Reviewerly/exHarmony huggingface dataset:
Klasifikasi Eritrosit Pada Thalasemia Minor Menggunakan Fitur Konvolusi dan Multi-Layer Perceptron

Thalassemia blood disorder is a condition that can affect the blood's ability to function normally and can lead to erythropoiesis. In this blood disorder, there are nine types of abnormal erythrocytes, namely elliptocytes, pencils, teardrops, acanthocytes, stomatocytes, targets, spherocytes, hypochromic and normal. At present, thalassemia examination is carried out using Hb electrophoresis and is done manually so it will be subjective and take a long time. Therefore, this research implements the Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) algorithms. This study aims to determine the performance of convolution features as image feature extraction and MLP as an image classification method and then implemented on NVIDIA Jetson Nano. The convolution features used in this study apply the CNN VGG16 architecture. Then model learning is carried out on 7245 data by configuring hyperparameters. The best accuracy with the hyperparameter configuration is a batch that is 16, the epoch is 400, the learning rate is 0.0001, the dropout1 layer is 0.1 and the dropout2 layer is 0.1. From this configuration it produces optimal accuracy at 96.175%. In the following, the model that has been made is then implemented on the NVIDIA Jetson Nano as a mobile media to be applied to the medical world resulting in an average prediction speed for each class of 48.330 seconds. The obtained performance time and accuracy are suitable for use by medical personnel to predict the class of abnormal erythrocytes.
PERANCANGAN SISTEM INFORMASI MANAJEMEN EKSTRAKURIKULER BERBASIS WEB PADA SMA X JAKARTA

Extracurriculars in education are intended to answer the demands of student’s needs. By participating in extracurricular activities, students will be assisted with creative learning patterns that can increase creativity. In fact, in limited school hours there are many activities that cannot always be carried out, so extracurriculars are created where the implementation is carried out outside school hours to accommodate students in meeting their needs, interests and talents. The management of the extracurricular information system at SMA X has problems, where students sometimes do not get information such as schedules, grades, extracurricular activities and extracurricular registration is done by submitting registration forms where this activity results in file accumulation, which is of course very ineffective nowadays.&#x0D; &#x0D; From the existing problems, the researchers created a web-based extracurricular management information system with HTML, PHP, Bootstrap and database programming languages. This system was tested using the usability testing method which was directly tested on extracurricular members. The method used in the development of the system is the waterfall method. This system is expected to facilitate the extracurricular information management process and can provide extensive information about the extracurricular activities themselves, so that the information provided is not broken down and integrated in one system and can be useful for students, teachers and schools.
HUMAN GENDER DETECTION SYSTEM BASED ON FACIAL IMAGE USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM

The demand for system automation has been continuously increasing with the current technological developments. One of these advancements is in the implementation of face recognition. Camera capabilities have evolved from merely capturing images or videos to being able to process the resulting images. Facial images contain a wealth of information, one of which is the gender information of the individuals. To obtain this information, facial image classification using deep learning is required. In this scientific paper, the author utilizes the Convolutional Neural Network algorithm implemented with the Python programming language and employs TensorFlow as its framework. The research aims to predict human gender based on facial images. The dataset used in this study is obtained from the kaggle.com dataset provider, consisting of 9,600 male facial data and 9,600 female facial data. The data is divided into a training and testing set, with an 80% ratio for training data and a 20% ratio for testing data from the total available data. The model training process is performed for 15 epochs with 768 steps in each epoch. The testing results show that the Convolutional Neural Network method achieves a validation accuracy of approximately 91%. The developed program runs well through a webcam.
Automated Resume Parsing and Job Domain Prediction using Machine Learning

Objectives: This study aims to develop an efficient approach for parsing resumes and predicting job domains using natural language processing (NLP) techniques and named entity recognition to enhance the resume screening process for recruiters. Methods: The proposed approach involves preprocessing steps, such as cleaning, tokenization, stop-word removal, stemming, and lemmatization, implemented with the PyMuPDF and doc2text Python modules. Regular expressions and the spaCy library are utilized for entity recognition and name extraction. The model achieved a prediction accuracy of 92.08% and an F1-score of 0.92 on a dataset of 1000 resumes. An ablation experiment assessed the contributions of different factors. Findings: The approach demonstrated a high prediction accuracy of 92.08% and F1-score of 0.92 for job domain prediction, effectively identifying relevant job domains from resumes. Evaluations on individual job domains showed excellent precision and recall scores, validating its applicability. Preprocessing techniques significantly improved accuracy, while the integration of regular expressions and spaCy enhanced the model’s performance. This approach automates resume screening, reducing recruiters’ workload, saving time and effort, and improving candidate selection and the hiring process. Novelty: This study introduces a novel approach combining NLP techniques, regular expressions, and entity recognition for resume parsing and job domain prediction. This integration enhances accuracy and efficiency, offering a unique solution for resume screening. Keywords: Resume parsing; Job domain prediction; Entity recognition; Machine learning; Natural Language Processing
Cognitive model of the ESG transformation of the organization

