Reviewerly/exHarmony huggingface dataset:
SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS

Our study briefly discusses the architectures of convolutional neural networks (CNN), their advantages and disadvantages. The features of the architecture of the convolutional neural network U-net are described. An analysis of the CNN U-net was carried out, based on the analysis, a rationale was given for choosing the CNN U-net as the main architecture for using and building subsequent created and analyzed models of cert neural networks to solve the problem of segmentation of medical images. The analysis of architectures of convolutional neural networks, which can be used as convolutional layers in CNN U-net, has been carried out. Based on the analysis, three architectures of convolutional neural networks were selected and described suitable for use as convolutional layers in CNN U-net. Using CNN U-net and three selected convolutional neural networks (“resnet34”, “inceptionv3” and “vgg16”), three neural network models for medical image segmentation were created. The training and testing of the created models of neural networks was carried out. Based on the results of training and testing, an analysis of the obtained indicators was carried out. Experiments were carried out with each of the three constructed models (segmentation of images from the validation set was performed and segmented images were presented). Based on the testing indicators and empirical data obtained from the results of the experiments, the most suitable neural network model created for solving the problem of medical image segmentation was determined. The algorithm for segmentation of medical images has been improved. An algorithm is described that uses the predictions of all created models of neural networks, which demonstrated a more accurate result than each of the considered models separately.
Penerapan Normalisasi Data Dalam Mengelompokkan Data Mahasiswa Dengan Menggunakan Metode K-Means Untuk Menentukan Prioritas Bantuan Uang Kuliah Tunggal

At Budi Darma University there are obstacles in providing UKT rocks where it is profitable and less targeted for students who get it. This happened because those who deserved this assistance were students who had difficulty in costs, therefore we needed a way by grouping student data based on their social level. In determining the students who deserve to get the rock, they can use the data of students who are undergoing their studies at Budi Darma University. By digging up information based on student data. So that the data can be used first, the data normalization is carried out in order to obtain more accurate data. Where student data can be grouped correctly, data normalization must be carried out. One of the normalization methods that are often used in normalizing data is the decimal scaling method which is a data transformation method with normalization to equalize the range of values ​​on each attribute with a certain scale by moving the decimal value from data in the desired direction After the data is normalized, the next process is to explore student data information by applying data mining. The application of data mining is carried out to obtain information in the form of student data groups that are used as a priority in obtaining UKT assistance. The method used in classifying student data is using the K-Means algorithm. The manual testing method is that there are 3 clusters where the number of clusters 0 cluster 1 and cluster 3 is the same as testing data mining applications, namely rapidminer so that those who deserve to be prioritized get tuition assistance based on the sample, namely cluster / grouping 0 which consists of 22 people. This study aims to see the effect of applying data normalization in the K-Means method to classify student data which is used as a recommendation in the selection of UKT assistance.
Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN

Jakarta is a city in Indonesia that has a high population density that must pay attention to its health condition. Good air quality provides positive benefits to support public health so that they can be more productive at work and create fresh and healthy air. This study uses Machine Learning to classify air based on certain attributes. Then, the development of a prediction model based on time data is designed to produce a predictive map of air pollution in Jakarta area for the next 3 years. The methods applied are Decision Tree and Artificial Neural Networks. As a result, the Decision Tree and Artificial Neural Network models show very good accuracy for predictions from 2024 to 2026. The Decision Tree and Artificial Neural Network models get an accuracy of 98% and 94%. In 2025 the Decision Tree and Artificial Neural Network models get 99% and 93% accuracy. In 2026 the Decision Tree and Artificial Neural Network models get an accuracy of 94% and 93% which can be seen from the Decision Tree model which is superior to the Artificial Neural Network with a difference of 1 - 6%.
Implementation of Dimensionality Reduction with SVD to Improve Rating Prediction in Recommender System

Recommender system is widely implemented in various fields. Collaborative Filtering is one of the most used recommender system paradigms because it is easy to use. K-means clustering algorithm is widely use in Collaborative Filtering. This algorithm can predict the item rating that will be given by a user. Rating can be predicted by calculating the average rating of the item. The clustering performance of this algorithm is low because this algorithm selects initial centroid randomly. This causes high errors in the item rating prediction. To obtain lower error, we propose dimensionality reduction with Singular Value Decomposition (SVD). SVD is able to factorize the clustering result data and reduce dimensionality of the data. Dimensionality reduction with SVD can be carried out by removing non-dominant characteristics of the data. This study uses the result of factorization to calculate the similarity between clusters. The value of similarity between clusters is used to predict the rating of an item that will be given by a cluster. The experimental results show that the combined method of K-means and SVD can produces RMSE up to 8.936% lower than the K-means method.
Penerapan Metode Naïve Bayes Dalam Mendiagnosa Penyakit Leptospirosis

