Reviewerly/exHarmony huggingface dataset:
UJI KELAYAKAN KONSTRUKSI BALOK PT DAEYUNG DARI APLIKASI SAP2000 MENGGUNAKAN MACHINE LEARNING DENGAN MODEL LINIER REGRESSION

Authors:
raw_author_name: Ari Wibowo
raw_affiliation_string: universitas subang

Abstract: Structural design is one of the important elements in the construction of a building in order to produce a safe, strong and economical building. Especially in the design of upper structures (in the form of floors, beams, columns, walls and roofs). In beam structures, design and feasibility testing regarding dimensions and the use of reinforcement are safe and economical. Based on the allowable normal stress, it can be seen whether the normal stress is feasible or not if a certain input value is entered, so that information on the feasibility of this quantity can be applied directly to the building. Therefore, it is necessary to have a simulation process regarding the feasibility of stress so that the building to be created can meet the criteria for being a safe, strong and economical building. Overall the analysis process uses Machine Learning methods. By using three types of data, namely algorithm training data, validation data and testing data with a comparison percentage of training and testing data of 80%:20%. The training process produces very high accuracy. This can be seen in the R-Squared value of 0.99 while the RMSE is 1.30e-06. Meanwhile, in the data testing process, high accuracy was also obtained, for R-Squared of 1.0 and RMSE 3.87e-16. After the training and testing process has obtained very good results, we proceed to the beam feasibility testing process by entering certain input values, to produce the normal stress magnitude (Mpa) along with the confidence level of each normal stress prediction result.
Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection

Authors:
raw_author_name: Semih Utku
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Turkey

Abstract: Cyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence of social media in our lives, there is an escalating need for effective methods to detect and combat cyberbullying. This paper aims to explore the utilization of word embeddings and to discern the comparative effectiveness of trainable word embeddings, pre-trained word embeddings, and fine-tuned language models in multiclass cyberbullying detection. Distinguishing from previous binary classification methods, our research delves into nuanced multiclass detection. The exploration of word embeddings holds significant promise due to its ability to transform words into dense numerical vectors within a high-dimensional space. This transformation captures intricate semantic and syntactic relationships inherent in language, enabling machine learning (ML) algorithms to discern patterns that might signify cyberbullying. In contrast to previous research, this work delves beyond primary binary classification and centers on the nuanced realm of multiclass cyberbullying detection. The research employs diverse techniques, including convolutional neural networks and bidirectional long short-term memory, alongside well-known pre-trained models such as word2vec and bidirectional encoder representations from transformers (BERT). Moreover, traditional ML algorithms such as K-nearest neighbors, Random Forest, and Naïve Bayes are integrated to evaluate their performance vis-à-vis deep learning models. The findings underscore the promise of a fine-tuned BERT model on our dataset, yielding the most promising results in multiclass cyberbullying detection, and achieving the best-recorded accuracy of 85% on the dataset.
Klasifikasi Sentimen Opini Metaverse dari Twitter Menggunakan Algoritma Support Vector Machine

Authors:
raw_author_name: Herlawati Herlawati
raw_affiliation_string: Universitas Bhayangkara Jakarta Raya
raw_author_name: Adi Muhajirin
raw_affiliation_string: Universitas Bhayangkara Jakarta Raya

Abstract: With the increasing use of Twitter, a real-time social media platform, it has become one of the places or spaces for people to express their opinions about the metaverse. Therefore, the development of a program capable of classifying tweets based on their opinions into positive, negative, and neutral categories is necessary. In conducting sentiment analysis, the Support Vector Machine (SVM) algorithm is used for classification. The results of this research, through testing using a confusion matrix, yield an accuracy rate of 0.83 or 83%, indicating the level of agreement between the model's predictions and the actual outcomes. Additionally, a precision of 0.93 or 93% is obtained, which shows the model's ability to accurately identify positive, negative, and neutral sentiments in tweets, and a recall of 0.83 or 83%, which describes the model's capability to find and classify accurately.
PERBANDINGAN ANALISIS SURVIVAL MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD DAN REGRESI WEIBULL PADA PASIEN COVID-19 DI RSUD TAMAN HUSADA BONTANG

Authors:
raw_author_name: Triastuti Wuryandari
raw_affiliation_string: Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia; Indonesia, Indonesia
raw_author_name: Sudarno Sudarno
raw_affiliation_string: Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia; Indonesia, Indonesia

