Reviewerly/exHarmony huggingface dataset:
TOPICS IN COMMUNICATIONS WITH CHAOTIC SYSTEMS

The use of chaotic systems in communications approaches the threshold for industrial applicability. We shall give here an overview over a few techniques we think are promising. However, this overview does not pretend to be complete and it contains personal views on the subject. Also, the use of chaotic system for information encryption is not discussed at all. Only a choice of methods for coding and modulation for the transmission of digital information will be presented. An overview of a number of techniques to transmit information using chaotic systems is given. The difficulties in obtaining a good performance of such systems with respect to channel noise leads to fundamental question, to which we give a possible answer. Given the generally mediocre performance of such approaches, we have identified two fundamental questions which should help us pinpoint the main problems with the existing approaches with the help of information theory and communication theory. In particular it has been confirmed both in theory and by a constructive approach leading to a controlled variant of CSK, that indeed the information generated by free running chaotic systems (as captured by the Kolmogorov-Sinai entropy) is the problem, and the solution is a suitable control that makes this information part of the payload.
Content-based Image Retrieval Using Effective Synthesized Images from Different Camera Views via pixelNeRF

We propose a novel image retrieval method using synthesized images from different camera views based on pixel-NeRF. The proposed method aims to retrieve images from various viewpoints as related images. To achieve this, we synthesize images whose camera views are different from the original query image as additional new queries for the retrieval. Then we determine the weight of the synthesized images that indicates how much they contribute to the retrieval. The proposed method can effectively utilize synthesized images for the retrieval and provide desired images independently from the camera view of the query image. Experimental results show that our method can improve the retrieval performance by utilizing effective synthesized images from different camera views.
The Integration of the KWL (Know, Want, Learn) Strategy and the Zoom Conference in Teaching Reading: An Action Research

This research aimed to improve the students’ reading comprehension by using the Know Want Learn (KWL) strategy. The objectives of this research were to investigate whether the integration of the Know Want Learn (KWL) strategy in the Zoom conference can improve the students’ reading comprehension and identify the students' perception of using the KWL Strategy in the Zoom conference. In this research, the researchers used classroom action research as the research design in collecting the data. The research was conducted in three cycles. The samples of this research were 30 students. The researchers employed questionnaire, observation and test for the data collecting technique. The test used in this research was reading test consisted of 25 questions. The researchers used descriptive analysis for analyzing the data. The findings of this research show that the integration of the Know Want Learn (KWL) strategy in the Zoom conference successful to improve students’ reading comprehension and reading activity. The improvement of students’ reading comprehension can be concluded from the mean score of the pre-test is 65.42 and the mean score of the post-test is 85.49. Moreover, there are 26% (8) students who show moderate perception, 24% (7) students offer positive perceptions, and 50% (15) students show very positive perceptions. Furthermore, the combination of KWL Strategy and Zoom conference can help students achieve the learning target. The students can get many benefits from using this combination to study English. They also can conduct an effective teaching-learning activity in a modern way even though they are not allowed to attend the face-to-face classroom.
Promoting an Integrated Science and English Pop-Up Book for Primary School Students

After doing an observation and interview with teachers, the researchers found that English and science were taught in a conventional method and were not thoroughly learnt. Hence, the teaching was not effectively conducted, and learning often ended up in boredom, which resulted in ineffective teaching and learning atmosphere. The objectives of this research were to create a combined English language and science pop-up book learning media for animal and human movement organs and to acknowledge its feasibility. The 4D development steps developed by S. Thiagarajan et al. were used in the research design, consisting of four stages: define, design, develop, and disseminate. The participants included in this research were the 5th-grade students of SD N 26 Belinyu, consisting of 35 students. Out of 35, 20 were female, and 15 were male. Two senior female teachers in the same school were also included in the interview. Data were collected by documentation, expert validation sheets, feasibility test sheets and user response questionnaires. Expert validation was conducted by two expert judgments, while language validation was justified by two language experts in Bahasa Indonesia and English. The results showed that the benefits students gain from using pop-up book media include seeing, opening, and shutting images on the pop-up book, which helps strengthen children's motor development.
Women and Racism in Kamala Harris’ Speeches: A Critical Discourse Analysis

Critical Discourse Analysis is an approach to studying a text to increase the reader's awareness of the meaning behind and the real purpose of the text. Previous studies analyzed texts with a general CDA approach, while this research focuses on a specific topic, namely women and racism. Using Norman Fairclough's three-dimensional critical discourse analysis theory and Van Dijk's sociocognitive approach, this study aims to analyze the types and uses of women's issues and racism, as well as the personal and impersonal motivations behind Kamala Harris' speeches. A total of three speeches and their scripts were selected and categorized based on the indicators of women's issues and racism. This study revealed five types and use of racism issues for women of colour that Kamala Harris raised in her speeches, namely women of colour in the economy, education, civil and society, justice, and health care. Her motivation was a call to take action against women of colour's injustice.
An Exploration of Indonesian EFL Learners' Speaking Anxiety

