Reviewerly/exHarmony huggingface dataset:
Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique

Text document were transmitted over the internet for the text communication. So they were occurred many problems like repeated text occurred because of same data were provided in the internet. To characterize and extracting that is a most critical task for the researchers. Many researchers were characterized and applied in many fields like real-life scenarios, such as real-time monitoring on abnormal user behaviors, etc. In this case to detect and characterize the personalized behavior of the user were provide some drawbacks. To solve this problem, this paper analyzing the sequential data and characterize the user behavior with the help of the data mining sequential pattern matching algorithm.
SISTEM PAKAR DIAGNOSIS PENYAKIT HIPERTENSI DENGAN MENGUNAKAN METODE FORWARD CHAINING DAN TOERI PROBABILITAS

At this time hypertension is a disease that endangers the community a lot because people often ignore it because this disease does not quickly affect the body but will slowly damage health. Hypertension is not contagious but can trigger other diseases, such as stroke and heart disease. The habit of people who will check their health after experiencing problems causes delays in their help. Symptoms of hypertension can be detected early so that people can prevent the seriousness of the disease. The expert system is one of the fields of science that can help the public to check whether a person has hypertension and the type of hypertension and the percentage of possible diseases he suffers because the expert system can be designed how to work like an expert. The expert system designed uses several stages, namely (1) system requirements analysis, (2) input and output design, (3) coding and (4) testing. This expert system will be represented in the form of rules, the method of reasoning using the forward chaining method and probability theory in calculating the percentage of rules that are fulfilled. The form of output (results) from the expert system is in the form of diagnostic results based on the symptoms given and suggestions that are in accordance with the diagnosis used are forward chaining, namely drawing conclusions from several types of hypertension obtained from symptoms. The output of the system is the type of hypertension diagnosed and the solution for the diagnosis. Keyword : Sistem Pakar, Forward Chaining, Hipertensi, Diagnosa
Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

Georeferencing a kinematic Multi-Sensor-System (MSS) within crowded areas, such as inner-cities, is a challenging task that should be conducted in the most reliable way possible. In such areas, the Global Navigation Satellite System (GNSS) data either contain inevitable errors or are not continuously available. Regardless of the environmental conditions, an Inertial Measurement Unit (IMU) is always subject to drifting, and therefore it cannot be fully trusted over time. Consequently, suitable filtering techniques are required that can compensate for such possible deficits and subsequently improve the georeferencing results. Sometimes it is also possible to improve the filter quality by engaging additional complementary information. This information could be taken from the surrounding environment of the MSS, which usually appears in the form of geometrical constraints. Since it is possible to have a high amount of such information in an environment of interest, their consideration could lead to an inefficient filtering procedure. Hence, suitable methodologies are necessary to be extended to the filtering framework to increase the efficiency while preserving the filter quality. In the current paper, we propose a Dual State Iterated Extended Kalman Filter (DSIEKF) that can efficiently georeference a MSS by taking into account additional geometrical information. The proposed methodology is based on implicit measurement equations and nonlinear geometrical constraints, which are applied to a real case scenario to further evaluate its performance.
AN ERROR ANALYSIS OF USING SIMPLE PRESENT TENSE IN DESCRIPTIVE TEXT WRITTEN BY THE EIGHTH GRADE STUDENTS OF SMP BINA SATRIA MEDAN

This research is aimed to find out types of error analysis of using simple present tense in descriptive text written by the eighth grade students of SMP Bina Satria Medan. This research used the qualitative research design. The sample of this research was the eighth grade students of SMP Bina Satria Medan which consisted of 20 students. The result of the data analysis showed that there were the types of errors made by the students on writing descriptive text based on surface strategy taxonomy which are classified into omission, addition, misformation, and misordering. The types of errors made by the students on writing descriptive text were 25% in errors of omission, 22,4% in errors of Addition, 49,2% in errors of Misformation, 3,4 % in errors of Ordering. From this data, it can be said that the misformation error was the highest number of errors made by the students in 49,2%.There were some causes of errors made by students in misformation. Firstly, the students felt confused with tense formula, they still could not distinguish regular and irregular verbs, which are present tense and past tense. Secondly, the students made mistakes in arranging word or confused to select the word sequence in sentence which important to formation writing that meanwhile to arrange the word and group of words in English and Indonesia are different. Thirdly, The students made errors because they didn’t know grammar rule, they were lazy to learn about tense, they couldn’t interpret sentences or paragraph directly and many other reasons. Therefore, it can be stated that source of the errors made by students influenced by interlingual and intralingual transfer(Corder, 2000). Interlingual transfer is errors that are influenced by the interference of users’ mother tongue. The users still apply Indonesian pattern into English when they translate the sentences. Then Intralingual transfer occurs when students get difficulty learn the second language. Three causes of errors in intralingual transfer, namely; errors caused by over-generalization, errors caused by ignorance of rule restriction, and errors caused by incomplete application of rules.
ASSESSING TOURISM STUDENTS’ ENGLISH PROFICIENCY COMPETENCE BASED ON ASEAN COMMON COMPETENCY STANDARD