There are still many people who are less concerned with environmental cleanliness, where this can lead to the accumulation of bad bacteria that cause disease. One of them is Leptospirosis, a disease caused by a bacterial infection, namely the Leptospira strain. Leptospirosis is a disease caused by the laying of bacteria in animals that is transmitted to humans. This disease is often ignored by the public due to lack of understanding about this disease and the high cost of conducting examinations and consulting a doctor or hospital. So that in overcoming this we need a way that is able to help the community in knowing and diagnosing Leptospirosis, one of which is by using an expert system. The expert system used in solving the problem of Leptospirosis is by using the Naïve Bayes method. The application of the Naïve Bayes method in diagnosing Leptospirosis is carried out by collecting data about Leptospirosis where this process aims to find out what symptoms are caused by Leptospirosis. The process of collecting data on this disease is done by interviewing an expert or doctor who handles the problem of Leptospirosis. The results of the diagnosis of Leptospirosis based on the calculation of the Naïve Bayes method with new user data samples get results with a definite level of accuracy where the user experiences Leptospirosis disease with mild symptoms of 63% and the results of the user experiencing Leptospirosis disease with severe symptoms of 37%. Naïve Bayes is able to diagnose with 100% accuracy seen from the total severe and mild symptoms
Pendeteksian Penyakit Mulut dan Kuku Pada Sapi dengan Menerapkan Metode Naïve Bayes

The outbreak of the issue in 2022 related to foot and mouth disease which is a viral outbreak experienced in ruminant livestock. This virus outbreak is a virus that is easily transmitted and attacks all types of animals that have even or split nails. The spread of this epidemic is very troubling for the breeder, one of the cattle or cattle breeders. Where as a result of this outbreak, many cattle breeders suffered enormous losses both in terms of finances and the time of raising their livestock. The reason is that many cattle breeders, especially in rural areas, do not know how to treat this disease early and require a large amount of money if the cattle are examined by a veterinarian. Therefore, to make it easier for cattle breeders, we need a way to find out the causes and conditions of cows suffering from mouth and hoof disease based on the symptoms experienced by cows, one of which is by using an expert system. Naive Bayes. The Naïve Bayes method is a probability method by predicting future opportunities based on past experience. As a result, the owner of the cow should treat the cow so as to avoid death and infection to other animals.
Question Answering System at the Kingdom of Sumedang Larang with Naïve Bayes Method

The Sumedang Larang Kingdom is one of the kingdoms in Indonesia which was founded by Prabu Tajimalela in 721 AD. The Sumedang Larang Kingdom is known as the national history of Indonesia. Still, most of the current generation does not know the history of the Sumedang Larang Kingdom, especially the younger generation. Therefore, we developed a question-and-answer system to seek information about the Sumedang Larang Kingdom. With the development of information technology, research on question answering systems is applied to research on Biomedical Questions to produce correct answers. Our system will help literacy about the Sumedang Larang Kingdom for the younger generation, especially students, and increase Indonesian cultural assets. The QA system aims to generate and provide precise short answers to user questions by automatically using information extraction and natural language processing methods. To collect and create questions, we use the concept of ontology. In addition, we use the Natural Language Naïve Bayes method to answer user questions. We built a QA system that can help students find information about the history of the Sumedang Larang Kingdom. Based on the accuracy of the results of testing the method we propose. In our evaluation, we involve the Decision Tree method as the base model. We note that the accuracy of the Naïve Bayes method is higher than that of the Decision Tree. The accuracy result of Naïve Bayes at the ratio of 8:2 and 7:3 is 67%, while the Decision Tree is only 56%.
Pendeteksian Penyakit Limfadenopati dengan Menerapkan Metode Naive Bayes