A universal model of ESG (Environmental, Social, Governance) transformation is proposed as a basis for developing an organization’s ESG strategy. Fuzzy cognitive logic technologies were used as the instrumental base of the study. A fuzzy cognitive map (FCM) of the organization’s ESG transformation has been developed with a substantiation of a set of parameters (concepts) in the environmental, social and managerial spheres. Based on the static analysis of the fuzzy cognitive model (FCM) and the calculation of its system indicators, it was determined that the factors of the level of ESG-corporate culture, ESG-motivation and integration into the practice of corporate management methodology of strategic management have the greatest positive impact on the target concepts and on the process of ESG transformation as a system. The system experiences a significant negative impact from the factor of resistance to changes in the organization. Collectively, these factors are identified as the main control actions for the organization’s ESG transformation.
The role of creative literature teaching based character on character development

The purpose of this study was to investigate the effectiveness of character-based creative literature learning in developing student character which includes attitudes, behavior, and personality. The research method used was a quasi-experimental involving 250 high school students. The sample was selected using random sampling technique. The findings show that learning character-based creative literature makes a positive contribution to the development of student character, which includes attitudes, behavior, and student personality. The effect of learning literature on student character produces t values at the posttest stage for each aspect of attitude, behavior, and personality (4.452, 3.683, and 4.579) and p values less than 0.05. In addition, the effect size of character-based literary learning on character development is 5.80 which has a significant influence. So, it can be concluded that teaching character-based creative literature has a significant positive influence on character development (attitudes, behavior, and personality) in high school students. The implication of this research is that literature can be used as an alternative in instilling character in students.
PERBANDINGAN K-MEANS DAN K-MEDOIDS UNTUK KLASTERING TINGKAT STRES PADA MANUSIA

Society is faced with various problems as a result of progress and development of the times. Things in social relationships and demands from an expectation in achievement but not being met, from these inability and demands can cause stress in a person. Stress is the body's response caused by demands from outside the individual that exceed the ability to meet the demands to overcome and resolve the problem. The need to respond and manage stress well so that the quality of life becomes better, with clustering it can make it easier to group data. The clustering technique used is the K-Means and K-Medoids methods which partition the data into clusters. Comparison of cluster results used the use of a covariance matrix. So that in the comparison of the K-Means method k=2 and k=3, the best one is k=3 because the determinant of the covariance matrix is ​​smaller, namely -1.4709e-11. In the comparison of the K-Medoids method k=2 and k=3, the best one is k=3 because the determinant of the covariance matrix is ​​smaller, namely -1.4285e-11. Continued comparison of the two methods, namely K-Means and K-Medoids, the best is K-Medoids with a smaller covariance determinant than K-Means.
PEMBUATAN APLIKASI SISTEM INFORMASI PENGELOLAAN KOMUNITAS KARUTA BERBASIS WEBSITE

The OKAKURA karuta community is a gathering place for Indonesians who are interested in the game. The karuta community has carried out and participated in activities such as karuta training, workshops, and inter-community tournaments. The Website-Based Karuta Community Management Information System Application was created to help Karuta community administrators manage internal data and community members view training results. The lack of management data in the Karuta community creates uncertainty among community members, resulting in ineffective training procedures and data redundancy. Furthermore, because exercise recording is still done manually, community members who are not present cannot view training histories. As a result, a Website-Based Karuta Community Management Information System Application was developed to aid in the recording and management of data as well as the search for training history. The System Development Life Cycle development method was used in the creation of this application (SDLC). The programming languages used are HTML, CSS, and PHP, with MySQL as the database and XAMPP as a local server, resulting in a website application that can assist Karuta community administrators in data processing and makes it easier for community members to view training history.
Food Supply Blockchain: A Bright Future for the Food Supply Chain

