Reviewerly/exHarmony huggingface dataset:
Fine-Tuning IndoBERT for Indonesian Exam Question Classification Based on Bloom's Taxonomy

Background: The learning assessment of elementary schools has recently incorporated Bloom's Taxonomy, a structure in education that categorizes different levels of cognitive learning and thinking skills, as a fundamental framework. This assessment now includes High Order Thinking Skill (HOTS) questions, with a specific focus on Indonesian topics. The implementation of this system has been observed to require teachers to manually categorize or classify questions, and this process typically requires more time and resources. To address the associated difficulty, automated categorization and classification are required to streamline the process. However, despite various research efforts in questions classification, there is still room for improvement in terms of performance, particularly in precision and accuracy. Numerous investigations have explored the use of Deep Learning Natural Language Processing models such as BERT for classification, and IndoBERT is one such pre-trained model for text analysis. Objective: This research aims to build classification system that is capable of classifying Indonesian exam questions in multiple-choice form based on Bloom's Taxonomy using IndoBERT pre-trained model. Methods: The methodology used includes hyperparameter fine-tuning, which was carried out to identify the optimal model performance. This performance was subsequently evaluated based on accuracy, F1 Score, Precision, Recall, and the time required for the training and validation of the model. Results: The proposed Fine Tuned IndoBERT Model showed that the accuracy rate was 97%, 97% F1 Score, 97% Recall, and 98% Precision with an average training time per epoch of 1.55 seconds and an average validation time per epoch of 0.38 seconds. Conclusion: Fine Tuned IndoBERT model was observed to have a relatively high classification performance, and based on this observation, the system was considered capable of classifying Indonesian exam questions at the elementary school level. Keywords: IndoBERT, Fine Tuning, Indonesian Exam Question, Model Classifier, Natural Language Processing, Bloom’s Taxonomy
Transfer Learning based Low Shot Classifier for Software Defect Prediction

Background: The rapid growth and increasing complexity of software applications are causing challenges in maintaining software quality within constraints of time and resources. This challenge led to the emergence of a new field of study known as Software Defect Prediction (SDP), which focuses on predicting future defect in advance, thereby reducing costs and improving productivity in software industry. Objective: This study aimed to address data distribution disparities when applying transfer learning in multi-project scenarios, and to mitigate performance issues resulting from data scarcity in SDP. Methods: The proposed approach, namely Transfer Learning based Low Shot Classifier (TLLSC), combined transfer learning and low shot learning approaches to create an SDP model. This model was designed for application in both new projects and those with minimal historical defect data. Results: Experiments were conducted using standard datasets from projects within the National Aeronautics and Space Administration (NASA) and Software Research Laboratory (SOFTLAB) repository. TLLSC showed an average increase in F1-Measure of 31.22%, 27.66%, and 27.54% for project AR3, AR4, and AR5, respectively. These results surpassed those from Transfer Component Analysis (TCA+), Canonical Correlation Analysis (CCA+), and Kernel Canonical Correlation Analysis plus (KCCA+). Conclusion: The results of the comparison between TLLSC and state-of-the-art algorithms, namely TCA+, CCA+, and KCCA+ from the existing literature consistently showed that TLLSC performed better in terms of F1-Measure. Keywords: Just-in-time, Defect Prediction, Deep Learning, Transfer Learning, Low Shot Learning
English for Daily Activities: Textbook Developed for Islamic Education Study Program at STAIN Mandailing Natal

This research project aims to create teaching materials in the form of textbooks specifically designed for English courses, focusing on "English for Daily Activities" within the PAI study program at STAIN Mandailing Natal. The research methodology employed in this study is research and development, utilizing the ADDIE model (Analysis-Design-Develop-Implement-Evaluate). Data collection involves three main instruments: 1) needs analysis tools, 2) validity assessment tools, and 3) practicality evaluation tools, with the practical stage serving as the project's concluding phase. The outcomes of the research and development process yielded a positive impact on both lecturers and students during the learning process. The validation results indicate high levels of validity, with a total validation percentage of 83.85%, falling within the "very valid" category. Limited trials or practicality testing of the textbooks were conducted by gathering feedback from lecturers and students. The results show strong practicality, with its total percentage of 86.09%, classifying the developed textbook as "very practical" to use by lecturers and students within the Islamic Education Study Program at STAIN Mandailing Natal.&#x0D; Keywords: Textbook, Teaching Materials, Islamic Studies
Towards Smart and Green Features of Cloud Computing in Healthcare Services: A Systematic Literature Review

