number of local Chinese teachers and the dissatisfaction with teaching conditions, the abandonment of teaching position by local Chinese teachers is becoming more and more serious, which leads to the shortage of teachers in Chinese teaching in Cameroon's secondary schools. Based on qualitative research design, this paper found that the main reasons that push local Chinese teachers to quit teaching position include teachers' lack of teaching motivation and interest, the problem of the integration model, low wages, teachers' lack of support from principals, and excessive curriculum burden. After describing the main reasons that increase rapidly the turnover (run off) of local Chinese teachers and the negative impact of this phenomenon on Chinese language teaching, this paper tried to put forward some important suggestions to stimulate and encourage local Chinese teachers to have ardent affection for Chinese language’s teaching, and improve the continuous deepening and healthy development of Chinese language teaching in Cameroon, especially in secondary schools.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0202/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Vote Now Application

Abstract: The "Vote Now" application is an online e-voting system that aims to provide a more efficient and secure way to vote. The system includes multiple layers of verification, including face recognition and finger verification, to ensure the reliability of the device. Additionally, the application includes a location-free voting system that allows voters who are unable to come to the voting location to cast their vote from their hometown. This system eliminates the time-consuming and paper-based process of the traditional manual voting system and provides a more secure and authenticated voting experience. The proposed system is an Android-based application and implements the Haar Cascade algorithm for individual authentication. The "Vote Now" application offers a modern and efficient voting experience while ensuring the security and reliability of the system.
Pengembangan Media Komik Digital pada Materi Siklus Air untuk Meningkatkan Hasil Belajar Siswa Kelas V SD Negeri 5 Pontianak Timur

The purpose of this study was to develop digital comic learning media on water cycle material as an effort to improve student learning outcomes. This research is a Research and Development (R&amp;D) study by applying the ADDIE research stages. Data collection in this study was carried out through questionnaires, interviews, tests, validation, and questionnaire sheets. The subjects in this study involved 27 students for two months, starting from February-March 2023. The research findings show that digital comic products have gone through the stages of making, validation, and limited trials. The results of the material expert validation received a score of 3.30 with the label "feasible" while the media expert validation received a score of 4 with the label "very feasible". The pre-test results obtained a score of 62.03 and the post-test score increased and obtained a score of 72.59. The level of practicality of the use and presentation of digital comics learning water cycle material is 3.52 from students and 30 from class teachers with "very feasible" criteria. Based on the results of the study, it can be concluded that the use of digital comic learning media is effective in improving student learning outcomes.
THE LEVEL OF DISASTER RISK REDUCTION AND MANAGEMENT (DRRM) PROGRAM IMPLEMENTATION AMONG PUBLIC ELEMENTARY SCHOOLS: BASIS FOR A PROPOSED ‘OUR SCHOOL, OUR SAFE ZONE’ PROGRAM

The study aimed to determine the level of Disaster Risk Reduction and Management (RDDM) Program Implementation of the three different districts in Polomolok South Cotabato. A cross-sectional survey was utilized in this study. The respondents were the 40 schools district coordinators of the three districts namely South, West, and East districts of Polomok, South Cotabato. It was found out that the level of Disaster Risk Reduction and Management in Districts 1, 2, and was high in terms of prevention and Migration. In terms of preparedness, the level of implementation of DRRM was high in District 1, moderate in District 3, while low in District 2. In terms of disaster response, the level of implementation of DRRM in Districts 1, 2, and 3 was high. In terms of recovery and rehabilitation, the level of implementation of DRRM was high in Districts 2, 3, while moderate in District 1. A proposed ‘Our School, Our Safe Zone’ program was crafted to address the gaps of the study.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0007/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Sustainable Business Performance for Local Business Development: Evidence from Indonesia Coffee Shops

Drawing on the concept of sustainable business performance, this study proposes a research model to investigate how variables such as digital organizational culture, organizational learning culture, ethical sensitivity, and access to finance contribute to the formation of a business performance model that is expected to contribute to long-term business performance in local industries dominated by coffee shops. The measure of sustainable business performance is the performance of economic, social, and environmental factors. The PLS-SEM approach was utilized to estimate this investigation's sustainable business performance model. The study examined 111 coffee shop owners, managers, and employees, selected based on their present position within the company, using a questionnaire distributed between July and November 2021. Several factors, including the organizational learning culture, the digital organizational culture, ethical awareness, and financial access, significantly influence the long-term financial performance of the coffee shop's business. This study contributes to the existing theory and the existing body of literature on topics such as the importance of ethical awareness, access to finance, digital organizational culture, and organizational learning culture. To improve overall business performance, business owners, managers, and even employees should examine the role that sustainable company performance plays in the development of each human resource's talents.
Tweet Sentiment &amp; Emotion Analysis