Abstract: COVID-19 is brought on by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and transmitted to humans through animal. SARS-CoV-2 infection affects patient's metabolism and causes hyperinflammatory. This condition affects individuals with risk factors such as age, gender, diabetes, heart disease, hypertension, Chronic Obstructive Pulmonary Disease (COPD), obesity, and Acute Respiratory Distress Syndrome (ARDS). One approach to figuring out the association between the time of an occurrence and the independent factors is the Cox Proportional Hazard Regression. The Cox PH regression is a semiparametric model because it doesn’t require a specific distribution test. There is a parametric model used in modeling and analyzing failure time data, namely Weibull regression. The case study is patients with COVID-19 at Taman Husada Bontang Regional Public Hospital who underwent hospitalization from August 2021 to September 2021 data. Based on the Cox PH Regression and Weibull Regression models, variables that affect the survival time of COVID-19 patients are heart disease and ARDS. The AIC value obtained using the Cox Proportional Hazard regression is 635.6149, this value is smaller than the Weibull regression which is 745.5509 so the use of survival analysis with the Cox Proportional Hazard regression is better than the Weibull regression in this case.
Sosialisasi dan Simulasi Mitigasi Bencana dan Pertolongan Pertama pada Gen-Z di Kota Jayapura

Authors:
raw_author_name: Suratni Suratni
raw_affiliation_string: Universitas Terbuka, Jayapura

Abstract: Natural disasters cannot be avoided. The provision that can be given is to provide training to all elements of society so that they have the ability to be able to face natural disasters. disaster mitigation and first aid that was held was to increase public awareness about the risk of natural disasters, increase public knowledge in dealing with disasters and increase knowledge about examples of disaster response simulations. The urgency of this activity is to equip generation Z (Gen-Z) in responding to disaster conditions. The partner in this activity is Basarnas with training participants are the community, students and students. Knowledge and techniques to save themselves and victims of natural disasters are important points for Gen-z to remain socialised and not always dependent on technology.
The Advantages and Challenges of Digital Resources as Supporting Learning Media in Teaching Narrative Writing Text

Authors:
raw_author_name: Rani Santika
raw_affiliation_string: Universitas Muhammadiyah Cirebon, Indonesia
raw_author_name: Rafi Farizki
raw_affiliation_string: Universitas Muhammadiyah Cirebon, Indonesia

Abstract: This research aims to investigate the advantages and challenges of digital resources as supporting learning media in teaching narrative writing text. This research used qualitative methods by using descriptive qualitative design. The data was obtained through document analysis, observation and interview. The subject of this research were two English teachers in Vocational High School Ibnu Khaldun at 10th Grade the academic years of 2022/2023 who implemented digital resources in teaching narrative writing text. The findings of this research show there are four advantages of digital resources. First, digital resources help English teachers in delivering material. Second, access a wider range of material and resources. Third, it can create fun and enjoyable classes. Fourth, improve the quality of learning by new activities. Furthermore, there are three challenges, First, limited internet connectivity. Second, choosing digital resources appropriately. Third, the lack of digital resources implemented by English teachers.
Development of Web-Based Student Registration Information System with Rapid Application Development Approach

Authors:
raw_author_name: Arif Tri Widiyatmoko
raw_affiliation_string: Universitas Pelita Bangsa
raw_author_name: Agung Nugroho
raw_affiliation_string: Universitas Pelita Bangsa
raw_author_name: Wiyanto Wiyanto
raw_affiliation_string: Universitas Pelita Bangsa

Abstract: The management of student data and the student registration process is an important aspect in the world of education. In the digital era, the use of information technology is crucial to maintain the quality and efficiency of education. Therefore, the development of a web-based student registration information system with a Rapid Application Development (RAD) approach is an efficient and effective solution. This research proposes the development of a web-based student enrolment information system with a RAD approach to improve efficiency, accessibility of student data, and the ability to adapt the system to continuous change. The RAD method consists of requirements planning stages, RAD design workshops, and implementation. The test results of the application show that this application is worth using and meets the expected standards. Thus, the development of a web-based student registration information system with the RAD approach is expected to provide innovative and efficient solutions in overcoming student data management problems and the student registration process
Impact of corporate social responsibility on employees’ attractiveness in hospitality industry

Authors:
raw_author_name: Kashif Saleem
raw_affiliation_string: Lahore Leads University

Abstract: &lt;p&gt;This study begins the conversation on the impact that applicant CSR orientation has on a major phase of workforce development—employer attractiveness. There is also virtually no research that investigates CSRO and workforce development. Meanwhile, this present research effort provides evidence that there is some basic relationship between CSRO and employer attractiveness. The data comes from 280 participants who are interested in joining the hospitality and tourism industries in Pakistan. Structural equation modeling was used to analyze the data. The results showed that all four dimensions are significant predictors of employers’ attractiveness. More specifically, the ethical aspect of CSR has a stronger impact on employers’ attractiveness, whereas discretionary behavior in CSR has the least impact. The implications for academicians, researchers, and managers in the hospitality industry are given in detail.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Usability Evaluation and Solution Design Improvement of OCTO Friends as Banking Referral Application

Authors:
raw_author_name: Harry Budi Santoso
raw_affiliation_string: Faculty of Computer Science, University of Indonesia

