Reviewerly/exHarmony huggingface dataset:
Diagnosa Kecanduan Gadget Pada Anak Usia Dini dengan Metode Fuzzy Sugeno dan Fuzzy Mamdani

The Tsukamoto fuzzy logic method is one method for problem solving control systems, which is good to implement with a system method that will use human knowledge that can be entered into the system on a computer to find and solve problems that usually require expertise or expertise. looking for a truth. The purpose of this study is to analyze and find methods that can be used in diagnosing gadget addiction in early childhood, and to compare two fuzzy methods that will be used as methods to diagnose gadget addiction in early childhood. The methods used in this study are expert system methods, namely fuzzy sugeno and fuzzy mamdani. The results showed that many children experience addiction to gadgets with a susceptibility to 1-4 hours of use time in one day. The level of addiction in boys is somewhat higher than in girls, the large number of children who are addicted to gadgets at a very early age to recognize and experience addiction to gadgets is the author's main goal to make this thesis if it can be used to monitor the level of gadget addiction in early childhood, research on the contribution of the method of applying the method the fuzzy sugeno and fuzzy mamdani algorithms in diagnosing gadget addiction in early childhood and the contribution to this method is the application of the fuzzy mamdani method and the fuzzy sugeno method to the fuzzy algorithm and the problem of this research is to determine the results of defuzzification of the data that has been obtained.
PELATIHAN FOTOGRAFI PRODUK DI BANK SAMPAH GULON ASRI SURAKARTA

Garbage banks have the potential to increase people's income. Where the waste bank can turn waste into useful items. Products made by waste banks are diverse, both in the form of accessories and crafts. The production of product waste is already good and useful. The member of the waste bank can make attractive products to fulfil the demand of the public. But in terms of selling their products, the waste bank doesn't really understand what kind of attraction is suitable to make their products sell. To overcome these problems, direction is needed using product photography which can increase the selling value of the product. This training aims to improve the community's ability to manage their products with product photography that can increase selling power. The method in this service is the mentoring method. The mentoring was conducted at the Gulon Asri Garbage Bank, Jebres, Surakarta. This training focuses on fostering the creativity of residents to make product photography with smartphones so that it is hoped that it can help residents sell their work from the Gulon Asri Waste Bank to become more attractive through social media.
An Instantaneous Memory Simulation Model Based on Information Fusion

In order to explore the process of the information processing for memory, we put forward an energy fusion model of neuron from functional view, that is, the neuron executes a fusion process before accepting the task. With the fusion computation and modeling processes of waiting and activation, initial selection, and network growth, an instantaneous memory model formed with tree structure and neural network structure. The modeling demonstrates the processing of memory function from encoding to storage, and the retrieve function in line with the extraction of memory. The model structures showed good network performance consistent with other memory network model. The theoretical model provides a new understanding for memory functions and applications.
"Fall PreNoSys": Augmented Reality-based Tripping Hazard Notification System and Initial User Feedback Study

Falls and falling remain a significant problem for people as they age. This work proposes a mobile augmented reality (AR) based system called “Fall Prevention via Notification System” (Fall PreNoSys) to detect likely tripping hazards around the wearer and provide notifications to help them avoid safety problems, along with two phases of user feedback to improve the system design. Blending mobile technologies and human-computer interaction requires significant work on human interface components to become an effective, calm, and useful tool in daily life. A series of studies involving human participants was conducted to gather feedback on the Fall PreNoSys interface design, its utility, and its underlying concepts. Current AR research in gerontechnology and in-home assessments represents a nascent field, and Fall PreNoSys offers a novel approach to fall prevention.Fall PreNoSys uses a Microsoft HoloLens v2 to gather real time 3D models of the space around the user. These models are segmented to identify potential tripping hazards, and the HoloLens scene understanding library is employed to classify objects using an AI classifier. The combination of the Fall PreNoSys algorithm for object segmentation and scene understanding results in a list of objects that can trigger notifications as the user moves around a room.To evaluate notifications style and to get feedback from possible users of the system, two pilot user studies were performed. These studies provided early-stage feedback, initial impressions, guided the continued design of notifications, tested the object detection algorithm's robustness, and evaluated user reactions to static and dynamic notification types developed for Fall PreNoSys.Notifications took the form of 3D visual objects projected onto the HoloLens' AR screen within the wearer's field of view. These notifications were shaped as arrows or OSHA safety-style triangles and were placed on or near identified potential hazards. Based on user feedback from the first phase of the user trial, notifications became interactive, changing color, bouncing in place, and reacting to the participant's relative location to orient their attention to hazards.The study used walking tracks with likely in-home tripping hazards, a combination of machine learning-based detection algorithms, and multiple styles of visual hazard notifications. Study data was collected through two phases of interviews, user feedback of their experiences with the technology, and measurements using the System Usability Study scale to help guide further development of Fall PreNoSys and similar systems in the future. Future work on Fall PreNoSys includes a series of studies with older adults after the latest user feedback from this study is incorporated into the interface design. Additional work includes using eye gaze notification acknowledgements, user path estimations, and out-of-view edge notifications to help people interact with notifications, adapt to the user's walking path, and handle issues with the AR screen's field of view limitations.
Two-Path Object Knowledge Injection for Detecting Novel Objects With Single-Stage Dense Detector