This study is aimed to analyze the tourism students’ English proficiency competence based on ASEAN Common Competency Standard (ACCS). The study used a descriptive qualitative method and the data were gained through English competency based workplace assessment for the students in three sub divisions at PDD Lombok Barat; hotel accommodation, culinary, and hotel engineering. The findings of the study showed that more than half of tourism students are still in the category of “low competence” in attaining English competencies based on ACCS.
Comprehensive Review on Effectual Information Retrieval of Semantic Drift using Deep Neural Network

Semantic drift is a common problem in iterative information extraction. Unsupervised bagging and incorporated distributional similarity is used to reduce the difficulty of semantic drift in iterative bootstrapping algorithms, particularly when extracting large semantic lexicons. In this research work, a method to minimize semantic drift by identifying the (Drifting Points) DPs and removing the effect introduced by the DPs is proposed. Previous methods for identifying drifting errors can be roughly divided into two categories: (1) multi-class based, and (2) single-class based, according to the settings of Information Extraction systems that adopt them. Compared to previous approaches which usually incur substantial loss in recall, DP-based cleaning method can effectively clean a large proportion of semantic drift errors while keeping a high recall.
Sensitivity of the General Linear Model to Assumptions

Non-Gaussian noise often causes in significant performance abatement for systems which are designed using Gaussian assumption. This report challenges the question of General Linear Model with White Gaussian Noise assumption in order to define the sensitivity of the performance of an optimal estimator. Gaussian noise models provide an important role in many signal processing applications. The Laplacian and Uniform signal are two worthy examples of noise that can be compared to the White Gaussian Noise, though the sensitivity which can be compared with any non-Gaussian. White Gaussian Noise has been considered for General Linear Models and deviation from whiteness would affect on our estimates under different circumstances. Moreover, new assumptions have been considered to generate different type of signals in order to evaluate the sensitivity of the General Linear Model.
Analisis dan Implementasi Algoritma Asimetris Dual Modulus RSA (DM-RSA) pada Aplikasi Chat

The RSA algorithm is one of the cryptographic algorithms with an asymmetric model where the algorithm has two keys, namely the public key and the private key. However, as time goes on, these algorithms are increasingly exposed to security holes and make this algorithm vulnerable to being hacked by people who do not have authority. The vulnerability stems from the algorithm's public keys (e and n). The strength of the RSA algorithm is based on the difficulty of factoring two prime numbers that are generated during the key generation process, if these values ​​can be known using certain methods, the public key and private key values ​​will be found. Therefore, there are many studies that improvise the RSA algorithm, one of which is the Dual Modulus RSA (DM-RSA) algorithm. The algorithm uses four prime numbers which produce 2 modulus and 4 keys (2 public keys and 2 private keys). From the results of the Kraitchik factorization test, it was found that the DM-RSA algorithm was proven to be more resistant up to 2 times or even more than the standard RSA algorithm. This is evidenced by the fact that the value of n is 24 bits, the RSA algorithm can last up to 63204 ms (1 minute 22 seconds) while the Dual Modulus RSA algorithm lasts up to 248494123 ms (142 minutes 47 seconds).&#x0D;
OPTIMASI KETAHANAN WATERMARKING AUDIO DIGITAL MENGGUNAKAN ALGORITMA RSA DAN MSB

Copyright is a serious problem in the digital world, the process of sending and distributing digital media is so easy nowadays, copyright in the digital world is very detrimental to those who feel that their digital rights are copied and pasted or taken without the consent of the creator. Therefore we need a way where when a digital file can be identified as original as a product, one of the right ways is to use a watermark technique. But often this watermark process can be lost or cannot be extracted because the digital file has gone through a compression process, duplicate, or something else. So in this study, it will be tried to increase the durability of a watermark in digital audio as a solution for identifying copyrighted digital works. Where the watermark process will use the RSA and MSB algorithms to enter information into a digital audio file, later this information can be extracted to view copyright ownership information from the digital audio. And it is hoped that this watermarking is resistant to various digital audio processes such as compression, duplication, and editing carried out on the file. the information that is inserted into is maintained without compromising the quality of the digital audio.
Bio-metric Encryption of Data Using Voice Recognition

