Reviewerly/exHarmony huggingface dataset:
Hubungan antara Teman Sebaya dengan Motivasi Belajar pada Siswa SMP Negeri 2 Sunggal Kelas IX-1 Tahun 2021.

This study aimed to obtail information and clarity on the relationship between in tercourse peer motivation with eighth grade students of junior high school Negeri 2 Sunggal class IX-1 at year 2021. The method use is likert scale the method data analitys used is product moment corellation. Based on the date r anality, product moment with coofisien = 0,324 and determinan coofisien 2 = 0,413 with P =0,0010,050. The result of the study showed that this hipotesis is accepted it showed that there are correlation between association peer reind study motivation. Is equetion, it means if association peer highost so that motivation. Data collection tools in this study was a questionaire. The result of data showed that association peer is 41,3% by hipotetic mean = 100 and empiries mean = 111,17, its over deviation standart = 11,049 and study motivation in high category by mean hipotetic = 100 and empiries mean= 115,80 and its over deviation standart=14,163. It means that the hypothesis is that the higher the good relationship between peers, the higher the motivation to learn.
Hubungan Antara Kecanduan Game Online Dengan Perilaku Agresif Pada Remaja Di Mabar Hilir Kota Medan

This study used quantitative correlational aimed to find out the relationship between online gaming addiction and aggressive behavior in adolescents in the lower reaches of medan cities. Aggression is the behavior of hurting a person, both physically and psychologically. One of the factors of aggressive behavior is addiction to online games. Online gaming addiction is the inability to control the behavior of playing online games that causes social problems and emotional problems for the perpetrator. The sample in this study was 70 people using quota sampling techniques based on criteria: adolescents who played fighting online games, played for at least one year, played online games at least 3 hours per day or 20-25 hours a week. Data capture uses a scale of online gaming addiction and aggressive behavior tested using product moments. Based on the results of the analysis it is known that there is a positive association of online gaming addiction with aggressive behavior, where rxy = 0.600 with a significant p = 0.000 0.05. This means that the hypothesis is that there is a positive relationship of online gaming addiction with aggressive behavior. Assuming the higher the addiction to online gaming then the aggressive behavior is higher, and vice versa. Hypothesis accepted. The determinant coefficient (r2) of the relationship between variable X and variable Y of r2 = 0.360, this indicates that online gaming addiction contributes to aggressive behavior by 36%. There are still 64% of other factors that influence aggressive behavior.
Additive Ratio Assessment (ARAS) Method in the Selection of Popular Mobile Games

Dealing with using smartphones was growing will increase the sales value of the mobile game industry, both from local and foreign developers. It can impact the increasing number of start-ups trying to get involved in the game industry, as well as a large number of job opportunities for those who want to be strengthened in the development of mobile games. This opportunity become the main sector in the domestic creative industry. In Indonesia, the development of mobile games was deemed necessary to analyze the most popular mobile game products that assist by criteria of game. It shows the market trend currently. The researcher hopes this result can provide for developers to choose the type of game to be strengthened based on certain categories. The mobile games involved in this research are Garena Free Fire, Mobile Legend: Bang Bang, PUBG Mobile, Higgs Domino Island, Ragnarok X: Next Generation, Rise of Kingdoms: Lost Crusade, Roblox, State of Survival: Survive The Zombie Apocalypse, Genshin Impact, Coin Monster, Minecraft, Clash Royale, Clash of Clans, Ragnarok M: Eternal Love, State of Survival: The Walking Dead Collaboration. The research was an Additive Ratio Assessment (ARAS) method with the results of 5 recommended mobile games. The test is carried out with a sensitivity test which shows the criterion of "not containing violence" which is the most sensitive criterion among other criteria.
Pelatihan Bahasa Inggris Prediction Test-TOEFL Like Section Structure dan Reading Bagi Siswa SMA Kesatrian 2 Semarang