This research reports the factors that trigger Foreign Language Speaking Anxiety as perceived by Indonesian EFL learners. The qualitative method was employed in this research. The participants of this research were the 33 Indonesian EFL learners from the class X IPS 3 of SMAN 1 Pandeglang, year academic 2019-2020. The initial process of collecting the data was by spreading a questionnaire known as Foreign Language Anxiety Scale (FLCAS) developed by Horwitz et al. (1986). Then it was followed by an in-depth interview for the ten students with high anxiety levels based on their FLCAS Score. The data was then analysed through three steps developed by Miles and Huberman (1994). The triangulation technique was used to strengthen this research's credibility. This research found that the factors that could trigger students' anxiety are divided into two. The first is teacher-related factors such as assertive teaching and the teacher's calling method. The second is student-related factors such as their negative view of English, being mocked by friends, fear of making mistakes, self-confidence, and inadequate support to practice English speaking. To reduce anxiety, students need to be exposed to the target language as often as possible and get used to it to improve their confidence in speaking English. In addition, the teacher should always be able to find a way to make an entertaining and less tense classroom environment.
NAÏVE BAYES ALGORITHM CLASSIFICATION IN SENTIMENT ANALYSIS COVID-19 WIKIPEDIA

In recent years during the pandemic Wikipedia created more than 5,200 new pages regarding COVID-19 cases, with an accumulation of more than 400 million pages by mid-June 2020. Wikipedia is one of the most popular websites of our time. In this case Wikipedia always integrates new and fast research. To get an opinion from wikipedia text, sentiment analysis is needed. The analysis was conducted using a classification containing public sentiment regarding the issue of COVID-19 in Indonesia. The classification method used in this study is naive bayes classifier (NBC). Naïve Bayes Classifier is a popular method of solving classification problems. This classification method is often used in sentiment analysis in both precision and data computing. This wikipedia classification is obtained from each label, namely positive, negative and neutral classes. The results of tests conducted in the classification of naive bayes get a high accuracy of 81%.
AUGMENTED REALITY BASED HUMAN DIGESTIVE ORGAN LEARNING APPLICATION WITH MULTI MARKER METHOD

Technology has been used by all ages, including children, because technology can be applied in many fields, including education. In education, there is learning about the human digestive organs, which is still taught in the conventional way because they use learning media such as books, but it is still lacking for students to visualize the organs. Efforts are needed to solve this problem by developing learning media that apply augmented reality technology in the form of learning applications. The learning application developed is called ORCEMAR, which has two menus, namely the learning and evaluation menu. The learning menu uses augmented reality technology with a multimarker, and the evaluation menu is a quick quiz that has a score calculation feature using the fuzzy tsukamoto algorithm. Based on alpha testing, the multimarker method was successfully implemented, and the features and components of the application worked well. The application is compatible with Android 5.1 to Android 10, with a minimum camera resolution of 13 MP and 2GB RAM. Based on beta testing collected through questionnaires, the percentage of 40 respondents who are 5th grade elementary school students who answered that the components and features of the application work well above 50%, and also more than 50% of the respondents answered "yes" to the question asking if the application can increase interest, understanding, and also The application can be a complement to conventional learning media to study human digestive organs. Based on this research, the ORCEMAR application was successfully developed and the multimarker method was successfully implemented, as well as features and components that functioned well and received a good response from the target audience.
WEB-BASED EXPERT SYSTEM TO DIAGNOSE OVARIAL CYST DISEASE USING CERTAINTY FACTOR METHOD

Ovarian cysts are one of the most common forms of reproductive disease affecting women. Cyst or tumor is a form of abnormality that can be regarded as a benign growth of smooth muscle cells in the ovaries. Cysts are usually harmless but do not rule out the possibility of a malignant cyst or even turn into cancer. The thing that makes ovarian cysts dangerous is when they burst, are very large, or block the blood supply to the ovaries. Lack of knowledge of the general public about the symptoms that cause ovarian cyst disease makes it too late to detect this disease early so it is slow in handling, there are even some cysts or tumors which when they become malignant are only detected as having ovarian cysts, as well as unhealthy lifestyles of today's society such as consuming alcohol, fast food, causing the body to produce more chemicals. To overcome this problem, the design of a web-based expert system to diagnose ovarian cyst disease using the certainty factor method is made to assist the public or users in diagnosing through the symptoms they feel. The method used to diagnose ovarian cyst disease is the Certainty Factor method. From the calculations that have been inputted by the user, the results obtained are 97% confidence that the patient is likely to be diagnosed with cystadenoma ovarii mucinosum. With this web-based expert system program, it is hoped that the general public or users can diagnose ovarian cyst disease through the symptoms felt so as to minimize the possibility of the cyst becoming malignant.
COVID-19 DIAGNOSIS EXPERT SYSTEM WITH CERTAINTY FACTOR METHOD