We present an effective system for integrating generative zero-shot classification modules into a YOLO-like dense detector to detect novel objects. Most double-stage-based novel object detection methods are achieved by refining the classification output branch but cannot be applied to a dense detector. Our system utilizes two paths to inject knowledge of novel objects into a dense detector. One involves injecting the class confidence for novel classes from a classifier trained on data synthesized via a dual-step generator. This generator learns a mapping function between two feature spaces, resulting in better classification performance. The second path involves re-training the detector head with feature maps synthesized on different intensity levels. This approach significantly increases the predicted objectness for novel objects, which is a major challenge for a dense detector. We also introduce a stop-and-reload mechanism during re-training for optimizing across head layers to better learn synthesized features. Our method relaxes the constraint on the detector head architecture in the previous method and has markedly enhanced performance on the MSCOCO dataset.
Peningkatan Pengetahuan Tanggap Bencana Kebakaran Melalui VBE

Knowledge of fire disaster response is very necessary to avoid the risk of fire hazards that can occur such as loss of property, injury and even loss of life. The results of the preliminary assessment found that most of the education staff and cleaning service had not received information related to fire disaster response including independent evacuation, around 37 participants (59.5%) were less responsive to fire. Therefore, the community service team conducted fire disaster response counseling through Video Based Education (VBE) to increase the knowledge of educational staff and cleaning service at the Siti Walidah building, University of Muhammadiyah Yogyakarta (UMY). This method of implementing community service activities is carried out by providing education through video media which contains fire prevention in the building and self-evacuation. The results of the implementation of community service activities showed an increase in knowledge about fire disasters among education staff and cleaning service from a less level of knowledge 54%, moderate 46% and high 6% to a high level of knowledge 44%, moderate 39% and less 17%. Through the implementation of community service activities with education using VBE, it can improve knowledge of fire disaster response for education staff and cleaning service at the Siti Walidah UMY building.Keywords:, Disaster Response, Education, Fire, Knowledge Video Based Education
Faktor-Faktor yang Mempengaruhi Prestasi Akademik Remaja

Academic achievement plays a crucial role in an individual's life. This study aims to examine the factors that influence academic achievement using the systematic review method. By gathering and analyzing data from various relevant studies, this research aims to enhance accuracy, comprehensive understanding, and knowledge updates, and identify research areas that have not been addressed in relation to factors affecting academic achievement. The study's results, which includes 11 research journals, indicate that factors such as motivation, social support, study habits, cognitive ability, and learning environment significantly impact academic achievement. This research provides valuable guidance for individuals, parents, teachers, and other stakeholders in comprehending the factors that influence academic achievement. Furthermore, it also serves as a foundation for the development of more effective education policies.Keywords: Academic achievement, adolescents, systematic review
Self-orthogonal codes over a non-unital ring from two class association schemes

There is a non-unital ring [Formula: see text] of order 4 defined by generators and relations as [Formula: see text]. In this paper, we present special constructions of linear codes over [Formula: see text] from the adjacency matrices of two class association schemes. These consist of either Strongly Regular Graphs (SRGs) or Doubly Regular Tournaments (DRTs). We investigate the conditions under which these codes are self-orthogonal, quasi self-dual, or Type IV. As a byproduct of this study, some diophantine relations on the weight of sum of vectors with coefficients in [Formula: see text] are derived. Some examples of codes with minimum distance better than that of Type IV codes over unital rings of the same order in modest lengths are given.
Writable PSI Generator for a Multi-Language IDE Platform

