Reviewerly/exHarmony huggingface dataset:
Pelatihan media pembelajaran berbasis digital bagi guru di SMPN 1 Rantau Selamat Aceh Timur

Information and communication technology development continues to proliferate, and teachers must continue to learn using various digital-based learning media. Training on using digital media for teachers will encourage teachers to create digital media needed during classroom learning. Community service (PKM) through digital-based learning media training for teachers at SMPN 1 Rantau Selamat, East Aceh. This activity aims to provide training in making digital-based media, such as digital books (e-books) and digital comics. The training participants were 20 teachers representing each subject area. PKM activity methods are (1) Dissemination of digital media training, (2) Preparation of technology devices, (3) Digital Media Training, and (4) Training Monitoring and Evaluation. As a result of this training activity, participating teachers at SMPN 1 Rantau Selamat have 20 digital-based learning media products, namely e-books or digital comics, for each subject. The teacher will later implement this digital media in learning activities in the odd semester of the 2022/2023 school year. This PKM program increases teacher competence in developing digital-based learning media.
The Classification of Breast Cancer: The Effect of Hyperparameter Optimisation Towards the Efficacy of Feature-Based Transfer Learning Pipeline

Breast cancer is the primary cause of death amongst women around the globe. The detection of such cancer at an early stage could allow patients to receive medical attention, which could increase their survival chances. This study attempts to investigate the effect of hyperparameter optimisation towards the classification efficacy of breast cancer. A total of 1080 histopathological images of benign and malignant breast tumours were split into the 70:15:15 ratio for training, testing and validation. The images are evaluated by two feature-based transfer learning pipelines, i.e., MobileNet-VanillaSVM and MobileNet-OptimisedSVM, respectively. It was demonstrated through this study that optimising the hyperparameters of the SVM model does increase the ability of the pipeline to discern the classes of the tumour.
Permanent training as a predictor of success in the digital competence of Education teachers carrying out the online tutorial action

The self-perception in digital competence of teachers has been studied in depth in recent years. However, these studies have rarely focused on the process of tutorial action and how to perform this action digitally between the tutor and the students, the teachers and the family. The main objective is to analyze the incidence of continuing education as a mandatory requirement for self-perception of teachers regarding their digital competence. For this purpose, an ex post facto design was used with a sample of 1069 pre-school and primary school teachers from all over Spain. The results showed that teachers who had previously received training in educational technology had a higher level of competence than those who had not been trained. In addition, the gender of the teachers was not a significant predictor of their level of digital competence in communicating with other teachers or with families, but it was found to be a significant predictor of their level of competence with their students. Finally, the use of digital resources such as Twitter and blogs increased this competence for teachers who already had previous experience, while for those who did not have this experience, the most significant resources were blogs and WhatsApp. La autopercepción en competencia digital del profesorado ha sido estudiada en profundidad en los últimos años. No obstante, ello no ha sido apenas focalizado en el proceso de acción tutorial y como realizar esta acción digitalmente el tutor con el alumnado, con el profesorado, y con la familia. El objetivo principal es analizar la incidencia de la formación permanente como requisito obligatorio en el nivel de autopercepción del profesorado respecto a su competencia digital. Para ello, se utilizó un diseño ex post facto con una muestra de 1069 profesores de la etapa de Educación Infantil y Educación Primaria de todo el territorio español. LOS RESULTADOS evidenciaron que el profesorado que había adquirido previamente formación en tecnología educativa tenía un mayor nivel competencial frente a los que no habían sido formados. Además, el género del profesorado no resultó ser un predictor significativo en su nivel de competencia digital para comunicarse con el resto de profesorado ni con las familias, pero sí con su alumnado. Por último, el uso de recursos digitales como Twitter y blogs inciden en un aumento de esta competencia para el profesorado que ya poseía experiencia previa, mientras que para los que no poseían esta experiencia los recursos más significativos fueron los blogs y WhatsApp.
CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media