Abstract: OCTO Friends application was developed to allow customers to directly participate in banking product referral program, has not met expectations and targets. Only 10.6% of the 39,105 members actively provided referrals, and business conversion rate was only 10% of the target. This study aims to evaluate the application's usability and generate recommendations for interface design improvement. A mixed methods research approach was used, including quantitative research through user needs questionnaires and System Usability Scale (SUS), along with qualitative research through in-depth interviews and usability testing. Data collection through questionnaires involved 104 respondents, resulting in a list of user needs and SUS score of 66. The results showed that OCTO Friends is requires some improvements. In-depth interviews and usability testing for six main functions led to 46 interface design improvements, especially improvements in referral data input, follow-up referral, and incentive information functions as these are primary functions of referral application.
Classification of flood disaster level news articles using Machine Learning

Authors:
raw_author_name: None Arna Fariza
raw_affiliation_string: Politeknik Elektronika Negeri Surabaya

Abstract: Floods have a significant socio-economic impact on Indonesian society. Much of this information is sourced from online news articles and social media. This research investigates whether the Support Vector Machine (SVM) method can be used for flood disaster level classification (low, medium, and high). Our methodology involves preparing data extracted from textual news articles on the National Disaster Management Agency (BNPB) website on the topic of flooding. We then labeled the data according to Regulation No. 02/2012 on general guidelines for disaster assessment and used the Support Vector Machine (SVM) method. Training and testing were conducted using different datasets, followed by accuracy and error evaluation. In addition, we considered the performance comparison of SVM with other classification methods, including Decision Tree, Naive Bayes, Adaboost, Random Forest, and Xgboost. The experimental results show that SVM still does not get good accuracy results for flood disaster level classification. The SVM accuracy level result of (52%) is still low compared to Random Forest (78%), and Xgboost (68%). Further research is expected to increase the accuracy of SVM for flood level classification.
Capabilities Comparison Of The Augmented Reality Application Development Frameworks On The Android Platform

Authors:
raw_author_name: Muhammad Ihsan Jambak
raw_affiliation_string: Universitas Sriwijaya
raw_author_name: Pacu Putra
raw_affiliation_string: Universitas Sriwijaya

Abstract: In the implementation of augmented reality, the natural process of human-computer interaction continues to be a challenge, specifically to reduce the complexity in the effort of using and providing comfortability. Therefore, it is necessary to search for methods and tools to simplify the complex process of building Augmented Reality applications. Vuforia is a multi-platform that has long been used as an Augmented Reality application development framework. Android and iOS platform frameworks are the new alternatives following the development of cellular technology. Furthermore, a comparative capability was conducted between Vuforia and ARCore on the Android platform. The general performance and the ability to understand the environment include working in horizontal and vertical planes, the ability to work based on lighting conditions, and the distance of the camera during the tests. The results showed that ARCore is superior to Vuforia in almost all testing metrics. However, in overexposed or too dim lighting and at very close surfaces, Vuforia is slightly superior but not essential. Therefore, it can be concluded that ARCore's capability is better than Vuforia.
A Systematic Review of Risk Management Tools and Techniques in Software Projects

Authors:
raw_author_name: Aji Prasetyo
raw_affiliation_string: Universitas Indonesia
raw_author_name: Teguh Raharjo
raw_affiliation_string: Universitas Indonesia

Abstract: The crafting of software is a continual procedure, and the success of each step of that process is contingent on effective management. Despite this, numerous organizations need help developing e-service systems, frequently dealing with budget constraints and tight deadlines. The lack of focus on risk management in software projects is likely to blame for these failures. Management of risks is crucial to ensuring the success and efficacy of software development projects, as it assists in identifying areas of vulnerability and provides valuable insights into the project's most important aspects. This study identifies and analyzes tools and techniques to support software development projects’ risk management activity. A systematic literature review (SLR) methodology was employed to collect and evaluate relevant research articles. The findings highlight various risk management tools and techniques, including brainstorming, root cause analysis, risk probability assessment, artificial intelligence, and risk response planning. These tools and techniques contribute to identifying, analyzing, planning, and controlling risks in software projects. The research provides insights into the state of the art in risk management. It complements previous studies by offering practical guidance on software development project risk management tools and techniques.
Picture Fuzzy Aggregation Operator based Integrated MEREC-WASPAS Technique for Video Conferencing Tool Selection

Authors:
raw_author_name: Baby Bhattacharya

Abstract: Journal of Uncertain SystemsAccepted Papers No AccessPicture Fuzzy Aggregation Operator based Integrated MEREC-WASPAS Technique for Video Conferencing Tool SelectionChayel Tripura, Sayanta Chakraborty, and Baby BhattacharyaChayel Tripura, Sayanta Chakraborty Search for more papers by this author , and Baby Bhattacharya Search for more papers by this author https://doi.org/10.1142/S175289092450003XCited by:0 (Source: Crossref) Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 5 July 2023 Accepted 10 February 2024 PDF download
Analysis of Cybersecurity Vulnerabilities Using Data Science Techniques