Any state-of-the-art integrated development environment (IDE) should provide software developers with services for quick and correct code transformation. Such services can be used both for program refactoring to improve its quality and for quick fixing of syntax errors in source code. This paper presents the method of constructing a subsystem that makes it possible to create the services described above and also has the property of fast extensibility to support different programming languages. The method of transformation of Program Structure Interface (PSI) - a special data structure, which provides API for development of IDE-services, is proposed. Besides, a method of generating types for PSI in accordance with the syntax of the supported programming language is proposed. The approach is developed for a multi-language platform of a large telecommunications company. Refactoring and Quick Fix features are implemented using on the proposed generator for two IDEs: a Python IDE and a Java IDE.
Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset

The rate of advancement in the field of artificial intelligence (AI) has drastically increased over the past twenty years or so. From AI models that can classify every object in an image to realistic chatbots, the signs of progress can be found in all fields. This work focused on tackling a relatively new problem in the current scenario-generative capabilities of AI. While the classification and prediction models have matured and entered the mass market across the globe, generation through AI is still in its initial stages. Generative tasks consist of an AI model learning the features of a given input and using these learned values to generate completely new output values that were not originally part of the input dataset. The most common input type given to generative models are images. The most popular architectures for generative models are autoencoders and generative adversarial networks (GANs). Our study aimed to use GANs to generate realistic images from a purely semantic representation of a scene. While our model can be used on any kind of scene, we used the Indian Driving Dataset to train our model. Through this work, we could arrive at answers to the following questions: (1) the scope of GANs in interpreting and understanding textures and variables in complex scenes; (2) the application of such a model in the field of gaming and virtual reality; (3) the possible impact of generating realistic deep fakes on society.
PENGEMBANGAN SISTEM INFORMASI UNTUK ADMINISTRASI LAYANAN SURAT DI KELURAHAN BUMIAJI

One of the various types of written communication tools is a letter, which is divided into official letters and non-official letters. Official letters are used for formal purposes. However, the process of letter administration in Kelurahan Bumiaji, Kabupaten Sragen is still done conventionally, where people who need a letter have to come to the Kelurahan office and queue, which makes the process ineffective. The letter archiving process carried out by the Kelurahan office is also poorly organized. To overcome this problem, in this study, an information system for letter service administration based on a website was created for the Kelurahan Bumiaji office. The method used to develop the system is the software development life cycle (SDLC), with a waterfall process model. The result of this research is the creation of an information system that can assist the community in obtaining the letters needed and assisting in mail services by the Kelurahan Bumiaji. In addition, this information system also displays information such as announcements from Kelurahan and assets owned by the Kelurahan to the public. Black box testing shows that the system can function as it should, and SUS testing obtained an average score of 77.4 and a grade scale B+.
KLASIFIKASI EMOSI PUBLIK TERHADAP LARANGAN PENGGUNAAN OBAT SIRUP MENGGUNAKAN ALGORITMA NAIVE BAYES

In October 2022 BPOM withdrew the circulation of syrup drugs and banned the public from using syrup drugs due to the increasing cases of kidney failure in children with a mortality rate of up to 59%. The phenomenon of mass death has caused psychological trauma that threatens. The purpose of this study was to find out how public comments on Twitter and Instagram social media regarding the prohibition of using syrup drugs by classifying emotions using the Naive Bayes algorithm with Particle Swarm Optimization (PSO) feature selection. The methodology used is the AI Project Cycle which consists of problem scoping, data acquisition, data exploration, modeling and evaluation. The amount of data in this study was 1213 comments with 381 surprised comments, 318 angry comments, 277 sad comments, 137 scared comments, and 100 happy comments. Happy and afraid comments have fewer comments than other emotions, so the imbalance dataset will be handled using SMOTE. The results of this study are to compare the classification results of the application of PSO and SMOTE to the Naive Bayes algorithm to determine the best model. Based on the classification results, the Naive Bayes, PSO, and SMOTE models produced the highest accuracy values of 76.48%, with a recall value of 76.47%, a precision of 76.20%, and a fmeasure of 75.62%.
Analysis of the Implementation of E-Learning in Melajah.id Using Human Organization Technology (HOT) Fit Model