&lt;p&gt;SMA Kesatrian 2 Semarang routinely organizes TOEFL English tests for prospective graduates. The Deputy Principal for Curriculum said that currently English is a very important provision for students after graduation. So, it is important to hold a TOEFL test to find out and improve students' English skills. In this activity which was held in early January 2022, the material in the TOEFL test that will be taught in this training is the Structure and Written Expression Section Reading Comprehension Section, namely the ability to understand and solve grammatical questions and reading texts in English, with consideration as advanced training. before and the importance of mastering both skills. In this activity, members of community service provide strategies in solving each problem in the Structure and Reading Section. The USM PKM team uses the lecture method in delivering the material and applies a pre-test and post-test system to evaluate the results of the activities. From the results of the pre-test and post-test, more than 75% of the participants experienced an increase in TOEFL scores. It can be concluded that the purpose of this service activity at SMA Kesatrian 2 Semarang was achieved. The material that we convey is very encouraging, new knowledge and understanding about the TOEFL test.&lt;/p&gt;&lt;p&gt;Keywords:&lt;em&gt; TOEFL, structure, reading&lt;/em&gt;&lt;/p&gt;
Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes dan Artificial Neural Network : Studi Kasus Fakultas Teknik UNIS Tangerang

The faculty of engineering has 4(four) studies programs namely, informatics engineering, civil engineering, industrial engineering, chemical engineering. The number of lecturers and students owned by the Faculty of Engineering based on PDDikti Year1 2019/2020 reporting data is 41 permanent lecturers and 750 students. The problems faced by the Faculty of Engineering UNIS Tangerang include the low percentage of students who graduate on time compared to students who graduate not on time. In the 2015/2016 graduation year, only 30% of students passed on time, the rest did not graduate on time. This study aims to assist the Faculty of Engineering in predicting student graduation, so that it can be anticipated earlier. This research uses the attributes of total credits, 1st semester IP, 2nd semester IP, 3rd semester IP, 4th semester IP. The methods used in this research are Naïve Bayes and Artificial Neural Network. The data used in this study used 330 records of students who graduated in 2012-2016. The results of the accuracy obtained after testing with the system using 20% data testing obtained an accuracy of 63.63%, 71.05% precision, 67.5% recall, and 62.6% AUC.
Multi-SAP Adversarial Defense for Deep Neural Networks

Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several Lâˆž attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several Lâˆž attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.
Mikro-Irigasi Cerdas dengan Sprinkler Menggunakan Fuzzy Logic Pada Lahan Terbatas Untuk Pertanian 4.0

&lt;span lang="EN-US"&gt;Most irrigation systems in Indonesia use surface irrigation systems or conventional irrigation, which are still heavily influenced by Earth's gravity, making it very difficult to manage and monitor. while current technological developments are almost evenly distributed throughout Indonesia with an already very good internet network, so that it can be used to support agricultural systems 4.0. In this study, researchers used intelligent computing technology on micro irrigation with Fuzzy Logic algorithm and Sugeno inference to decide when irrigation water is distributed to sprinkler irrigation systems based on a predetermined range value, then the results are evaluated to see the accuracy of the model that has been made before testing in the actual environment. The purpose of this research is to produce smart micro irrigation technology that can be used on limited land and lack of water so that it can help facilitate the work of farmers.&lt;/span&gt;
Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "&lt;em&gt;satrawi&lt;/em&gt;". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface.
Alquran Learning Model for Blind Students

Every Muslim has the right to receive Qur'an learning because the Qur'an as a way of life will not have an impact on Muslim life without starting with the ability to read and understand so that it comes to practice. Al-Quran learning for physically perfect people already has problems, even less for visually impaired people. Therefore, this study aimed to describe the Alquran learning model for students with special needs. This study was conducted using a descriptive approach. Data were collected through observation, interviews, and documentation studies. The data were analyzed through reduction, classification, interpretation to the conclusion. Based on the results, Alquran learning methods for blind students were lecture, question, and answer, discussion, sorongan, bandongan, and drill, while Alquran learning media for blind students were braille, reglet and stylus, Alquran Audio, and Digital Alquran.
A Systematic Review of the Adoption of eHealth Cloud-based Technology Applications during COVID-19