Authors:
raw_author_name: Ricardo Villarroel-Molina
raw_affiliation_string: Universidad Técnica Estatal de Quevedo
raw_author_name: Javier Guaña-Moya
raw_affiliation_string: Instituto Superior Tecnológico Japón

Abstract: Cybersecurity analysis using data science and machine learning plays a crucial role in today's digital era, where information security has become imperative for organizations. This study focuses on the intersection between cybersecurity and data science, using machine learning methods and text analytics to understand and strengthen defenses against threats. The importance of TF-IDF (Term-Inverse Document Frequency) is highlighted as a tool to evaluate the relevance of terms in documents and its application in vulnerability classification.The Multinomial Naive Bayes classifier is presented as an efficient tool in text classification, calculating probabilities of belonging to specific classes based on the frequency of terms. The essential formulas used in this algorithm, such as conditional probability and multinomial distribution, are detailed.The KDD (Knowledge Discovery in Databases) methodology guides the process, from datacollection on platforms like Kaggle to data selection, cleaning and transformation. The use of `TfidfVectorizer` facilitates the discretization of text data, and the `GridSearchCV` method optimizes the model's hyperparameters, achieving an accuracy of 97.36%. Finally, the confusion matrix reveals good overall performance, although areas for improvement are identified, especially in the 'High' class.
QR Code-Based Smart Document Implementation Using Distributed Database And Digital Signature

Authors:
raw_author_name: Idris Winarno
raw_affiliation_string: Teknik Elektro, Politeknik Elektronika Negeri Surabaya
raw_author_name: Amang Sudarsono
raw_affiliation_string: Teknik Elektro, Politeknik Elektronika Negeri Surabaya

Abstract: In digitized world, digital documents are essential for information sharing. However, some organizations continue to place their reliance in traditional hard-copy formats concerning about the legitimacy of documents. This study presents an innovative approach to document verification with digital signatures, distributed databases, and QR codes. Using a two-step process for data integrity and document authentication, the study approach entails developing a Smart Document with a QR code and digital signature. For increased security and scalability, the system design distributes hash fragments among several databases using the hash split approach. The system's excellent performance, resistance to sluggish HTTP-based attacks, and effectiveness in document verification are highlighted by the results and debates. The report ends with recommendations for future improvements to strengthen the system's resilience, like implementing more secure database engines and enhancing fault tolerance. In conclusion, this method offers a viable way to verify documents in hardcopy and electronic formats in a secure and scalable manner.
Twitter Sentiment Analysis of Public Space Opinions using SVM and TF-IDF Methods

Authors:
raw_author_name: Ulya Ilhami Arsyah
raw_affiliation_string: Politeknik Negeri Padang
raw_author_name: Mutiana Pratiwi
raw_affiliation_string: Universitas Putra Indonesia YPTK Padang
raw_author_name: Abulwafa Muhammad
raw_affiliation_string: Universitas Putra Indonesia YPTK Padang

Abstract: Public space opinion reviews are currently a source of information for interested parties and decision-makers. Twitter is a social media that is a means of expressing themselves for people to express their opinions and criticize the current situation. This becomes information for readers. Information published on Twitter contains elements of commentary on a situation or object Sentiment analysis of public space opinion on Twitter using Machine Learning with the Support Vector Machine (SVM) method with the data weighting process using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Dataset obtained by scraping using the Twitter API as much as 5000 data then labeled where the goal is to get accuracy on positive, negative, or neutral sentiment. The results of research conducted experiments on three Machine Learning algorithms with the extraction function "TF-IDF" obtained an accurate training model with good classification capabilities, especially SVM of 91,6% on data distribution 70: 30; SVM is 92.8% in the case of data distribution of 80: 20; the SVM is 91,8% in the case of 90:10 decomposition data.
Implementasi Pendekatan Kontekstual pada Pembelajaran Bahasa Indonesia di Kelas IIB MIS Mutiara Aulia

Authors:
raw_author_name: Rora Rizki Wandini
raw_affiliation_string: Universitas Islam Negeri Sumatera Utara

Abstract: This study aims to find out how teachers implement contextual approaches in the classroom. The research method used is field qualitative. By using this method, researchers obtain data through interviews, observations, and direct documentation in the field. The results of the study show that the Contextual Teaching and Learning (CTL) approach is very effective and efficient in the learning process. Students find it easier to understand the learning material because it is related to their daily lives. This learning approach is expected to encourage students to create and learn knowledge independently, as well as educate and motivate them to apply that knowledge in real life.