Lymphadenopathy or often known as lymphatic disease is a disease that is very risky if not handled properly. There are so many signs or symptoms of this disease that have not been known to the public, some even consider it an ordinary lump that grows on the human body and do not know that they are experiencing lymphadenopathy in themselves [3]. Usually to find out the disease in the lump, do an examination to the hospital or consult a doctor. Based on these problems, a technique or method is needed that can facilitate the community in diagnosing the early symptoms of this lymphadenopathy, one of which is by using an expert system. An expert system that in solving problems with oral and nail diseases, one of them uses the Naïve Bayes method. Naïve Bayes is a probability method by predicting future opportunities based on past experience. With the aim of making it easier for ordinary people to take the first step in dealing with diseases that are experienced about lumps on the body. Based on the results of the application of the Naïve Bayes method, the results are based on the input of the user's symptoms, the accuracy of the results of lymphadenopathy in users with mild lymphadenopathy is 32% and acute lymphadenopathy is 68%. critical
Sentiment Analysis of Student Satisfaction on Telkom University Language Center (LaC) Services on Instagram Using the RNN Method

Social media has become a medium for communication between individuals and aspects of the business, including decision-making processes, brand promotion, brand marketing, and personal branding. One of them is Instagram. Using the comments feature on Instagram, users can communicate and give opinions on an upload on an Instagram account. Sentiment analysis can be done to analyze comments on the LaC (language center) Instagram account to measure student satisfaction sentiment towards Telkom university's LaC (language center) services. This study aims to analyze the sentiment or opinion of student satisfaction with the Telkom University Language Center (LaC) service on Instagram. The author also performs a classification based on positive sentiment, negative, and neutral categories using the Recurrent Neural Network (RNN) method and the Confusion Matrix measurement. From the test results on the model built to get an accuracy value of 79%.
Penerapan Metode Long Short-Term Memory Pada Pendataan Warga Berbasis Android

Along with technological developments, utilization in data processing both input and output become very important. Starting from inputting data manually, it proves that the use of technology is still not fully used. Data that has been processed conventionally sometimes encounters problems caused by human error. Not only that, when data must be entered into a computer, it requires additional time and when natural disasters such as floods, earthquakes, landslides occur. Census files that are still in the conventional form are likely to be damaged and will take time to re-data. This is the main reason for RT 004 RW 008 to need an efficient data collection system. The application of Optical Character Recognition (OCR) technology in this study is expected to be a solution in increasing the efficiency of the data input process. In this study, the input process is carried out in real time using the Long Short-Term Memory (LSTM) method to scan data on e-KTP. The results of testing OCR technology with the LSTM method in this study for the three experiments carried out were 1) The e-KTP detection experiment resulted in an accuracy of 100%, 2) The trial of reading 19 attributes on the e-KTP resulted in an accuracy of 98.42%, 3) Testing the error reading of the e-KTP attribute resulted in an accuracy of 93.56%. Overall, the accuracy of the LSTM method in word detection on e-KTP results in an accuracy of 92.1%. These results indicate that the application of OCR technology with the LSTM method is one of the right solutions for the data input process.
Analisis Sistem Akuntansi Pendapatan Dalam Penetapan Jumlah Gaji Pegawai Pada Rs Surya Medika Gresik

The purpose of this research discussion is to find out whether the accounting system of solar medics income RS Surya Medika whether it can support the accuracy of the total salary of employees. Themethod inthis research is descriptive research with a qualitative approach. The data analysis technique used was survey, interview and documentation. The result obtained by researchers namely stated that RS Surya Medika has an adequate accounting information system to support the accuracy of the total salary of the employees in terms of the income system used by RS Surya Medika. But there are still some weaknesses, namely the concurrent assigment of the financial departement, the documents used are not complete such as accounting records that are used only general journals.based on that suggestion given to RS Surya Medika is the need for the application on maximum payroll procedures, it is necessary to separate the task beetween the personnel and the financial department. This is to facilitate the distribution of tasks facilitate supervision and the need for an increase in the quality of human resources so that it can support the success of the solar RS Sakit Surya Medika.
Implementation Of Promotion Push Notifications in Ticket Booking Applications Using Forward Chaining Algorithm

Waterfall is a leading tourist attraction in Winongsari Village, Kaliwiro District, Wonosobo Regency. This waterfall, named Curug Winong, has become a developing tourist spot. This research contains the design and development of promotion push notifications using the Forward Chaining method which is intended to help determine promotions. This application was developed using the PHP programming language with a MySQL database. This application determines the promotion with the conditions that have been determined. Based on dates, holidays, and other applicable conditions. system testing, namely black box and white box testing.
Pemberdayaan Guru-Guru Bahasa Inggris SMP Kota Banjarmasin dalam Mengembangkan Soal Berbasis HOTS untuk Mengukur Kemampuan Membaca

