Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Twitter towards the Ratification of a Bill on the Elimination of Sexual Violence in Indonesia using Machine Learning

In Indonesia, incidents of violence against women have developed into a problem that needs attention. The National Commission for Women has recorded an increase in cases of violence during 2019 which was 6 percent compared to the previous year. The number of increases is a concern for the government in designing a countermeasure and prevention action. In 2014, the National Commission on Women initiated the A bill Law on the Elimination of Sexual Violence to follow up on acts of sexual violence in Indonesia. The ratification of a Bill on the Elimination of Sexual Violence is important to suppress cases of sexual violence. The pros and cons that arise regarding that are found in various media, including social media, namely Twitter as a forum for the community in freedom of expression to make a bill until now it has not been ratified. Comments in the form of tweets become a representation of sentiment that can be seen by the public, both positive and negative. This can affect a policy that is made whether it is feasible to apply or not. So that sentiment from the public can be analyzed properly, this study will examine sentiment analysis on Twitter regarding a Bill on the Elimination of Sexual Violence in Indonesia by applying Natural Language Processing (NLP), using the Support Vector Machine (SVM), and Naïve Bayes Classifier (NBC) algorithms with three different scenarios. The purpose of this study was to determine the algorithm with the best performance in classifying categories. From this research, the highest accuracy result for the test data is in scenario 3 with 97% using SVM and 94.50% using NBC. With these results, the model created can classify positive and negative categories in a document properly.
Developing Folklore by Utilizing Augmented Reality which Implements The 3D Pipeline Method

Augmented reality is developing very rapidly and taking part in human life. Research shows that AR technology provides solutions in many domains ranging from education to military training. In related to folklore, unfortunately there are not many experts or user utilizing AR application in folklore. However, one of the most important aspects of AR in folklore applications is creating the right technique for interaction between users and the content of AR virtual applications. Immersive AR characters can become AR as the perfect tool to tell folklore in 3D and bring to life old stories that happened hundreds of years ago with AR back to the present. There are several different methods of developing folklore, but from the results of previous studies it is known that the 3D Pipeline Production method is the most appropriate method for animation development. Studies in the form of Study Literature Review are also presented in this study. The end result is a step-by-step explanation of the development of folklore in the form of AR using 3D Pipeline Production. This research is expected to provide enthusiasm and motivation for AR developers to create folklore projects with AR so that folklore becomes interesting and does not become extinct over time.
Modified Local Updates of the Ant Colony Optimization Algorithm for Image Edge Detection

Edge detection refers to the process of extracting edge information of an image. It is considered as a basic step used in the majority of image processing applications. The aim of this study was to modify local updates of pheromones. Therefore, the convergence of the Ant Colony Optimization (ACO) algorithm applied to image edge detection could be accelerated effectively. Such the algorithm is a metaheuristic method applying the ants as agents with their pheromone updates for an effective and efficient solution of search processes. Five ACO algorithms for edge detection, i.e., ACO, modified ACO, ACO with the Sobel operator, ACO with the Prewitt operator, and ACO with the Isotropic operator were in comparison. Nearly optimal solutions of several image datasets were discovered through examination of the number of ants and iterations. Additionally, calculation results of each image dataset and algorithm were compared. The evidence shows that solutions produced by all algorithms are equally good. For an image dataset with more ants, however, it is found that the modified ACO algorithm has the best solution in terms of time convergence. The study contribution is further next to adding the concept of improving edge detection in the image with the ant colony optimization algorithm. The implementation of the study carried out is to modify local updates which are functionally used for improving the edge detection dealt with by ants taking part in ACO.
Blockchain, Information and Speculation Calculations in Indonesia: Recent Work

Connecting cities further into nearby supply chains worldwide is the basis of Indonesia's provincial finance progress. We analyze Indonesia's budget considerations, allocations, and Blockchain investigations. We argue that we must address the four issues of topographic access, high costs, lack of managing account items, and no education related to money to address budget embezzlement. We propose investigating selection initiation, appropriation design, and outcomes to develop this understanding. The usage of blockchain in the financial sector truly extends beyond simple cryptocurrency exchanges. The usage of blockchain in the financial sector is very broad, the Chief Executive Director of the Payment System Policy Department of Bank Indonesia (BI), who was quoted by daily social. The answers to these devotional questions will offer help to help us keep our distance better, much better, and higher Novelty of this research by improving the performance of how Blockchain-based progress is accepted in the provinces of Indonesia. This hypothetical commitment is evidence of differentiating information that must be maintained so the Indonesian people can realize monetary considerations. With these criteria, the SWOT analysis is the correct method. SWOT analysis is a research method that identifies various factors systematically to formulate corporate strategy.
Goal-Oriented Requirements Analysis of Online Public Complaints System Using SAR, R2G, and KAOS Methods