Background: The healthcare sector has been facing multilateral challenges regarding the quality of services and access to healthcare innovations. As the population grows, the sector requires faster and more reliable services, but the opposite is true in developing countries. As a robust technology, cloud computing has numerous features and benefits that are still to be explored. The intervention of the latest technologies in healthcare is crucial to shifting toward next-generation healthcare systems. In developing countries like Ethiopia, cloud features are still far from being systematically explored to design smart and green healthcare services. Objective: To excavate contextualized research gaps in the existing studies towards smart and green features of cloud computing in healthcare information services. Methods: We conducted a systematic review of research publications indexed in Scopus, Web of Science, IEEE Xplore, PubMed, and ProQuest. 52 research articles were screened based on significant selection criteria and systematically reviewed. Extensive efforts have been made to rigorously review recent, contemporary, and relevant research articles. Results: This study presented a summary of parameters, proposed solutions from the reviewed articles, and identified research gaps. These identified research gaps are related to security and privacy concerns, data repository standardization, data shareability, self-health data access control, service collaboration, energy efficiency/greenness, consolidation of health data repositories, carbon footprint, and performance evaluation. Conclusion: The paper consolidated research gaps from multiple research investigations into a single paper, allowing researchers to develop innovative solutions for improving healthcare services. Based on a rigorous analysis of the literature, the existing systems overlooked green computing features and were highly vulnerable to security violations. Several studies reveal that security and privacy threats have been seriously hampering the exponential growth of cloud computing. 54 percent of the reviewed articles focused on security and privacy concerns. Keywords: Cloud computing, Consolidation, Green computing, Green features, Healthcare services, Systematic literature review.
THE IMPACT OF SCHOOL WATCHING METHOD ON EARTHQUAKE DISASTER PREPAREDNESS ON PRIMARY SCHOOL STUDENTS AT SDN MOJOREJO 2 SRAGEN

Introduction: The world's largest archipelago, Indonesia is located on the equator between the plains of Asia and Australia and between the Pacific and Indian oceans. This geographical position can increase the danger of natural disasters such as tectonic earthquakes, volcanic eruptions, tsunamis, and so on. There are many consequences of eartquakes, on of which is that many buildings and destroyed or collapsed. School children can get involved in disaster preparedness at their schools because it is an effective, dynamic, and sustainable strategy that teaches kids to recognize the signs of disasters that happen around them from an early age. Disasters can occur anywhere, at any time, even in the middle of the school day. The aims of this study was determining the effect of the School Watching Method on earthquake disaster preparedness on Primary School Students, particularly at SDN Mojorejo 2 Sragen. Methods : The method employed was Quasy Experimental. The research design was a group pretest-posttest without control group design . there were 49 respondents and the sampiling used was total sampling. The research instrument used was a modified questionnaire from a questionnaire and a check sheet or preparedness observation from LIPI (S3) and UNESCO/ISDR. Results: The results of the dependents t test showed a p value of 0.0000, this meaning that there was a significant difference in average disaster preparedness after being given disaster education using the school watching method. Conclusions: The findings showed that there is an effect of the school watching method on earthquake disaster preparedness at SDN Mojorejo 2 Sragen.
Educational Policy In The School Operational Assistance Program To Improve The Quality Of Education

The aim of this research is to describe and analyze how education policies in the School Operational Assistance program improve the quality of education. The research instrument used was a descriptive qualitative approach, data collection was based on library research. The results of this research are that educational policies in the school operational assistance program to improve the quality of education have been running quite well, Process Which quality achieved when all organizer education (including maker policy And holder interest) involved active in maintenance education the in accordance with role And not quite enough he answered, school operational assistance is one of the most important bridges for educational institutions and the community, because the funds spent by schools to achieve quality education are very large, but with BOS funds schools will be helped so that they can run effectively, and are very important for people who experience an unstable economy but have the desire to send their children to a higher level of education, with BOS funds students can study diligently without thinking about school fees, quality education is education that is able to create students who are active, intelligent, creative and graduates who are useful for themselves and for the country.
IELTS Reading: Perceived Challenges and Strategies by Vietnamese University English Majors

This study focused on identifying Vietnamese English as a foreign language (EFL) students’ perception of their challenges in doing the IELTS reading test and corresponding strategies to cope with these challenges. The study involved eighty-nine English majors at Can Tho University, and data were collected via a questionnaire administered to all participants and interviews conducted with 10 random students. The results showed that Vietnamese EFL students encountered a number of challenges in doing IELTS reading tests, among which lack of vocabulary and time constraint for the tests were two most noticeable difficulties. The results also revealed that the students in the study frequently employed text related, question related, and general practical strategies for doing the reading test in IELTS. Based on the findings, more opportunities to expose students to tests as well as an integration of instruction on test-taking strategies were suggested. Keywords: EFL Vietnamese students, IELTS reading test, reading strategies, test-taking techniques DOI: 10.7176/JEP/14-28-03 Publication date: October 31 st 2023
The Influence of Human Resource Quality, Social Media Use, and Business Capital on the Creation of A Bath Soap Business Based on Business Sustainability and the Empowerment Environment in the Southern Meruya Area