Electronic health technology applications are important tools that are utilized to provide health care services. These applications allow healthcare professionals and patients to share, manage and have control over their health information electronically whilst maintaining social distancing. Under hard conditions brought by the coronavirus 2019 disease (COVID-19), eHealth tools can be used to assist with the provision of healthcare services. However, the adoption of these technology applications by users is still questionable. This study presents a systematic literature review of 31 articles that were published on the adoption of eHealth cloud-based technology applications during COVID-19. A systematic literature review was conducted using PRISMA protocol by searching the Scopus databases. The search was conducted for the year 2020 to 2021 since COVID-19 is still new in the research field. From 31 articles that were selected, it was found that most countries are still left behind on this topic. It was only a few researchers in each country who tried to contribute or put effort into the topic. Furthermore, it was indicated that Italy contributed the highest with 5 authors to this topic. Thus, more research still needs to be conducted on this topic. One of the limitations of this study was that, the data was gathered from the Scopus database only. Researchers suggest that for future research, other additional sources need to be searched to find out more about the adoption of eHealth cloud-based technology applications during COVID-19.
Digital Exercisers in Students Training for the Unified State Exam in Mathematics

The article considers two types of digital exercisers for preparing students for the Unified State Exam in mathematics at the advanced level: exercises for training in solving problems with a short answer (the first part of the Unified State Exam) and training simulators for preparing for solving problems with a detailed answer (the second part of the Unified State Exam). The first type of the digital exercises is characterized by the ability to repeatedly solve both problems of a certain type, and the variants formed from them, similar to the variants of the Unified State Exam, receiving hints in case of difficulties, automatic verification and analysis of answers. The second type of the digital exercises is characterized by a step-by-step analysis of the solution of each problem with hints and analysis of the correctness of each step, as well as the support of such simulators (in particular, on the topics “Stereometry” and “Problems with a parameter”) with dynamic models created in the interactive mathematics environment “1C:Mathkit”. The various forms of using simulators in the educational process are also discusses in the article: support for independent activity of students in the full-time educational process, support for online training with a teacher in the context of the coronavirus pandemic, and independent online training without a teacher based on specially prepared and structured training courses. In the latter case, in addition to simulators, other digital educational resources were used, the purpose of which was to increase the degree of clarity in the presentation of educational material: videos, slides, dynamic models, training exercises for problems with a detailed answer of the Unified State Exam variants.
Performance Comparison of Apriori, ECLAT and FP-Growth Algorithm for No Biological Data Genes for Association Rule Learning

This project is carried out to study the performance comparison of Apriori Algorithm, ECLAT Algorithm and FP-Growth Algorithm for no biological data genes. There are many genes with no biological data, but for this project we have chosen 4 types of no biological data genes. No biological data genes are genes that have no specific data about themselves such as location, behaviour and function of the genes. Association Rule Learning is a technique implementing big data in finding frequent item-sets. Frequent item-sets are items that occur frequently in the database. The performance of these three algorithms is compared through time efficiency and the ability to process small and large datasets. After the comparison, we can conclude that FP-Growth algorithm is the fastest algorithm for small data-set and Apriori algorithm and ECLAT algorithm takes lesser time to generate the frequent item-sets compared to FP-Growth algorithm.
Augmented Reality in Solar System Learning at Primary School Level in Indonesia: A systematic literature review

A systematic review of the use of AR in learning about the solar system in primary schools in Indonesia was conducted. This review identified 25 articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. We examined the tools used to develop AR, the type of AR used, the focus of the research, and the benefits of AR for solar system learning. Our findings show that the most commonly used tool for developing AR is Unity, and the most popular type of AR used is marker-based AR. Most of the research focuses on development and only a few on effectiveness and implementation. The use of AR is known to improve learning outcomes, critical thinking skills, scientific literacy, visual spatial skills and student curiosity. This systematic review provides researchers and educators with avenues for future research.
Transforming customer banking experiences: Ai-Driven RPA for customized service delivery