&lt;p&gt;Text processing is an important task in various machine learning applications. One among the applications is Sentiment analysis. However, the presence of sarcasm makes it difficult for analyzing the sentiment of the statement. In the current scenario, the amount of sarcastic statements in any social media platform is high taking the forms of memes, comments, trolls etc. Hence it is important to identify sarcasm to preserve the polarity of any given statement. Sarcasm usually means the opposite of what the sentence seems to convey. While the existing works in literature have focused on detecting sarcasm, the proposed model, in addition to that, determines the levels of sarcasm present in the text, which will aid in finding the level of harshness present in the statement. In this work, an unsupervised learning model, Conditional Random Field model based Modified Expectation Maximization (CRF-MEM) algorithm has been proposed for detecting sarcasm in tweets. The proposed model aims to overcome the limitation present in the traditional EM algorithm, the random assignment factor, with the proposed aspect relationship value. Experimental results showed that the proposed CRF-MEM achieved an accuracy of 91.89% whereas the traditional EM displayed an accuracy of 80% in detecting sarcasm from text.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Siamese network in face verification online learners

Online training is constantly developing in the world as well as in Vietnam with many benefits to meet training needs in the new global context. In addition to the advantages of this form of training, studies also point out any concerns that need to be resolved to improve the quality of training. Authenticating online learners is one of the problems that are interesting and deployed as an integrated application into the online training system. The article proposes a face verification method based on Siamese network architecture that combines the Facenet model with limited resources and is integrated into an online training system. The proposed approach tends to achieve relatively high accuracy while using a small data set and simple architecture by directly learning the similarity/difference of face pairs.
Learning Disentangled Representations and Temporal-Correlation Dynamics for Robotic Anomaly Diagnosis

Anomalous diagnosis is valuable for reducing potential damages in long-term autonomy robot manipulation tasks, especially in Human-robot collaboration scenarios. Deep learning-based methods have been widely investigated for robot anomaly diagnosis, which can effectively encode complex dynamics from multi-modal sensory data. However, the lacking of enough anomalous samples and the fusion of high-dimensional and modality correlation as well as time-dependent is still a challenging problem. In this paper, a novel framework is introduced to generate synthetic anomaly samples for data augmentation by learning the disentangled representation with sequential disentangled variational autoencoder (sDVAE), and a temporal-correlation VAE (tcVAE) model for robot anomaly diagnosis by learning the temporal correlation features of multimodal anomalies. To evaluate the proposed methods, 115 original anomalous samples from 7 representative anomalies that are first recorded on a self-developed human-robot kitting task. Results indicate that the proposed methods show the best performance of the highest precision (97%), f1-score (95%), and accuracy (93%) with synthetic samples across all baseline methods.
An Error Analysis on the Use of Simple Past Tense in Students’ Recount Text Writing

This research was conducted based on the phenomenon happened in the school. The students had low ability in grammar. They got difficulty in constructing sentences by using some tenses. Therefore, this thesis discussed the error analysis on using simple past tense in writing recount text. The purpose of this research was to classify the types of errors made by the students and find out the sources of errors which students made on using past tense in writing recount text. This research was conducted based on descriptive qualitative analysis. The subjects of the research were the students at the first semester of SMA Mardi Yuana Depok Class X Social III which consisted of 20 students, it was considered by using purposive sampling technique. In collecting the data, this research used writing task to identify the students‟ errors on using action verb in writing recount text based on linguistic category taxonomy and sources of errors according to Brown’s theory. The result of the research showed that there were 4 types of errors made by the students. The highest two common errors were Misinformation errors 30 or 37,5% and Omission errors 24 or 30 %. The lowest two errors were Addition errors that were 14 items or 17,5% and Mis-ordering with 12 items or 15%. Based on the total result types of errors, this research found that Interlingual Transfer was the major source of errors made by the students.
THE PREDICTION OF PPA AND KIP-KULIAH SCHOLARSHIP RECIPIENTS USING NAIVE BAYES ALGORITHM

The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.
QUALITY ANALYSIS OF THE LAPKIN WEBSITE SYSTEM USING THE WEBQUAL 4.0 METHOD AT DEPARTMENT OF THE ENVIRONMENT AND CLEANING PALEMBANG CITY