There are some methods to perform goal-oriented requirements analysis, i.e.: KAOS, GBRAM, i*, etc. The House of Representatives of Republic of Indonesia, as we call DPR RI, is an institution with obligations to accommodate people's aspirations and complaints. DPR RI Online Public Complaints System (PCS) is one of the media that can be used by the public to provide complaints to the DPR RI. Through interviews with experts, the current DPR RI Online PCS (system-as-is) still needs to be developed, so it needs to be performed in-depth requirements analysis of the system. The analysis was performed using the GORE (Goal-Oriented Requirements Engineering), by combining SAR (Select Appropriate Regulations), R2G (Regulations to Goal Model), and KAOS (Keep All Objectives Satisfied) methods. The results of the requirements analysis that have been carried out show that the SAR, R2G, and KAOS methods are proven to bend suitable to analyze the requirements of the DPR RI Online PCS. Furthermore, the analysis shows that system-as-is can only realize 13 system requirements or equal to 81.25% of the total 16 actual system requirements (system-to-be), so there is a gap between system-as-is and system-to-be of 3 system requirements or 18.75%.
LaravelFramework on Child Friendly Integrated Public Space Management Information System

Organized data management can support the reporting system in an organization and facilitate decision making. At the time of the research, the management of the integrated public space data had not yet optimized the use of the database so that data redundancy often occurred and caused ambiguity of the data to be re-managed, the unintegrated data on booking facilities caused a messy schedule, and it was difficult and long in making activity reports. This study aims to design a Child Friendly Integrated Public Space management information system (RPTRA) with the Laravel framework. The system is expected to facilitate the management of integrated public space use data and facilitate the preparation of RPTRA activity reports. This study uses data collection methods with observations, interviews and literature studies, for system development researchers use the Rapid Application Development (RAD) method and the UML Diagram tool to design system requirements. The result of the research is a child-friendly integrated public space management information system with facility booking features, approve booking facilities, and input forms to add daily activities that occur in RPTRA.
Support Vector Machine and Lexicon based Sentiment Analysis on Kartu Prakerja (Indonesia Pre-Employment Cards Government Initiatives)

Machine Learning is a technology that is able to study existing data and perform certain tasks according to what data it learns, either text, video, images, or numerical data using supervised learning and unsupversied learning techniques. The Pre-Employment Cards (Kartu Prakerja) is one of the government programs that aims to provide assistance to the Indonesian people, especially those who do not have a job. Based on data from the Central Statistics Agency for 2020–2021 recipients of the pre-employment program as many as 11.4 million recipients, the pre-employment card program received responses from various communities, whether recipients or not, where these opinions included pro-contra opinions on the Pre-Employment Cards (Kartu Prakerja) program. The purpose of this study is to classify the response (sentiment) of the community by using machine learning on pre-employment cards. Sentiment analysis is used to obtain information in the form of opinions (sentiments) based on textual data to determine the public's view of news, service satisfaction, and government policies. The sentiment analysis process is divided into several stages, namely: crawling, data preprocessing, classification, and data visualization. In this study, preprocessing consists of several stages, namely: cleaning, lemmization, stemming, tokenizing, and stopword removal. The method used in this research is a combination of unsupervised learning: Lexicon Based and supervised learning: Support Vector Machine. Textual data weighting is based on data matching against the normalized Lexicon Sentiment with scaling to determine the positive, neutral, and negative sentiment classes. The results of the classification of 940 tweet data obtained 330 positive tweets (35%), 302 negative tweets (32%), and 308 neutral tweets (33%). From the test results on the classification accuracy with the Support Vector Machine, the results obtained an average accuracy of 98.75%, precision 0.98, recall 0.98, and f-measure 0.98 with the conditions for selecting the Cost value in SVM using the help of 10-fold cross validation.
Effects of IT Service Management on E-Learning Readiness through the HOT-Fit Model

