Reviewerly/exHarmony huggingface dataset:
The Effectiveness of the E-Library as a Learning Resource for Elementary School Students' Social Studies in Improving Literacy Skills

The aim of this study is to describe the effectiveness of an e-library as a social study (hereafter, IPS) learning resource for elementary school students in improving their literacy skills. When developing learning materials for social studies, Social Studies (IPS) teachers in elementary schools should have a clear goal and direction, including the use of e-library resources that can enhance students' literacy skills. A descriptive qualitative research approach was utilised to examine the efficacy of the e-library as a learning resource for primary school pupils in the field of social sciences, with a specific focus on enhancing their reading abilities. Data was collected through a literature review and document analysis on social studies learning in elementary schools, literacy skills, and e-libraries. In this investigation, up to 40 research articles were used. The findings indicated that the e-library was effective in helping primary school students study social subjects and develop their literacy skills. In order to optimize the potential of the library, the government or schools should: 1) provide relevant learning resources; 2) establish a 24-hour accessible system; 3) employ a creative librarian; and 4) utilize information technology to target students. This will enable students to access and acquire learning content easily. Additionally, the library should create a space for students to express their ideas freely and channel creativity, making library activities more lively and enjoyable.
Deep Neural Network for Visual Localization of Autonomous Car in ITS Campus Environment

Intelligent Car (I-Car) ITS is an autonomous car prototype where one of the main localization methods is obtained through reading GPS data. However the accuracy of GPS readings is influenced by the availability of the information from GPS satellites, in which it often depends on the conditions of the place at that time, such as weather or atmospheric conditions, signal blockage, and density of a land. In this paper we propose the solution to overcome the unavailability of GPS localization information based on the omnidirectional camera visual data through environmental recognition around the ITS campus using Deep Neural Network. The process of recognition is to take GPS coordinate data to be used as an output reference point when the omnidirectional camera takes images of the surrounding environment. Visual localization trials were carried out in the ITS environment with a total of 200 GPS coordinates, where each GPS coordinate represents one class so that there are 200 classes for classification. Each coordinate/class has 96 training images. This condition is achieved for a vehicle speed of 20 km/h, with an image acquisition speed of 30 fps from the omnidirectional camera. By using AlexNet architecture, the result of visual localization accuracy is 49-54%. The test results were obtained by using a learning rate parameter of 0.00001, data augmentation, and the Drop Out technique to prevent overfitting and improve accuracy stability.
Case Method Model: Strategies for High School Students to Overcome Problems in Learning Poetry

Learning poetry serves a variety of purposes, including fostering fluency in spoken language, exposing students to a variety of teaching methods, exposing students to issues of global significance, and fostering an appreciation for different cultures. The purpose of this study is to identify effective strategies for helping high school students learn poetry using a case-method method. The research is a qualitative study. The data analysis procedure used in this study is an objective hermeneutic model which is divided into two analytical procedural models, namely (1) serial analysis and (2) detailed analysis. The participants were senior high school students from a private Palu high school in the 2022–2023 school year. For this study, thirty youngsters are taking part. First, students have trouble creating poetry, and second, they have trouble understanding poetry, both of which are significant issues in our research. The study's findings include (1) a case method model that helps students overcome their challenges with poetry writing, and (2) a case method model that helps students overcome their issues with poetry reading. Based on these results, it was determined that using a case method model to address the poetry-learning needs of students in class XI of senior high schools is highly effective in preparing them to produce poetry that adheres to established quality standards. Using proper poetry reading strategies, students are also adept at reading poetry accurately and appropriately.
ANALYZING OF STUDENT’S INTEREST IN SPEAKING ENGLISH SKILL IN PESANGGAR, PEGANTENAN, PAMEKASAN

This study aims to determine the level of students' interest in English, especially speaking skills and to find out the reasons why students are not interested in rural areas. The benefit of this research is that students in rural areas can be more aware of how important English is, and keep the spirit of learning even though the school condition is still not sufficient in terms of facilities and infrastructure. The method in this study uses a qualitative method which in this study uses a questionnaire and interviews to obtain data which in this study involves several tenth grade (X) and eleventh grade (XI) students who have been recommended by the teacher as samples. The results of this study are students in rural areas tend not to be interested in English because there are several factors and weaknesses that exist in schools and in individual students themselves.
BOOSTING SPEAKING SKILL THROUGH GAME ONLINE IN STUDENTS’ MADURA UNIVERSITY