In the contemporary digital age, financial institutions are persistently seeking novel methodologies to enhance consumer experiences and optimize service provision procedures. This research paper investigates the profound impact that Robotic Process Automation (RPA) and Artificial Intelligence (AI) could have on the financial industry, focusing on the deliverance of customized services. Through the utilization of RPA powered by AI, financial institutions have the ability to customize their services, streamline processes, and improve overall consumer contentment. This manuscript explores the advantages, obstacles, implementation tactics, and case studies that demonstrate effective AI-powered RPA deployments within the banking sector. Upon conducting an extensive examination, it becomes indisputable that RPA powered by AI possesses immense potential in revolutionizing the banking experience for customers.
Research on Science and Technology Intelligence Text Classification Based on TFIDF Weight Improvement

With the rapid advancement of technology and informatization, the quantity of science and technology intelligence has grown exponentially. The automatic classification of such texts has become an important research direction in the field of science and technology intelligence. This paper extracts the required information and establishes a science and technology intelligence classification dataset based on the intelligence data resources accumulated by the enterprise over the long term, which have been manually screened and annotated. In view of the special structure of the dataset, this paper adjusts the TF-IDF values (Term Frequency times Inverse Document Frequency) by setting corresponding positional weights based on the word frequency in class titles or class keywords, which compensates for the shortcomings of TFIDF ignoring word position and word category distribution. Building upon the improving the TFIDF weight, this paper combines a high-precision machine learning model to achieve the classification of science and technology intelligence, with an accuracy rate of 97.2%. The experimental results demonstrate that the method adopted in this paper significantly enhances the classification accuracy of science and technology intelligence texts.
A framework for neuromotor and neurofunctional rehabilitation using multi-agent systems

Degenerative diseases and stroke disable many people, so investigating methods for rehabilitating patients is necessary. Thus, using the framework for creating virtual environments to work with neuromotor and neurofunctional rehabilitation, benefits can be achieved using virtual environments and body tracking devices. Developing such applications involves many requirements, such as constructing virtual environments, data storage, interaction with input devices, processing, and communication in rehabilitation sessions. In this paper, the proposal and development of a multi-agent framework are presented to facilitate the development of software for neuromotor and neurofunctional rehabilitation, providing advantages for developers, such as eliminating the need for code rewriting, facilitating the reuse of coding standards, and decreasing the time to develop new features.
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning

Multi-agent value-based approaches recently make great progress, especially value decomposition methods. However, there are still a lot of limitations in value function factorization. In VDN, the joint action-value function is the sum of per-agent action-value function while the joint action-value function of QMIX is the monotonic mixing of per-agent action-value function. To some extent, QTRAN reduces the limitation of joint action-value functions that can be represented, but it has unsatisfied performance in complex tasks. In this paper, in order to extend the class of joint value functions that can be represented, we propose a novel actor-critic method called NQMIX. NQMIX introduces an off-policy policy gradient on QMIX and modify its network architecture, which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for the joint action-value function. In addition, NQMIX takes the state-value as the learning target, which overcomes the problem in QMIX that the learning target is overestimated. Furthermore, NQMIX can be extended to continuous action space settings by introducing deterministic policy gradient on itself. Finally, we evaluate our actor-critic methods on SMAC domain, and show that it has a stronger performance than COMA and QMIX on complex maps with heterogeneous agent types. In addition, our ablation results show that our modification of mixer is effective.
On quantum states generated from interacting oscillators

In this paper, we study the quantum states generated from two and three linearly interacting quantum harmonic oscillators. We consider the possibility that one of the oscillators be under the influence of a classical external source and obtain the total and reduced density matrices related to the system. We show how the problem can be generalized to n linearly interacting oscillators straightforwardly.