In symmetric cryptosystems, the protection of secret keys is based on the traditional user authentication and likewise the security of the cryptosystem depends on the secrecy of the secret keys.In the event of lost, theft or infection of these secrete keys; the security of the cryptosystems would be compromised hence exposing critical information.Biometrics has been commercially used to verify user's identity.Voice biometrics has been proven to be even more effective because it cannot be stolen in some cases like face, fingerprint or even iris biometrics.The research proves that a well-designed system will prompt an authentication question and on verification user must provide both the desired answer as well as desired matching threshold or the system ignores the user features.This research proposes a software-based architecture solution for Biometric Encryption of data using Voice Recognition that employed the Dynamic Time Warping (DTW) technique to solve the problem of speech biometric duration varying with non-linear expansion and contraction.The approach then used database to store the monolithically bind cryptographic key with the equivalent biometric hardened template of the user in such manner that identity of the key will stay hidden unless there is a successful biometric authentication by intended party.The research used the MIT mobile device speaker verification corpus (MDB) and A data set in quiet environment (QDB) for training and verifying session.Finally using the Equal Error Rate (EER) the research evaluated performance or rate at which False Acceptance Rate (FAR) and a False Rejection Rate (FRR) are equal.Therefore, according to the result it offers a better substitute method of user authentication than traditional pre-shared keys for benefit of protecting secret keys.
PENINGKATAN INTERAKSI BELAJAR MATEMATIKA MATERI TRIGONOMETRI MELALUI METODE QUANTUM LEARNING PADA SISWA KELAS X.2 SMA DDI MAROS

This study aims to increase the interaction of learning mathematics with trigonometry through the use of the Quantum Learning method in class X.2 SMA DDI Maros.&#x0D; This type of research is Classroom Action Research using the Kemmis and Mc Taggart cycle model. The subjects of this study were students of class X.2 SMA DDI Maros for the academic year 2020/2021. This classroom action research was carried out in 2 cycles. Data collection techniques using observation and tests. The data analysis used is descriptive qualitative and descriptive quantitative. The method used in this study is the Quantum Learning learning method with the research success criteria the percentage of student learning interactions reaches 75%.&#x0D; The results of the study indicate that the Quantum Learning method can improve the learning interactions of class X.2 students of SMA DDI Maros. The learning is using the TANDUR strategy in the form of facilities from the teacher so that students can grow the strength of AMBAK, get learning experiences, name materials, demonstrate results, repeat material, and celebrate success. In the pre-action or initial score, the average student learning interaction in mathematics obtained a percentage of 74.91%. After being given the action, it increased to 78.47% at the end of the first cycle, and at the end of the second cycle, it increased to 87.78%.
Sistem Informasi Setoran Wajib Jemaat Menggunakan Framework Codeigniter

Klasis GKI Sentani is part of the GKI Denomination which regulates churches in the Jayapura (Sentani) Regency area. One of the tasks of the Klasis is the monitoring and evaluation of the Setoran Wajib Jemaat (SWJ). Each Church is required to collect offerings cumulatively each month. The obstacle faced in financial management in the Klasis Sentani environment is the budget processing system which is manual, where donations are deposited to Klasis by attaching proof of deposit. this makes the process of collecting donations an inefficient routine. Including the documentation process that looks not up-to-date and the difficulty in publishing the results to the congregation at Klasis Sentani. This study aims to facilitate the reporting of mandatory deposits for the classic GKI Sentani congregation, namely by creating an information system for the congregation's mandatory deposit. The development of the software system uses the Waterfall method, with the Unified Modeling Language as the system modeling language. This system has been implemented and makes it easier to manage finances for the church treasurer and classics. Financial information can also be viewed transparently on the page provided and can be accessed by the congregation.
Determining the Hausdorff Distance Between Trees in Polynomial Time

The Hausdorff distance is a relatively new measure of similarity of graphs. The notion of the Hausdorff distance considers a special kind of a common subgraph of the compared graphs and depends on the structural properties outside of the common subgraph. There was no known efficient algorithm for the problem of determining the Hausdorff distance between two trees, and in this paper we present a polynomial-time algorithm for it. The algorithm is recursive and it utilizes the divide and conquer technique. As a subtask it also uses the procedure that is based on the well known graph algorithm of finding the maximum bipartite matching.
Granska API – an Online API for Grammar Checking and Other NLP Services

We present an online API to access a number of Natural Language Processing services developed at KTH. The services work on Swedish text. They include tokenization, part-of-speech tagging, shallow parsing, compound word analysis, word inflection, lemmatization, spelling error detection and correction, grammar checking, and more. The services can be accessed in several ways, including a RESTful interface, direct socket communication, and premade Web forms. The services are open to anyone. The source code is also freely available making it possible to set up another server or run the tools locally. We have also evaluated the performance of several of the services and compared them to other available systems. Both the precision and the recall for the Granska grammar checker are higher than for both Microsoft Word and Google Docs. The evaluation also shows that the recall is greatly improved when combining all the grammar checking services in the API, compared to any one method, and combining services is made easy by the API.
Impact of Model Architecture Against Adversarial Example's Effectivity