The food supply chain plays a vital role in guaranteeing the availability of safe, nutritious, and adequate food for human consumption. However, the data transmission in the food supply chain currently faces challenges when combined with blockchain technology, including issues related to data sovereignty, cross-chain interaction, and concurrency performance within blockchain. To address these three challenges, this paper presents a novel architecture that optimizes the aforementioned issues. For the data sovereignty problem, we introduce the International Data Spaces Association (IDSA) approach. To address the cross-chain interaction problem, we employ various cross-chain technologies based on different scenarios, and analyze the advantages and disadvantages of each. The data block matrix is used to tackle the blockchain concurrency performance problem, and we provide an overview of its working mechanism. We evaluate the performance of the data block matrix and demonstrate that it greatly enhances the consensus efficiency of the blockchain.
Translating Identifiers

Don’t. Identifiers should be opaque. If you’re given an owl:sameAs assertion from a party you trust, use that. If you need to mint surrogates because what you’re given aren’t Globally Unique, Persistent and Resolvable Identifiers (GUPRIs)&lt;sup&gt;1&lt;/sup&gt;, either house your inheritance as local parts/suffixes in your global namespace, assert datatype properties to record the historical correspondence, or both.
Sistem Informasi Akuntansi Dan Pengendalian Internal Atas Pembelian Bahan Baku

The purpose of this study is to determine the implementation of purchasing accounting information systems and internal control over the purchase of raw materials. This study uses a qualitative method using an interpretive paradigm and a case study approach. The data used in this study are primary data, such as the company's organizational structure, operational system procedures, and enterprise resource planning systems that are implemented in the company. The results of the research that has been done show that PT Standard Indonesia Industri (SII) at the time of implementing the accounting information system and internal control of raw materials had good and neat documents related to purchases, making it easier to record. Plus PT Standard Indonesia Industri (SII) has procedures or standard operating procedures that are good enough so that they can maintain and control the stock of raw materials for the production process.
Diffraction mathematical model of a laser speckle interferometer of transverse displacements of a scattering object

On the basis of diffraction transformations of wave fields, a mathematical model of a speckle interferometer of transverse displacements of a scattering object has been developed and numerical modeling of speckle-modulated interference patterns and signals at the output of the interferometer has been performed. Numerical calculations of the spatial distribution of complex amplitudes of wave fields in an interferometer were used for modeling when the displaced scattering surface was illuminated by two obliquely incident laser Gaussian beams. A statistical numerical experiment was performed to determine the measurement error of the scattering surface displacement caused by the change of realizations of interfering speckle fields. The simulation results are in good agreement with the results of experimental studies of transverse displacements in the range up to 600 micrometers. Keywords: interferometry, diffraction, interference, laser interferometer, speckle interferometry, interference pattern, speckle modulation, computer simulation.
Wiki-match: A multi-model pipeline for image-caption matching task on Wikipedia dataset

We propose a multi-model pipeline for image-caption matching tasks on Wikipedia-based dataset which leverages object-detection technique and attention mechanism to achieve fine-grained matching between textual representation and image representation. Different from the prior research, we not only evaluate our pipeline effectiveness on common benchmark dataset such as MS-COCO and Flickr30k, but also a new dataset that is de-rived from Wikipedia which is rich in natural entities and abstract concepts. Our findings show: 1) our model pipeline improves R@1 by 113.4%, R@3 by 86.1%, and R@5 by 114.4% compared to the original pipeline provided by the Wikipedia-based dataset. 2) our model pipeline has close to the state-of-the-art performance in common benchmark dataset including Flickr30k and MS-COCO. 3) images that are from Wikipedia creates bigger challenges for models to understand compares to MS-COCO or Flickr30k due to the abstract concepts and broad topics covered by Wikipedia.
Performance Analysis of Fixed-Point and Floating-Point 32-bit Quantizers for $L_{1}$ Norm and Laplacian Source

In this paper, the goal is to examine how the choice of parameters of two different quantizers designed to correspond to fixed-point and floating-point 32-bit formats affect the signal-to-quantization-noise ratio (SQNR) for a wide variance range of the input signal modelled by the Laplacian probability density function, where the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{L}_{\mathbf{1}}$</tex> norm is used to measure the distortion brought by both quantization models. It is shown that it is possible to choose the parameters of the quantizer designed according to the fixed-point 32-bit format in such a way that it outperforms the quantizer designed according to the 32-bit floating-point format in terms of the average SQNR. Since the quantizer designed according to the fixed-point format is simpler to design and implement than the one designed according to the floating-point format, the presented analysis is of great importance for the implementation of a simpler quantizer model, which could be important for numerous applications in which the simplicity of the quantizer model is very important.
Efficient Blockchain-Based Energy Auction Platform for Secured Energy Trading on Industrial Applications