Covid-19 is a new type of virus that is currently a pandemic in almost all countries. The limitations of experts for diagnosis and recommending rapid action to patients who are diagnosed positively, as well as the limitations of the expert area in the environment wh ere the Covid-19 outbreak occurred. This expert system aims as an early diagnosis system for Covid-19 so that patients can receive treatment immediately, and help stop the wider spread of the virus. Using predetermined parameters and there is also a system that can later make it easier for users to find out the diagnosis of Covid-19 caused by the coronavirus. The method used for this expert system research is Certainty Factor (CF). This method aims to showed how much the confidence value is based on the clinical parameters given by MYCI. The advantage of the Certainty Factor method is that it can be measured something that is certain or uncertain. And to maintain accuracy, Certainty Factor can only process two data. The Expert System uses the Certainty Factor (CF) method to diagnose Covid-19 disease, starting with the collection of symptom data and giving confidence values by the expert on each symptom, then the symptom data input stage from the user and the system will calculate based on the Certainty Factor rule, multiplying the weight value namely user CF and expert CF, combining the results of the multiplication of each symptom, so that a percentage of confidence is obtained as the final diagnosis result. After calculating the accuracy test of the 3 rule system with 99% positive Covid-19 results, 48% most likely Covid-19 and 29% negative Covid-19.
MODIFICATION ADVANCED ENCRYPTION STANDARD (AES) ALGORITHM WITH PERFECT STRICT AVALANCHE CRITERION S-BOX

The Advanced Encryption Standard or better known as the AES Algorithm is a standard algorithm and has been widely used as an application of cryptography. Currently, a lot of research is developing about attacks on the AES algorithm. Therefore, there have been many studies related to modifications to the AES algorithm with the aim of increasing the security of the algorithm and to produce alternatives to encryption algorithms that can be used to secure data. In this study, modifications were made to the AES algorithm by replacing the S-box using the perfect SAC S-box in the SubBytes process. The Perfect SAC S-box has an exact SAC average value of 0.5. The S-box that will be used must have good security strength, therefore the perfect SAC S-box is tested, namely the AC, SAC, BIC, XOR Table Distribution, and LAT Distribution tests. Based on the results of the study, it was found that the perfect SAC S-box had almost the same S-box test results as the AES S-box. Furthermore, after the perfect SAC S-box is applied to the AES algorithm, it is analyzed how the effect of these modifications on the AES algorithm uses randomness testing for the block cipher algorithm, namely the strict avalanche criterion (SAC) test. The results of the AES test with perfect SAC S-box can meet the SAC test since the second round with better results than the original AES algorithm with SAC values of 0.5003 and 0.5019.
KNOWLEDGE BASED RECOMMENDATION MODELING FOR CLOTHING PRODUCT SELECTION RECOMMENDATION SYSTEM

One of the markets which is the largest wholesaler of clothing in Central Java is the Klewer market area and its surroundings. The Simple Inc Store is one of the clothing stores in the area. Simple Inc Store is a large kiosk that sells clothing products in the form of various types of t-shirts, shirts, jackets, sweaters and pants. Sales of products in these stores are still done conventionally, namely customers come directly to the store to choose and buy products. The number of clothing products that are sold makes customers experience difficulties in the process of selecting clothing products. Therefore, it is necessary to develop a recommendation system that can assist customers in choosing clothing products. The purpose of this research is to create a Knowledge Based Recommendation model for the Recommendation System for Selection of Clothing Products. The system development method used in this research is Rapid Application Development (RAD), which consists of stages, namely business modeling, data modeling and process modeling. The recommendation system method used in this research is knowledge based recommendation. Knowledge based recommendation has the advantage of being able to set the level of user priority based on the user's needs for the product by calculating the similarity value between customer needs and clothing product attributes. Knowledge based recommendation modeling for this clothing product selection recommendation system can provide 5 choices of clothing product search attributes, namely brand, price, material, color and size. Based on the results of modeling the knowledge based recommendation method with 20 sample data, it can provide recommendations for clothing products based on the criteria needed by customers by calculating the similarity value between customer needs and the attributes possessed by each clothing product. Clothing products with the highest similarity value will be displayed as a result of clothing product recommendations, namely the highest similarity value of 0.6 is obtained for Maternal t-shirts. The results of this knowledge-based recommendation modeling can be used as a reference in developing a recommendation system for the selection of clothing products.
APPLICATION OF DATA MINING FOR PREDICTION OF LONG COVID ON COVID-19 SURVIVAL WITH FEATURE SELECTION AND NAÏVE BAYES METHOD