This research is motivated by the absence of prior analysis on the success level of implementing Melajah.id e-learning and the performance analysis of the e-learning platform from the perspective of human, organizational, and technological support aspects. Therefore, the existence of this scholarly research is considered necessary as a consideration for stakeholders involved in the policy-making for the development of educational service quality in vocational schools. The purpose of this research is to assess the performance and success level of the implementation of the Melajah.id e-learning platform in use at SMK Negeri 3 Tabanan. For the indicators of the human variable, they include: (a) System Use and (b) User Satisfaction. For the indicators of the organizational variable, they include: (a) Organization Structure and (b) Organization Environment. As for the indicators of the technology variable, they encompass: (a) System Quality, (b) Information Quality, and (c) Service Quality. Data collection for the research was conducted through the distribution of online questionnaires using Google Forms, while supplementary data was obtained through observation and interviews to gather information that could not be revealed through questionnaires. Subsequently, the data analysis approach used to measure the success and performance of the e-learning system implementation was linear regression analysis as the quantitative method. Furthermore, qualitative data analysis was performed through content analysis of interview scripts and interpretation of observational data (photos and videos). The combination of the results from quantitative and qualitative analyses served as the basis for drawing conclusions and making recommendations regarding the analysis of the success of the Melajah.id e-learning implementation in the vocational school where the research was conducted.
THE CORRELATION BETWEEN STUDENTS’ SPEAKING ANXIETY AND SPEAKING ABILITY AT SMA NEGERI 4 BAUBAU

The problem statement of this research was whether there was a significant correlation between students’ speaking anxiety and speaking ability at SMA Negeri 4 Baubau. This research aimed to determine whether there was a significant correlation between students’ speaking anxiety and speaking ability at SMA Negeri 4 Baubau. The method of this thesis was an ex post facto method. The population of this research was the grade eleven students at SMA Negeri 4 Baubau in the school year of 2022/2023 which consisted of a total population was 287 students. The technique of sample used in this research was simple random sampling and the sample of the research was 50 samples. The research was carried out by using a questionnaire and a test as the instruments. The questionnaire was used to find out the category of students’ speaking anxiety and the test was used to find out the category of students’ speaking ability. The data obtained using the questionnaire and test then were analyzed by descriptive statistical analysis, prerequisite analysis, and inferential statistical analysis. Based on the result of this research, it concluded that the Sig. value was 0.001. Because of the Sig. value was higher than 0.05, which meant that Ha was accepted and H0 was rejected. It meant that there was any significant correlation between students’ speaking anxiety and speaking ability on grade eleven students at SMA Negeri 4 Baubau.
Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements

This work demonstrates cascaded diffractive optical elements (DOEs) designed through Machine Learning at terahertz frequencies. Machine learning in this application enables the realization of reconfigurable terahertz holograms through a tailored multi-degree-of-freedom variation; that is by either varying the spacing, number, order, and the rotational alignment of the elements it is possible to attain targeted holograms. The designed DOEs are fabricated by 3D printing technique and validated through experimental measurements. In all cases, the desired diffraction patterns are achieved. Such spatial terahertz light control can have a profound impact on terahertz signal multiplexing, displays, and imaging.
Assessing the Performance of Urban Drainage Networks in Settlements as an Impact of Urbanization

The increase in the number of residents in the city area due to urbanization and the rapid growth of the city's population itself causes many environmental problems, one of which is flooding. The drainage network becomes a vital infrastructure in the development of the city to be free from flood problems, especially in dense areas such as settlements or housing, which are increasingly densely populated every year. The purpose of this study is to examine whether the drainage network in the Berlian Simpati Blorep housing estate in the middle of Merauke City is able to accommodate flood discharge for the next few years. The methods used in this study are quantitative and qualitative, based on the results of the analysis. The results showed that with an existing channel capacity (Qs) of 0.097 m3/sec &lt; the planned flood calculation discharge (Qr) of 13.657 m3/sec, the existing channel capacity was not able to function effectively with a 5-year recurrence period.
Segmentation of Brain Tumor Using a 3D Generative Adversarial Network

Images of brain tumors may only show up in a small subset of scans, so important details may be missed. Further, because labeling is typically a labor-intensive and time-consuming task, there are typically only a small number of medical imaging datasets available for analysis. The focus of this research is on the MRI images of the human brain, and an attempt has been made to propose a method for the accurate segmentation of these images to identify the correct location of tumors. In this study, GAN is utilized as a classification network to detect and segment of 3D MRI images. The 3D GAN network model provides dense connectivity, followed by rapid network convergence and improved information extraction. Mutual training in a generative adversarial network can bring the segmentation results closer to the labeled data to improve image segmentation. The BraTS 2021 dataset of 3D images was used to compare two experimental models.