Speaking is complex skilll that express the ideas,opinions,desires,our fellings freely,structured and spontaneously that aim give or get information and knowledge from other people.The speaking process base on experiences that can be obtained through discusiion,reading,obsrvation, and listening.All the students want to speak English fluently, therefore students must learn to boosting their speaking skills. Talking about Speaking boosting, the boosting is divided into several aspects ranging from boosting pronounciation,comprehension, grammar, and vocabbulary. It can be used as a measurement our speaking is good or not..almost all students have method to boosting English speaking skill namely Online game. The studennts was familiar with gadgets rarely have online games, not only one but more online games on one gadget.This researcher have research the student of english department in academic year 2017-2020 with using interview method. The result of the study is can boosting speaking skill but not efeective to be Media learning in English program students Madura University.
Poster: Raising the Temporal Misalignment in Federated Learning

The rapid evolution of public knowledge is the trend of the present era; rendering previously collected data susceptible to obsolescence. The continuously generated new knowledge could further affect the performance of the model trained with previous data, such a phenomenon is called temporal misalignment. A vanilla mitigation approach is to periodically update the model in a centralized learning scheme. However, in a decentralized learning framework like Federated Learning (FL), such a patch requires clients to upload the data, which contradicts FL's intention to protect clients' privacy. Furthermore, considering the stationary defenses in FL, new knowledge could be misjudged and rejected as malicious attacks, which hinders the further update of the model. Yet dynamically adapting defenses requires meticulous fine-tuning and harms the scalability. Thus in this poster, we raise such practical concern and discuss it in the context of FL. We then build a prototype of a GPT2-based FL framework and conduct experiments to demonstrate our perspective. The performance in new knowledge drops by 33.47% compared with the previous data, which justify the FL with defenses strategy can misjudge the new knowledge.
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL incentive mechanisms are typically designed by stimulating a fixed subset of clients based on their data quantity or system resources. Hence, FL is performed only using this subset of clients throughout the entire training process, leading to a biased model because of data heterogeneity. This paper proposes a game-theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high-performance model. Each client responds to the server's monetary incentive by choosing its best participation level, to maximize its profit based on not only the incurred local cost but also its intrinsic value for the global model. To effectively evaluate clients' contribution to the model performance, we derive a new convergence bound which analytically predicts how clients' arbitrary participation levels and their heterogeneous data affect the model performance. By solving a non-convex optimization problem, our analysis reveals that the intrinsic value leads to the interesting possibility of bi-directional payment between the server and clients. Experimental results using real datasets on a hardware prototype demonstrate the superiority of our mechanism in achieving higher model performance for the server as well as higher profits for the clients.
Lightweight Streaming Graph Partitioning by Fully Utilizing Knowledge from Local View

Data partitioning is the most fundamental procedure before parallelizing complex analysis on very big graphs. As a classical NP-complete problem, graph partitioning usually employs offline or online/streaming heuristics to find approximately optimal solutions. However, they are either heavyweight in space and time overheads or suboptimal in quality measured by workload balance and the number of cutting edges across partitions, both of which cannot scale well with the ever-growing demands of quickly analyzing big graphs. This paper thereby proposes a new vertex partitioner for better scalability. It preserves the lightweight advantage of existing streaming heuristics, and more importantly, fully utilizes the knowledge embedded in the local view when streaming a vertex, which significantly improves the quality. We present a sliding window technique to compensate for the additional memory costs caused by knowledge utilization. Also, a parallel technique with dependency detection optimization is designed to further enhance efficiency. Experiments on a spread of real-world datasets validate that our proposals can achieve overall success in terms of partitioning quality, memory consumption, and runtime efficiency.
INTRAGROUP CONFLICT AND COHESION IN INTERACTIVE SPORTS TEAMS

The aims of this study are to: (a) identify the most frequently used conflict management style; (b) determine the level of cohesion; and (c) examine the relationship between conflict and cohesion in interactive sports teams. The study was conducted on a sample of 205 professional sports players from interactive sports teams from Serbia (basketball, handball, water polo, volleyball, and football teams). The data was collected through the Rahim Organizational Conflict Inventory – II (ROCII – II) and the Group Environment Questionnaire (GEQ). In relation to all of the sports considered, the findings revealed high levels of all four dimensions of cohesion (Group Integration – Task, Group Integration – Social, Individual Attractions to the Group – Task, and Individual Attractions to the Group – Social). The study has shown a positive correlation between cohesion and the collaborative conflict management style, and a negative correlation between cohesion and the competing style. Cooperation proved to be the most frequently used conflict management style, and competing proved the least frequently used style in interactive sports teams.
A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce

The popularity of the e-commerce system has increased, especially under the COVID scenario. Consumer product reviews from the past have had a significant impact on influencing consumers' purchasing decisions. Fake reviews—those written by humans and computers that engage in dishonest behavior—are consequently generated to increase product sales. The fake reviews hurt consumers and are dishonest. The goal of this research is to examine and evaluate the performance of various methods for identifying fake reviews. The well-known and widely-used Amazon Review Data (2018) dataset was used for this research. The first 10 product categories on Amazon.com with favorable feedback will be provided in the data section. After that, perform fundamental data preparation procedures such as special character trimming, bag of words, TF-IDF, etc. The models are trained to create a dataset for detecting fake reviews. This research compares the performance of four different models: GPT-2, NBSVM, BiLSTM, and RoBERTa. The hyperparameters of the models are also tuned to find the optimal values. The research concludes that the RoBERTa model performs the best overall, with an accuracy of 97%. GPT-2 has an overall accuracy of 82%, NBSVM has an overall accuracy of 95%, and BiLSTM has an overall accuracy of 92%. The research also calculates the Area Under the Curve (AUC) for each model and finds that RoBERTa has an AUC of 0.9976, NBSVM has an AUC of 0.9888, BiLSTM has an AUC of 0.9753, and GPT-2 has an AUC of 0.9226. It can be observed that the RoBERTa model has the highest AUC value, which is close to 1. Therefore, it can be concluded that this model provides the most accurate prediction for detecting fake reviews, which is the main focus of this research. Doi: 10.28991/HIJ-2023-04-02-08 Full Text: PDF
Text Classification for Analysing Indonesian People's Opinion Sentiment for Covid-19 Vaccination

The purpose of this study is to implement text mining for sentiment analysis of Indonesian public opinion on COVID-19 vaccination on Twitter social media using text classification techniques Support Vector Machine (SVM) and Random Forest. The research begins with crawling data from Twitter from September 2021 to October 2021; data cleansing; text translation into English; data preprocessing using NTLK performed with and without the lemmatization process; sentiment analysis using TextBlob; distribution of training and testing data with the Hold-Out method of 70:30 and 80:20; hyperparameter tuning with GridSearchCV; text classification with SVM and Random Forest; and testing the classification results by calculating Accuracy, Precision, Recall, F-Measure based on confusion matrix. The results show that text classification Random Forest consistently has a higher accuracy rate than SVM with the highest accuracy value of 90,59% and most of the sentiments indicate neutral to the COVID-19 vaccination program.
Sentiment Analysis on WeTV App Reviews on Google Play Store Using NBC and SVM Algorithms

Since the Covid-19 outbreak hit Indonesia, all community activities have become very limited. The government's decision regarding PPKM to reduce the level of Covid-19 cases forced the community to reduce the level of activities outside the home including work. One activity that has recently been popular with the public is watching movies through the online streaming service available on the Google Play Store. Applications with high total downloads and ratings show people's interest in the application. The WeTV online streaming service is an application that has high downloads and ratings on the Google Play Store. This service provides various types of content from various countries. However, the WeTV application also has drawbacks that can be seen in the reviews from users. Based on this, research was conducted on the classification of positive and negative sentiments from WeTV application users. There are two classification algorithms implemented, namely Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM). Apart from classifying positive and negative reviews, this sentiment analysis also aims to compare the performance of the two algorithms. The total data used is 100 data. After conducting sentiment analysis, it was concluded that the SVM classification method was the best classification method in this study with 80.00% accuracy, 80.00% precision, and 80.00% recall.
Implementation of Image Distortion to Design Mockups in Web-Based Mug Sales Application