The purpose of this study is to gain an understanding of the impact of model architecture on the efficacy of adversarial examples against machine learning systems implemented in self-driving applications. Prior research shows how to create and train against adversarial examples in many use cases; however, there is no definite understanding of how a machine learning model’s architecture affects the efficacy of adversarial examples. Data was collected through an experimental setting involving end-to-end self-driving models trained through behavioral cloning. Three model types were tested based on popular frameworks for machine learning algorithms dealing with images. Results showed a statistically significant difference in the impact of adversarial examples between these models. This means that certain model types and architectures are more susceptible to attacks. Therefore, the conclusion can be made that model architecture does impact the efficacy of adversarial examples; however, this is potentially limited to closed-loop, end-to-end systems in which algorithms make the entire decision. Future research should investigate what specific structure within models causes increased susceptibility to adversarial attacks.
Evaluation of Random Number Generator Functions Using Statistical Analysis

Most programming languages have in-built functions for the sole purpose of generating pseudo-random numbers. This manuscript is aimed at analyzing the appropriateness of some of these in-built functions for some basic goodness-of-fit statistical tests for random number generators. The document is divided into four sections. The first section gives a broad introduction about randomness and the methods of generation of pseudo-random numbers. Section two discusses the statistical tests that were employed for testing the built-in library functions for random number generation. This section is followed by an analysis of the data collected for the various statistics in the third section, and lastly, the fourth section presents the results of the data analysis.
Quantitative Analysis Method of College English Curriculum Based on Blended Teaching

The development of industry is inseparable from the improvement of technical personnel's English level. Nowadays, College English classroom is inefficient. Various experts and scholars start from changing the teaching content, teaching methods and teachers' position in the classroom to solve the practical problems. No matter what aspect of the reform has promoted the further development of College English teaching, but it has not completely solved the practical problems in College English classroom. This paper proposes a quantitative teaching model of College English classroom performance to stimulate students' enthusiasm. Through the combination of qualitative analysis and quantitative analysis, this paper analyzes the teaching practice results of College English curriculum integration. The research shows that the integration of College English curriculum and multi teaching education can effectively improve students' English literacy. In the current environment, it is an effective way for colleges and universities to carry out media literacy education. The study also reflects the low level of students' English literacy, which needs to be paid attention to.
Identifying Fake News: A Lesson from Library Science Students

This study aims to determine the level of the Library Science student’s awareness at UIN Sunan Kalijaga Yogyakarta to prevent spreading fake news from the internet. This was carried out by identifying fake news with indicators by the International Federation of Library Associations (IFLA). This research is a quantitative descriptive study. The data were obtained through questionnaires, interviews, and non-participative observation. The grand mean was used to conclude the data from the collected questionnaires. The results showed that the students' awareness of fake news was included in the high category, with a score of 3.77 from grades 1 to 5. Students of Library Science at UIN Sunan Kalijaga had the willingness and ability to recognize hoaxes on the internet, including 1) willingness to ask experts, 2) ability to understand news contexts, 3) willingness to check the sources of the news, 4) ability to read between the lines, 5) willingness to avoid prejudice (being neutral), 6) willingness to check the news production time (date), 7 ) willingness to examine news writers, and 8) willingness to examine supporting sources for news production. Students should implement critical thinking as the main skill for identifying fake news. Information literacy education can be used as a way to get their critical thinking skills.
A Proposed Method for Mining Breast Cancer Pattern Using Particle Swarm Optimization

Breast cancer is one of the leading causes of death among women in many parts of the world. In this paper, we have developed an efficient hybrid data mining approach to separate from a population of patients who have and who do not have breast cancer. The proposed data mining approach has consisted of two phases. In first phase, the statistical method will be used to pre-process the data, which can eliminate the insignificant features. It can reduce the computational complexity and speed up the data mining process. In the second phase, we proposed a new data mining methodology, which based on the fundamental concept of the standard particle swarm optimization (PSO), namely discrete PSO. This phase aimed at creating a novel PSO in which each particle was coded in positive integer numbers and had a feasible system structure. Based on the obtained results, our proposed DPSO can improve the accuracy to 98.71%, sensitivity to 100%, and specificity to 98.21%. When compared with the previous research, the proposed hybrid approach shows the improvement in both accuracy and robustness. According to the high quality of our research results, the proposed DPSO data mining algorithm can be used as the reference for deciding on hospital and provide the reference for the researchers.