The LAPKIN website is one of the websites in the form of the Republic of Indonesia's State Civil Apparatus (ASN) Annual Performance Report. The LAPKIN website itself is intended for Central Government Agencies and Regional Agencies. The functions or advantages are to facilitate ASN in submitting the Annual Performance Reports carried out by each agency, and to be able to view agency profiles, organizational structures, work units, job titles, work performance, absenteeism permits, comparison charts of annual assessments and employee status . With the construction of the LAPKIN website, it is hoped that it can assist the tasks of central/regional agencies in carrying out employee performance reporting. In this study it is important to know the quality of the system from the LAPKIN website (Performance Report) at the Palembang City Environment and Hygiene Service, therefore this study aims to analyze the quality of the LAPKIN website system using the WebQual 4.0 method which consists of 3 variables, namely usability, quality information, and the quality of service interactions. The results of this study can provide recommendations to the Environmental and Hygiene Agency of Palembang City to improve the level of website quality in order to achieve maximum utilization.
TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE

The PeduliLindungi application is an application launched by the government during the COVID-19 pandemic, with the aim of helping government agencies carry out digital tracking to monitor the public, as an effort to prevent the spread of the Corona virus. Many people express their opinions on the PeduliLindung application on social media, one of which is through Twitter. To improve the performance of the application, of course, need input or complaints from users, opinions from the public on Twitter about the PeduliLindungi application can be input to improve or improve the performance of the application. Sentiment analysis is carried out to see how the public's sentiment towards the PeduliLindung application is, and these sentiments will be categorized into positive sentiment and negative sentiment, this sentiment can later be used as evaluation material for application development. This study aims to see and compare the accuracy of two classification methods, Naïve Bayes and Support Vector Machine in the classification process of sentiment analysis. The data used are 4636 tweets with the keyword " PeduliLindungi". The data obtained then goes to the pre-processing stage before going to the classification stage. The results obtained after classifying using the Naïve Bayes method and the Support Vector Machine show that the Support Vector Machine method has a higher accuracy of 91%, while the Naïve Bayes method has an accuracy of 90%.
TWITTER SOCIAL MEDIA SENTIMENT ANALYSIS AGAINST BITCOIN CRYPTOCURRENCY TRENDS USING RAPIDMINER

Cryptocurrency trends, especially Bitcoin, have gained a place in a group of people and there are even countries that already use Bitcoin as a legal transaction tool. The dynamics that occur in this Bitcoin trend make many new users. This lack of understanding of the technology can cast doubt on those who want to get started, so it is necessary to conduct sentiment analysis to increase knowledge of what Bitcoin is and how it works. This study aims to conduct a Sentiment Analysis regarding Bitcoin through Twitter social media, so that their opinion on this technology will be known. The method used is by using Tweet data that has been downloaded on the www.data.world.com website. The data is the result of using the Crawling technique, then sentiment analysis is carried out to classify a tweet into Neutral, Positive, or Negative. The results showed that from the 1998 dataset, 46.69% were classified as Neutral, then Positive, 43.54%, and 9.75% Negative.
Online Fall Detection Using Wrist Devices

More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of elderly people’s movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, exhaustive research is carried out with the low computational FS-1 feature set (maximum, minimum, mean and variance) with various machine learning methods. This work presents an accelerometer-only fall detector streaming data at 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. This work had battery and memory limitations in mind. It also developed a learning version that boosts the fall detector’s performance over time, achieving no single false positives or false negatives over four days.
Characterization of fuzzy order relation by fuzzy cone

In the present paper, fuzzy order relations on a real vector space are characterized by fuzzy cones.It is well-known that there is one-to-one correspondence between order relations, that a real vector space with the order relation is an ordered vector space, and pointed convex cones.We show that there is one-to-one correspondence between fuzzy order relations with some properties, which are fuzzification of the order relations, and fuzzy pointed convex cones, which are fuzzification of the pointed convex cones.
Vision-based Monitoring of Student Attentiveness in an E-Learning Environment