Nowadays, people are increasingly concerned about personal hygiene because many diseases are caused by bacteria and germs. One of the means to clean yourself is soap. The form of soap that is currently in demand by the public is paper soap because it is practical and easy to use. Usually, soap added active substances such as triclosan to kill bacteria, but triclosan has a negative impact on the body. Judging from the many negative impacts that triclosan can cause, it is necessary to think of other alternative ingredients that can replace triclosan as an antimicrobial. By utilizing aloe vera as an ingredient in soap making, it is not only able to kill bacteria, but can also soften the skin. This is due to the presence of lignin which is useful for maintaining skin moisture and retaining water in the skin so that excessive evaporation does not occur. Aloe vera contains saponins that function as natural antibacterials. By utilizing aloe vera as an ingredient in soap making, not only can it kill bacteria, but it can also soften the skin. And through this community service activity, it is hoped that it can contribute in the form of data and information related to the influence of the quality of human resources, the use of social media, and business capital in the creation of a bath soap business in the sustainability of the business and the empowerment environment of the community, especially in the South Meruya area. Keywords : Influence of Human Resources Quality, Use of Social Media, Business Capital, Bath Soap Creation DOI: 10.7176/JRDM/92-03 Publication date: October 31 st 2023
Personality Detection on Reddit Using DistilBERT

Personality is a unique set of motivations, feelings, and behaviors humans possess. Personality detection on social media is a research topic commonly conducted in computer science. Personality models often used for personality detection research are the Big Five Indicator (BFI) and Myers-Briggs Type Indicator (MBTI) models. Unlike the BFI, which classifies personalities based on an individual’s traits, the MBTI model classifies personalities based on the type of the individual. So, MBTI performs better in several scenarios than the Big Five model. Many studies use machine learning to detect personality on social media, such as Logistic Regression, Naïve Bayes, and Support Vector Machine. With the recent popularity of Deep Learning, we can use language models such as DistilBERT to classify personality on social media. Because of DistilBERT’s ability to process large sentences and the ability for parallelization thanks to the transformer architecture. Therefore, the proposed research will detect MBTI personality on Reddit using DistilBERT. The evaluation shows that removing stopwords on the data preprocessing stage can reduce the model’s performance, and with class imbalance handling, DistilBERT performs worse than without class imbalance handling. Also, as a comparison, DistilBERT outperforms other machine learning classifiers such as Naïve Bayes, SVM, and Logistic Regression in accuracy, precision, recall, and f1-score.
The Taxis Relations of Clause Complexing in the Editorials of The Daily Graphic

This paper focuses on the use of taxis relations of clause complex in editorial writing to match the length which seems not longer than a page. The problems discussed are: the hypotactic and paratactic clause complexes in ten editorials spanning January 2014 to March, 2014, and the realization of those clause complexes through the use of conjunctive devices in the lexicogrammar. The theory of Systemic Functional Grammar which opines that the taxis can be of two kinds parataxis and hypotaxis, suggested by Halliday (1994), and Halliday &amp; Matthiessen (2004) underpins the discussion. This study adopts a qualitative approach as it investigates 10 purposively sampled editorials of the Daily Graphic . A first step of the analysis explores the choice of the hypotactic vs. paratactic clause complexes in the editorials while the second step also reveals the use of conjunctives to realise the choice of a particular clause complex structure. The results reveal that the clause complexes are of both parataxis and hypotaxis in the editorials but hypotaxis dominates parataxis, as the writers do not depart from the dominant use of hypotaxis in all the ten editorials. The realization is relatively excellent as the choice among parataxis and hypotaxis characterizes each relation between clauses (each nexus) within a clause complex. Keywords: Clause Complexing, Taxis, Parataxis, Hypotaxis, Conjunctives DOI: 10.7176/JLLL/99-02 Publication date: October 31 st 2023
INFLUENCE OF USING SEMANTIC MAPPING TO TEACH WRITING NARRATIVE TEXT