Having the ability to think critically is very important, especially for school-age teenagers who are still in the process of self-discovery and have not been able to sort out information. Teacher is an important figure in preparing students with critical thinking skills since teachers have a great opportunity to instill positive things that support the development of their students. That is why, it is important for teachers to be able to train their students to think critically and develop instruments that can measure students’ critical thinking skills. This activity aims to train teachers in developing instruments of critical thinking skills assessment in reading English texts. The method used is a participatory method in which emphasizing active participation of 35 teachers of English MGMP SMP in Banjarmasin. The implementation of this activity consists of delivering the material, evaluating the understanding of the material, assisting the compilation of questions, and analyzing question items. The findings show that 96% of the items compiled have met the standards of critical thinking assessment in the field of reading skills. In addition, more than 65% of the total items are formulated correctly according to the concept of assessment. This means that this activity has reached the target which 71% of the total participants scored more than equal to 6. It is concluded that the participants of the training have a good understanding of the concept of critical thinking assessment and are able to compile multiple choice questions based on critical thinking skills.
The Outlook of German Companies on IT-Security and Their Readiness for Quantum Computing

Quantum computers are currently in development.As soon as they are available for widespread use, they can offer great benefits for companies.However, they can also pose dangers to information security.Because of this, protection measures are needed.To gain an insight into the outlook and knowledge of companies on quantum computing and their internal decision processes when establishing new technologies, qualitative interviews were conducted.Because not all companies know about quantum computing, the questions were designed without necessary previous knowledge in mind.Additionally, an employee of the German Federal Office for Information Security (BSI) was asked for their opinions.Many of the interviewed companies were not well informed about quantum computing and some lacked preparation of their general IT security.However, they had ideas how they could utilize such a much more powerful computer in the future.To gauge how quantum-secure measures could be implemented among companies, the companies were asked about their decision criteria when deciding on implementing a new technology.Their answers often included price and distribution, functions of the technology and external influences like other companies or guidelines from the government.To support the transition to quantum computer-safe cryptography in companies, different areas of action are examined.Supporting the availability of relevant use cases, market incubation and standards, collaboration between companies and education among them can help in pushing the spread of post-quantum security in companies in the future.
Automatic Standardization of Arabic Dialects for Machine Translation

Based on an annotated multimedia corpus, television series Mar{\=a}y{\=a} 2013, we dig into the question of ''automatic standardization'' of Arabic dialects for machine translation. Here we distinguish between rule-based machine translation and statistical machine translation. Machine translation from Arabic most of the time takes standard or modern Arabic as the source language and produces quite satisfactory translations thanks to the availability of the translation memories necessary for training the models. The case is different for the translation of Arabic dialects. The productions are much less efficient. In our research we try to apply machine translation methods to a dialect/standard (or modern) Arabic pair to automatically produce a standard Arabic text from a dialect input, a process we call ''automatic standardization''. we opt here for the application of ''statistical models'' because ''automatic standardization'' based on rules is more hard with the lack of ''diglossic'' dictionaries on the one hand and the difficulty of creating linguistic rules for each dialect on the other. Carrying out this research could then lead to combining ''automatic standardization'' software and automatic translation software so that we take the output of the first software and introduce it as input into the second one to obtain at the end a quality machine translation. This approach may also have educational applications such as the development of applications to help understand different Arabic dialects by transforming dialectal texts into standard Arabic.
An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Knowledge networks play an important role in the process of knowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge. Existing research lacks insight into the internal structural features of knowledge networks constructed from expertise. There is also a lack of effective methods for constructing personalised knowledge networks for students' cognitive states. This paper analyses the categories and structures of expertise for students' cognitive states, and presents in detail a grey prediction algorithm to identify students' cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students' cognitive states, and analyses the knowledge network structure from the perspectives of paths and statistical properties. After that, the paper gives a method for analysing the knowledge flow of the expertise network. The experimental results validate the effectiveness of the proposed method.
Video Chatting Platform with Video Enhancing Feature Using Super Resolution

Abstract: This paper describes the process of creating a Multi-User Video Chatting Platform with Agora Video SDK and a Django Backend. The Video Super Resolution technique is being used to improve the quality of video calls. VSR is the process of reconstructing high-resolution videos from low-resolution ones. We employ the Dynamic Up sampling Filters Without Explicit Motion Compensation technique for VSR. We reconstruct an HR image directly from the input image using dynamic Up sampling filters, and the fine details are added using the computed residual.