Since it was declared a global pandemic in March 2020, Corona Virus Disease (Covid-19) has become the world's attention, a lot of research has focused on all things related to Covid-19. Covid-19 is an infectious disease caused by the acute respiratory syndrome Corona virus 2 (SARS-CoV-2). Several studies have shown that the symptoms of COVID-19 persist for a long period of time even though they have been declared cured of Covid-19, this is known as Long Covid. Complaints that are often experienced by patients who progress to Long Covid are fatigue, headaches, coughs, runny noses, sleep disturbances and even shortness of breath. Several risk factors for the occurrence of Long Covid include age, gender, patient congenital disease, condition during acute infection, ethnicity and the patient's Body Mass Index (BMI). To anticipate the occurrence of long covid, it is necessary to have a risk prediction system for the occurrence of long covid in covid-19 patients, this aims to anticipate and prepare for early handling and prevention efforts for covid-19 patients who are at risk of experiencing long covid. Prediction of the risk of long covid can be done by classifying long covid risk factors by utilizing data mining. The purpose of this study is to classify symptom data and patient history, so that data patterns can be obtained that can be used as predictions to estimate the risk of the occurrence of Long covid-19 in Covid-19 survivors. This study uses the Naïve Bayes classification method by classifying data based on the Long covid risk factor and the feature selection information gain method which is used as a technique in attribute selection to optimize the nave Bayes algorithm. The results of this study have a real contribution to the development of science and technology. The concept of the resulting prediction data pattern can be used as a reference in developing early detection of the risk of the occurrence of Long Covid.
DESIGN AND BUILD INFORMATION SYSTEM FOR BPJS POLYCLINIC CLAIM FILE COMPLETENESS AT MUHAMMADIYAH HOSPITAL BANDUNG

Completeness of medical records is vital and important in the BPJS claim process in accordance with what is stated in the practical guidelines for administering and claiming BPJS health facilities. This study aims to design an information system for the completeness of the BPJS Polyclinic claim file at the Muhammadiyah Hospital in Bandung. This study uses a qualitative analysis method with a descriptive approach and waterfall for the design development method. The Waterfall method has a clear flow, so errors can be reduced, and makes project work more detailed. The data collection technique used is by means of observation, interviews, documentation and data analysis. Some of the problems found during the observation were incomplete medical records, the process of analyzing the completeness of medical records was done manually, not reading the diagnosis written by the doctor on the medical resume, the absence of a doctor's signature, and the absence of a photocopy of the patient's identity card, for example a photocopy of ID card and photocopy of BPJS card so that there is a delay in the claim process. Therefore, an information system is needed to support the completeness of medical records so that the BPJS claim process runs correctly and smoothly. The information system that the researcher built helps PMIK in carrying out BPJS claim activities.
WEBSITE NETWORK AUTOMATION DESIGN AND IMPLEMENTATION IN RT RW NET SENDEN DUSUN MAGELANG WITH DJANGO FRAMEWORK

RT RW NET is part of the Internet facilities owned by the hamlet of Senden Magelang, whose purpose is to provide cheap Internet facilities.However, behind the existing facilities several problems emerged, based on a statement from the hamlet of sending that the problem that arose was the configuration process for adding new users, which took a long time due to manual configuration in each RT (Rukun Tetangga) and RW (Rukun Warga) alley. Therefore, in this study, a website-based network automation application was created using the Django framework and developed using the Waterfall method, thus providing easy access and fast configuration when configuring new users. After designing the network automation website, the website will be operated by the network administrator, who hopes to simplify and shorten the time for new user configurations.
IMPLEMENTATION OF MARKET BASKET ANALYSIS WITH APRIORI ALGORITHM IN MINIMARKET

The rapid growth of the retail business has an impact on increasing the economic growth of the community. The retail business has high profit potential in areas that have a large population such as Indonesia. A retail business that is popular among the public is a modern market retail business or convenience store. With the rapid growth, it gives a tendency between convenience stores to compete. By designing a marketing strategy is one of the efforts to win the competition in supermarkets. Management needs to understand the purchase behavior made by customers, this action is useful to find out the products that customers are popularly buying. Association algorithm is a form of algorithm in the field of data mining that serves to provide correlation between one item and another. there are several popular algorithms in applying association algorithms one of which is the a priori algorithm created by Agrawal and Srikant in 1994. To support the understanding of customer purchase patterns, it is necessary to implement market basket analysis that has the ability to recognize pattern patterns from transaction data in a convenience store. Performance in market basket analysis also needs to be tested to handle a lot of transaction data, considering that the recording of sales transaction data continues to run over time. The implementation carried out using flask is one of the implementations that is relevant to technological developments, this implementation results in a relatively short data speed with the factor that the magnitude of transaction data is middle to lower, which is 14,963 transaction data.