The majority of private universities are prone to encounter gaps and incompletely realize contributions and effects of IT service management on e-learning readiness. So far, the focus of this readiness has been more on online learning processes and mechanisms. Distributing knowledge through electronic media regardless of time and locations, therefore, becomes existing limitation. This study objective was on confirmation and ascertainment of the extent of IT service management in positively influencing e-learning readiness through the HOT-Fit model. The researchers applied a survey method with an explanatory design and a follow-up explanation model. There were questionnaires successfully completed and submitted by 298 respondents who were in association with special schools of informatics skills and computing in West Kalimantan. Likert scales were used to process the whole data collected and the SEM-PLS method was used for analyses. Meanwhile, bootstrapping was applied for data distribution in terms of normality. Finally, a feedback process lasted. Outcomes represent that IT service management is a crucial factor and technology is no longer a factor determining the success of implementing e-learning readiness with very high predictive relevance (99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).
Classification of Bird Species using K-Nearest Neighbor Algorithm

Birds play an essential role in the functioning of the world's ecosystems by directly impacting human health, economy, and food production and benefiting millions of other species. The diversity of bird habitats shows that there are many types of bird species. This study aims to provide a new treasure for the ontology field by applying a machine learning algorithm to classify bird species based on K-Nearest Neighbor (KNN). A total of 400 species of bird images with a total of 58388 images of data were tested in this study. The test scenario was carried out in 3 stages: 400, 200, and 100 species. The results of testing the accuracy of the K-Nearest Neighbor model applied to the bird image dataset are 26.846%, with a value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=1$</tex> . The comparison of training data with test data is 95:5 percent (%). This result shows that the KNN algorithm can classify bird species.
Analysis of Public Sentiment Using The K-Nearest Neighbor (k-NN) Algorithm and Lexicon Based on Indonesian Television Shows on Social Media Twitter

This study aims to implement a combination of the k-Nearest Neighbor (k-NN) and Lexicon Based algorithms in the case of sentiment analysis of public responses about Indonesian television shows uploaded on Twitter with 3 sentiment classes, namely positive, negative and neutral. The chosen method is a combination classification method between k-Nearest Neighbor (k-NN) and Lexicon Based. Before classifying, the pre-processing stage of this study was carried out first including cleaning, case folding, tokenizing, normalization, stopword removal and stemming. Then weighting is carried out by the TF-IDF method. The dataset used amounted to 200 tweets taken from tweet mentions of 4 Indonesian television stations namely, TVRI, RCTI, SCTV and ANTV. The program is designed using the python programming language with the help of the jupyter notebook framework. The scenario is carried out using the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> in the k-NN algorithm of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=1,\ k=3$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=5$</tex> . The best results obtained were <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=3$</tex> values with an accuracy of 74%, an error rate of 26%, a recall of 83.3%, a precision of 80.64% and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f$</tex> -results at a value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=5$</tex> amounting to 90%.
Data Clusterization of Muslim Majority Countries to Find Out the Most Factors Causing Gender Issues Using the K-Means Algorithm

Islam is a religion that upholds the dignity of women. Even Islam teaches that heaven is at the feet of a mother. However, in countries where most of the population is Muslim, gender issues persist. Therefore, this article was created to know the factors that most contribute to gender issues in various countries where most of the population is Muslim. These factors are divided into internal factors and external factors. Internal factors include those that are relevant to oneself. External factors include things other than oneself, such as culture. The author uses the K-Means algorithm as the algorithm used to manage the retrieved data. The author uses a collection of survey data found on the Internet about the reactions of men and women to Islam and gender issues in Muslim-dominated countries.
Heuristic and Webuse Method to Evaluate UI/UX of Faculty Website

Websites at higher education institutions have a strategic role as a medium for delivering practical information and communication. Evaluation of the UI/UX on the website is one way to maintain the effectiveness of the website. This study provides an overview of the UI/UX evaluation on a faculty website at State Islamic University (UIN) Sunan Gunung Djati Bandung. The heuristic evaluation method is used to dig up information and find errors and successes in a website interface design. The Webuse method is used to measure usability about user satisfaction with a website. The respondents of this study were website users, students, and lecturers, totaling 98 respondents. The results of the study indicate that seven aspects of website assessment are lacking. Based on the evaluation of appearance and usability, 17 proposals for improving the website's appearance on desktop and mobile have been made.
Combining PCA and SMOTE for Software Defect Prediction with Visual Analytics Approach