Due to the global spread of COVID-19, the world's educational institutions had been ordered to close. As a direct result of this, the time-tested method of acquiring knowledge by visiting classes is gradually being replaced by online education. In virtual classrooms, teachers had difficulty detecting student postures and determining whether or not students were comprehending the material. This research suggests using a computationally efficient method based on computer vision and machine learning to determine the attention levels of e-learning students. The method extracts characteristics using HoG and SIFT. Using K-means and PCA, the resulting feature vector is optimized for dimension reduction. The attentiveness is classified using the classifiers Decision Tree, KNN, Random Forest, and SVM. Random Forest yielded the best accuracy at 99.2% with a dataset of 15000 images.
Whistling in the clavichord

Sympathetic string vibration plays an essential role in the clavichord's sound quality and tonal identity. Sympathetic vibration comes from the undamped string segments between the bridge and tuning pins. Under some conditions, a specific note, a whistling tone, stands out of the reverberation halo due to sympathetic vibration. It is hypothesized that this whistling tone comes from resonance between played and sympathetic segments of strings that are coupled through the bridge. Vibratory measurements for three pairs of excited and sympathetic strings are conducted on a copy of a historical instrument built by Hubert in 1784. The influences of bridge mobility and tuning on sympathetic string frequency and damping are studied. The results show a significant increase in vibratory amplitude, frequency veering, and damping increase in the string segments when tuning approaches frequency coincidence. Numerical simulations of a reduced clavichord model corresponding to the experiments are conducted using the modal Udwadia–Kalaba formulation. Simulation gives a more accurate picture of the veering phenomenon. Simulation and experimental results are in good agreement, showing that whistling in the clavichord comes from string resonance. It is favored by frequency coincidence between excited and sympathetic string segments and by higher bridge mobility.
Design and creation of an information system for analytical data processing

An information system has been developed that makes it possible to determine the relationship between socio-economic factors of the country's development by means of multiple correlation analysis. Effective analysis of indicators can greatly help managers to make the right decisions that will positively affect the development of the country. The software product is implemented as a website. The following technologies and frameworks were used in the development process: Java, Java EE, Spring framework, Spring data, Spring security, MySQL, Maven, Git, jUnit, Mockito, Liquibase, Tomcat, JSP.
THE CHALLENGE OF LEARNING TOURISM ONLINE DURING A PANDEMIC: STUDENTS’ VIEWPOINT

The impact of the COVID-19 outbreak has caused every college and school to stop offline learning activities. For one year, online tourism learning has become a learning option, as well as a new challenge for students and lecturers. This study aims to find out the problems and challenges faced by students during online tourism learning during the pandemic. Respondents from this study were 67 students who took tourism courses. The results showed that students experienced a number of difficulties during online learning such as signal difficulties, lack of motivation to study, difficulty concentrating, and difficulties in practicing tour guiding online. The positive sides of online learning include; unlimited distance, prevent the transmission of the covid-19 virus, no need to meet in person. Overall, students prefer offline tourism learning compared to online learning due to the difficulties they face during the implementation of online learning. It is hoped that lecturers can convey interesting methods and techniques in online tourism teaching to boost student motivation and performance to study tourism in this pandemic situation.
Using Virtual Reality Technology in Biology Education

The use of virtual reality (VR) as a medium for education can contribute to the learning efficiency of students. This study aimed to assess the effectiveness of VR application in advanced biology courses, specifically in enhancing the comprehension and understanding of high school students toward the topic of human organs and other related systems. Four high school teachers and 138 high school students selected from three separate classes participated in this study. To determine the impact of VR education from both teachers’ and students’ perspectives, learning satisfaction and the effectiveness of instructional material were assessed with questionnaires. We found that from teachers’ perspective, VR was an efficient teaching tool that enhanced students’ attention and contributed to the improvement of learning outcomes. From the students’ perspective, they were willing to use VR instructional material and were satisfied with this learning method. Applying VR technology in the classroom should be encouraged. However, some students identified dizziness as a concern when VR glasses were used for longer periods of time. Therefore, we suggest that VR glasses be limited to 30 minutes of use at a time.