Abstract: Tweet Sentiment &amp; Emotion Analysis using Bi-LSTM in RNN. Twitter has developed into a useful medium for sharing ideas, attitudes, and feelings. Applications like opinion mining, market research, and social trend analysis all depend on the sentiment and emotion of tweets. The Bi-LSTM architecture in RNN (Recurrent Neural Networks) is used in this study to present an advanced method for sentiment and emotion analysis on Twitter. By utilising machine learning techniques, the objective is to increase the precision and efficacy of mood and emotion analysis. The study emphasises both conventional text analysis and real-time data analysis. Organisations and governmental bodies may continuously monitor sentiment and emotional patterns on Twitter thanks to real-time analysis, which enables them to react quickly to emerging problems or crises. In a typical text analysis, historical tweet data is examined to learn more about user viewpoints, emotional patterns, and sentiment distributions. The Bi-LSTM architecture is used because it can effectively capture the context and sequential dependencies found in tweets. To ensure consistent analysis, the system gathers real-time tweets and conducts the preprocessing stages. Monitoring sentiment changes, emotional responses, and new trends on Twitter are all made possible by this study. The goal of the research is to improve the precision and efficacy of sentiment and emotion analysis on Twitter by utilising machine learning techniques, real-time data analysis, and standard text analysis. The findings and conclusions will aid in understanding public mood and feelings in the digital age.
Review of Malware and Phishing in the Current and Next Generation of the Internet

Abstract: The advent of Web 2.0 and Web 3.0 technologies has created new opportunities for cybercriminals to launch malware and phishing attacks. This review paper aims to provide an overview of the current state of these types of attacks in Web 2.0 and Web 3.0 environments, as well as the tools and strategies that can be used to prevent them. The paper begins by defining malware and phishing and describing the basic methods used to execute these attacks. It then delves into the specific characteristics of Web 2.0 and Web 3.0 environments that make them vulnerable to these types of attacks. For example, the ability to share information and collaborate in real time in Web 2.0 environments can create opportunities for cybercriminals to exploit trust relationships and launch phishing attacks. In Web 3.0 environments, the use of blockchain and decentralized technologies introduces new challenges for preventing malware and phishing attacks. The paper discusses the the functioning and the procedures which are followed in the event of a malware attack or phishing attack in unison with social engineering and also takes a look at a case study of a real life phishing attack in web 3.0. The paper concludes by outlining some of the key tools and strategies that can be used to prevent malware and phishing attacks in Web 2.0 and Web 3.0 environments. These include technical measures such as anti-malware software, firewalls, and spam filters, as well as user education and training to recognize and avoid phishing attacks. Overall, the paper provides a comprehensive overview of the current state of malware and phishing attacks in Web 2.0 and Web 3.0 environments, as well as the strategies and tools that can be used to prevent them. As the use of these technologies continues to grow, it is essential that individuals and organizations take steps to protect themselves against these types of cyber attacks.
Literasi dan Sistem Pembelajaran Daring Penunjang Kualitas Belajar Mengajar Sekolah Dasar Inklusi

This Community Partnership Program (PKM) answers the problems faced by partner, SDN 2 Inclusion in Bengkala Buleleng Village, Bali. The first problem is the lack of literacy that meets the needs of the Inclusive Elementary School. The second problem there is no online learning system. Moreover, the third, teachers do not have facilities of the literacy and learning system for the inclusive elementary school. The solutions offered to answer the three problems are, first, literacy that supports learning needs. Second, the provision of an integrated website-based learning information system and library-based teaching materials library service. Third, literacy for teachers about the application of learning systems and the manufacture of teaching materials that facilitate the teaching and learning process. The method of implementing this PKM is community participation by developing into a number of activities such as: participatory mentoring, workshops, simulations and substitution of science and technology, creation of learning information systems and library services for integrated web-based teaching materials as well as literacy and socialization processes. The evaluation of this activity uses the CIPP method, namely context, input, process, and product tailored to the needs of literacy and teaching and learning processes. The target of this program is the availability of online learning support facilities, increasing the ability of teaching teachers in online teaching and creating digital learning media, as well as the availability of a Learning System and Teaching Material Library Service.
The Process of Technological Innovation in Islamic Universities: E-Campus Application at Islamic Institute of Bunga Bangsa Cirebon

The use of management information systems in Islamic higher education is one of the efforts that can be made to improve the quality of education. This article aims to describe the innovation of e-campus services used by the Campus of the Islamic Institute of Bunga Bangsa Cirebon to make it easier for all elements of the campus community to access all information and academic services through one door. This research uses a qualitative approach with a descriptive method. The data collection technique is by conducting interviews and collecting several references. The interview was conducted on the subjects studied, namely the Rector, Vice-Chancellor for Curriculum, and IT Operator of the IAI BBC campus, by SWOT analysis to capture information about the mechanism for using the e-campus Information Technology application at IAI BBC. The results of this study describe the e-campus applications used at THE BBC IAI including the services of (1) PPDB; (2) Lectures; (3) Administration; (4) Data management of students and lecturers. The e-campus application is a platform that provides menus for the learning process (academic) and non-academic. The use of e-learning through the IAI Bunga Bangsa Cirebon e-campus is a combination of face-to-face (offline) with online (online) or concluded as blended learning. This learning stands on an information technology infrastructure and can be accessed anytime and anywhere that has open source characteristics for all users.
Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.