Varisty Mug Souvenir is a small business entity that provides services to make custom souvenir mugs according to customer requests. Souvenir mugs are the main product produced by Varisty Mug Souvenir. When the COVID-19 pandemic hit, Varisty Mug Souvenir's main shop was forced to close, this resulted in disruption of transactions between customers and owners and Varisty Mug Souvenir's sales decreased. aims to increase its sales and simplify the business transaction process by designing a web-based mug sales application with the feature of creating mug design mockups. The mug design mockup creation feature on the application will help customers visualize and make design decisions when placing an order. The development of a web-based mug sales web application will use the Hypertext Preprocessor (PHP) programming language and use the Laravel framework and MySQL database. The creation of the mug design mockup feature uses ImageMagick technology for the feature of generating mug design mockups that apply image distortion. The use of ImageMagick is done by running the command script on the command-line. In the application, customers can place an order for a custom mug by previewing the mug mockup of the desired design. In addition, the admin part of the application allows the admin to process and store data.&#x0D; &#x0D; Keywords: Product mockup, Image Distortion, ImageMagick, Sales application, Souvenir mug
Grouping of Regencies/Municipalities in Eastern Indonesia in 2021 Based on Socio-Economic Indicators

Differences in social, economic, demographic and resource conditions in each region can cause inequality, so it is necessary to encourage economic development according to the capabilities of the region. The importance of looking at socio-economic indicators that are development targets, especially in Eastern Indonesia (KTI). This study uses two non-hierarchical methods, namely K-Means and K-Medoids. In this study, Principal Component Analysis (PCA) was carried out to produce 3 factors. Determining the number of clusters using internal validity and stability shows that the K-Means method with a number of clusters of 2 produces the most optimal clusters. Cluster 1 consists of 152 regencies/municipalities, while cluster 2 consists of 80 regencies/municipalities. Cluster 1 has above average infrastructure and economic characteristics, while its human quality is still below average.
Implementation of the Levenshtein Distance Algorithm and the Regular Search Expression Method for Detecting Typors in Javascript

Typing is an activity to write an article in printed form that has been assembled by a typewriter. With the rapid development of the times, typewriters were replaced by computers because they were efficient in making writing or text. a text or writing that is easy to understand in conveying information does not have word mistakes that result in unclear information being conveyed. In word processing applications such as Microsoft Office Word, it has the word suggestions and autocorrect word features which are very useful in checking an article where there are word errors in the writing. This research develops a javascript library to detect typo errors for writing wrong words and recommends the right words to change the wrong words. This study uses the Levenshtein Distance Algorithm and the Regular Search Expression method. The results of this study were successfully applied to the word recommendation feature in the library with an accuracy value of 50% and a precision level of 5%.
Implementation of ISO 31000:2018 in Risk Management Activities of Libsys Application Student Interest

This study aims to apply ISO 31000:2018 for risk management activities in the Libsys Interests of Students application. CV XYZ, an information technology consultant in Yogyakarta, developed this application. This application detects students' academic interests, so teachers can easily direct students according to their interests. So far, the company has never carried out risk management activities in the system development process, even though this activity plays a role in managing and mitigating risks so that the application can achieve its goals. Risk management activities start from the stages of communication and consultation, establishing context, risk assessment and treatment, and monitoring and review by ISO 31000:2018 standard. The study's results found the effectiveness of the ISO 31000:2018 standard for managing risk in information systems consulting companies. The risk management activity found 17 risk opportunities from the Student Interests Libsys application, with four high-level, eight medium-risk, and five low-level risk opportunities. This study also provides suggestions for handling risks in the Libsys Interests Students application and documents them to help management manage the risks in this application.
Improvement of KNN Collaborative Filtering Model in User-based Approach on Anime Recommendation System

This research aims to resolve the challenge of finding the list of recommendations that correspond to user preferences. The MyAnimeList dataset is utilized for model evaluation, accessible via Kaggle website. The outcome of this study is the development of a recommendation system based on the preferences of other users (user-based model). The suggested solution employs a collaborative filtering model based on the KNN algorithm and weighted attribute. The dataset consisted of 193,272 user ratings on anime, with the following attributes: username, anime_id, my_score, and my_status. As an extension of the KNN collaborative filtering paradigm, the rating value is weighted based on the user’s status. The determination of the weight is based on the responses of 105 respondents to a questionnaire. my_score and my_status values will be combined and adjusted using MinMaxNormalization in addition to being weighted. This work implemented the KNN algorithm with the following k parameter values: 3, 5, 9, 15, 23, 33, and 45. Variations in parameters are utilized to determine the optimal k value to employ in KNN, which uses the Pearson similarity matrix to calculate user similarity values. The model evaluation indicate that the optimal Mean Absolute Error and Root Mean Square Error values at parameter k = 5 are 0.14726 and 0.19855, respectively. This improved model’s findings further demonstrate that KNN collaborative filtering with an additional weighted parameter can predict ratings with stable and generally low error values for all k values.