This study aims to obtain empirical evidence about the use of semantic mapping in learning to write narrative texts in class X MAS Integrated Al-MUnawaroh. The method used is a quantitative method with a quasi-experimental design. The sample in this study were students of class X-A as the experimental class and X-B as the control class. The instrument in this study is a test. Tests were carried out before and after students received treatment. The average pretest score for the experimental class was 57.69, while the control class was 49.74. The student's writing ability in the experimental class and control class during the pre-test was still constrained by vocabulary and differences in text. The average value of the posttest in the experimental class was 72.60 and that of the control class was 63.77. The writing ability of the experimental class students improved more than the control class. The results showed that the average value of the class that used semantic mapping was higher than the class that was not given treatment. Therefore, it can be concluded that there is a significant increase in the effect of using semantic mapping on learning to write narrative texts for class X MAS Al-Munawaroh Integrated Academic Year 2022/2023.
Neuroevolution-Based Fine Tuning of the Quantised Deep Learning Models

Deep learning models are the most efficient models in many machine learning tasks. The main disadvantage when using them in IoT, mobile devices, independent autonomous or real-time systems is their complexity and memory size. Therefore, much research has concentrated on compression techniques of deep learning architectures. One of the most popular technique is quantisation. In most of the works, the quantisation is done based on the nearest neighbour quantisation technique. This work focuses on improving the quantisation efficiency in pretrained and quantised models. This approach has the potential to improve the final accuracy of quantised models. The main postulate of the work is that final quantisation states of the network based on nearest neighbour rounding does not guarantee optimal accuracy. In the presented work, the neuroevolution strategy is used as an optimisation approach. The neuroevolution in each iteration changes the values of the small percentage of weights. It shifts theirs values to different quantisation states. The work shows that proposed neuroevolution with an appropriate set of operators and parameters can fast improve the accuracy of the quantised models. The results are presented for popular architectures such as VGG and Resnet for image classification and detection. Additionally, simulations were carried out for the autoencoder architecture.
The Historical and Practical Logic of Marxist Virtue and Law in the New Era

The relationship between the rule of law and the rule of morality has always been an issue of great concern in both academic and political circles, and it is also a cutting-edge issue of Marxism in the new era. Marxist theory contains the theoretical content of moral and legal governance, and it is this content that provides a scientific and powerful theoretical guidance for the idea of moral and legal governance with Chinese characteristics, which was gradually formed in the process of "Sinicization" after Marxism was introduced into our country, and at the same time points out the direction for our future development. Starting from the ideological sources and theoretical innovations of Marxist moral and legal governance, further generalizing and analyzing the historical and practical logic of moral and legal governance in China in the new era will clarify how to combine the Marxist theory of moral and legal governance with the traditional Chinese concept of moral and legal governance and realize creative transformation and development.
Governance of a Blockchain-Enabled IoT Ecosystem: A Variable Geometry Approach

The proliferation of Internet of Things (IoT) applications is rapidly expanding, generating increased interest in the incorporation of blockchain technology within the IoT ecosystem. IoT applications enhance the efficiency of our daily lives, and when blockchain is integrated into the IoT ecosystem (commonly referred to as a blockchain-IoT system), it introduces crucial elements, like security, transparency, trust, and privacy, into IoT applications. Notably, potential domains where blockchain can empower IoT applications include smart logistics, smart health, and smart cities. However, a significant obstacle hindering the widespread adoption of blockchain-IoT systems in mainstream applications is the absence of a dedicated governance framework. In the absence of proper regulations and due to the inherently cryptic nature of blockchain technology, it can be exploited for nefarious purposes, such as ransomware, money laundering, fraud, and more. Furthermore, both blockchain and the IoT are relatively new technologies, and the absence of well-defined governance structures can erode confidence in their use. Consequently, to fully harness the potential of integrating blockchain-IoT systems and ensure responsible utilization, governance plays a pivotal role. The implementation of appropriate regulations and standardization is imperative to leverage the innovative features of blockchain-IoT systems and prevent misuse for malicious activities. This research focuses on elucidating the significance of blockchain within governance mechanisms, explores governance tailored to blockchain, and proposes a robust governance framework for the blockchain-enabled IoT ecosystem. Additionally, the practical application of our governance framework is showcased through a case study in the realm of smart logistics. We anticipate that our proposed governance framework will not only facilitate but also promote the integration of blockchain and the IoT in various application domains, fostering a more secure and trustworthy IoT landscape.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 16 • Number of submissions sent for review: 16 • Number of submissions accepted: 16 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 100 • Average number of reviews per paper: 2 • Total number of reviewers involved: 3 • Contact person for queries: Name: Dinan Anshary Email: 5thiwwgarbindonesia@itb.ac.id Affiliation: Bandung Institute of Technology