Electricity is vital for numerous aspects of human existence, including the operation of electronic devices and the maintenance of vital infrastructure. Even minor electronic devices and essential infrastructures cease functioning without electricity. The industrial and manufacturing sectors depend on a consistent and reliable energy supply because any mismatch between supply and demand can result in significant financial losses. Therefore, a solution is required to address the imbalance between energy supply and demand, particularly in the industrial sector. This study proposes a blockchain-based smart contract for energy auctions that will provide a safe and secure auction system with transparent and low maintenance costs referred to as gas costs, to enable industries to efficiently acquire energy from an energy supplier. The proposed smart contract is secure, effective, and user-friendly.
A method of environment perception for automatic driving tunnel based on multi-source information fusion

In order to improve the collection accuracy of multi-source information, reduce the false alarm rate of tunnel driving risk and improve the accuracy of environmental perception, a new tunnel environment awareness method based on multi-source information fusion is proposed in this paper. Firstly, R-Fans-16 line lidar and GP-KD6Q01FC monocular camera are selected as the information acquisition equipment. Secondly, according to the multi-source information fusion principle, the Markov distance between the observation value and the actual value is calculated to complete the multi-source data fusion. Finally, the fused information is input into the Adaboost classifier to complete the accurate classification perception of the autonomous driving tunnel environment. The experimental results show that, compared with the traditional environment awareness methods, the proposed method can accurately collect multi-source information, and can achieve high-precision perception of tunnel environment, with the highest perception accuracy of 97.7%.
Peningkatan Kualitas Mahasiswa Pendidikan Matematika Melalui Pelatihan Pemanfaatan Mobile dalam Pembelajaran Matematika

Learning is a interaction processes between students, teachers, and materials in the learning environment. Learning in the past gave the impression of being boring because it was done manually. today, Learning requires a touch of technology to provide concrete visualization and also encourage students to be more enthusiastic in learning. The touch of technology provides the difference between past learning and today's learning. A teacher needs a technology that can support the implementation of learning, one of which is the use of mobile applications in learning mathematics. To improve the quality of students in learning mathematics, the Mathematics Education Study Program conducts student self-development efforts through mobile training. Students participate in training activities well, and they are able to operate mobile applications that can help learning. This training is very important to improve the soft skills and hard skills of students. Mathematics Education Study Program, Jember Islamic University conduct self-development training activities in collaboration with Center for the Development and Empowerment of Mathematics Educators and Education Personnel (P4TK-M).
A Transformer based approach for Abstractive Text Summarization of Radiology Reports

Abstractive text summarization has emerged as a promising approach for generating conciseand informative summaries from radiology reports. The topic of research focuses on developing a mechanism for abstractive text summarization specifically tailored for radiology reports, to generate informative summaries from the voluminous and complex information contained within reports. Manual summarization is time-consuming and prone to errors, while automated techniques can save time, reduce human bias, and improve the overall quality of the generated summaries. The mechanism involves preprocessing the text with NLP techniques, utilizing deep learning-based architectures and transformer models, and generating summaries that capture the essence of the original reports in a more concise form.Challenges such as handling complex medical terminologies, incorporating contextual information, and evaluating the quality of generated summaries are important considerations in this mechanism. The potential applications of radiology report summarization include improving report readability, facilitatingdecision-making, and enabling large-scale data analytics. In this research, Biobart-V2 model is used for summarization and Rouge-L value of 69.42% is being achieved whereas the dataset used is MIMIC III.Further research is needed to address the remaining challenges in this domain and integrate summarization into clinical practice for more effective and efficient radiology report interpretation and patient care.
Developing Web Services and Challenges

Web services have recently sparked a lot of attention among vendors and researchers. The issue of application integration may have a flexible solution thanks to web services, which are based on open standards and already-existing Internet protocols.Web services are becoming more and more common in web applications with the use of WSDL, SOAP, and UDDI. The present Web services designs, though presented with certain obstinate issues, like security. We will provide an outline of these issues in this essay. We think that the success of Web services will depend on resolving these issues. Finally, we anticipate distinct developments in semantic Grid services.