Software defect prediction enables efficient management of time and resources in the form of improving software quality. Therefore, research to improve the performance or accuracy score of the software defect prediction model is still being carried out. However, datasets for SDP often have a large number of attributes and imbalance between the defective and non-defective class samples, which reduces classification performance. In this study, we propose combining PCA with SMOTE with aims to produce models with better performance and visualization approach to represent the model created to help understanding and analysis for modeling in the future. The SVM, RF, NB, and NN classification algorithms which the best parameters are sought, are evaluated based on the Recall, AUC and G-Mean values in five different NASA datasets. The authors then compare the results of the evaluation of the proposed model with the PCA model without SMOTE to find out whether the performance of the model has improved. Visual analytics is successfully built after the model is created for all stages of the model building so it provides confidence, helps users understand and gain insights from the resulting model. The findings indicate that the proposed method outperforms the model using PCA alone on average by 60%, 47%, and 16% for Recall, AUC, and G-Mean scores, respectively. SMOTE is proven to overcome the effect of class imbalance by increasing the g-mean score in all models and NN is the best algorithm based on the average score in the proposed model.
Artspo: Website Design to Improve Digital Artist Creativity

Creative block is a period when the creative worker cannot conduct their creative idea, this problem is one of the most creative workers including digital artists commonly face. Creative block can affect creative workers productivity if not being handled, not only affect creative workers especially digital artist productivity, if prolonged this issue can cause stress and depression. Every creative worker especially digital artist has their own way to solve this issue, but their solution does not always work. There are several ways trying to solve this problem, but the truth is to solve creative blocks there are needed creative workers' contributions like motivation, spirit, mood, and appreciation. By using design thinking methods, this research aims to propose user interface of website to support the digital artist deal with creative block. Artspo have features wherein the web, artist can communicate, give appreciation, motivation, and support each other. This web will also recommend some challenges and keep giving motivation to the artist. Hopefully this web can developed further in order to improve creativity.
Online Intermediary Liability and the Role of Artificial Intelligence in Malaysia

It is widely accepted notion around the world that online intermediaries are immune from civil or criminal liability for hosting illegal or harmful third-party content, provided they comply with legal obligations set out by law through measures such as takedown. Due to advancements on the internet, their roles have expanded from mere intermediaries into publishers at the same time. Consequently, it invites confusion over an intermediary's true status and risking injustice through a ‘one size fits all’ inference. This was the scenario that the Malaysian Federal Court had to address in the MKini case in 2021, which established a new narrative on internet intermediary liability for Malaysia. The objective of this article is to investigate whether a publisher or content creator acting as an intermediary should be held liable when a third party posts illegal or harmful content on their platforms. Through a comparative analysis of the cases of Mkini, Delfi and Bunt we called into question the legal protection provided by the Malaysian Communications and Multimedia Act 1998 and the Content Code, which guaranteed such immunity to their subjects. We propose for the possibility of a publisher utilising artificial intelligence (AI) as an effort to mitigate the liability for illegal third-party publication on its platform.
Forestry english corpus construction and application in foreign language teaching under the background of big data and internet of things

With the development of science and technology, the concept of big data and the Internet of Things is being used in the social life of ordinary people widely more and more. Realizing the efficient use of big data and Internet of Things technology, has become an important means to improve work efficiency and scientific research results. Such a rapid development trend makes each team actively participate in the application of these technologies, hoping to make their work more in line with the trend of the times. Especially for the collection and construction of some databases, it has more advantages than traditional tools. This research makes full use of this technical advantage, selects the abstracts of the core journals of forestry at home and abroad as the corpus source, and builds a small English corpus of forestry. The corpus is used in English teaching in colleges and universities, uses a combination of comparative analysis and sampling surveys, and can play an active role in vocabulary learning and translation practice, semantics, grammar, syntax teaching, thesis writing, etc. Stimulate students’ interest in learning and enhance their independent learning ability and spirit of exploration. The experimental data shows that the English corpus constructed under the background of big data and the Internet of Things improves the average retrieval efficiency of college students by 27.3%, and the comprehensiveness of retrieval items increases by 9.6% on average. These improvements have a very positive effect on the development of foreign language teaching in related professional colleges and universities.