Reviewerly/exHarmony huggingface dataset:
Classification and Counting of Vehicle using Image Processing Techniques

Abstract: In this research work, we explore the vehicle detection technique that can be used for traffic surveillance systems. This system works with the integration of CCTV cameras for detecting the vehicle. Vehicle Tracking and Counting Monitoring highway traffic is an important application of computer vision research. In this paper, we analyze congested highway situations where it is difficult to track individual vehicles in heavy traffic because vehicles either occlude each other. In this we focus on the issues to propose a viable solution and we apply the vehicle detection results to multi object tracking and vehicle counting. Vehicle detection and vehicle type recognition is a practical application of machine learning concepts and is directly applicable for various operations in a traffic surveillance system contributing to an intelligent traffic surveillance system. This paper will introduce the processing of automatic vehicle detection and recognition using static image datasets. Further using the same technique, we shall improvise vehicle detection by using live CCTV surveillance. The surveillance system includes detection of moving vehicles and recognizing them, counting number of vehicles.
Univariate Time Series Analysis of Cryptocurrency Data using Prophet, LSTM and XGBoost

Abstract: Cryptocurrency, also known as crypto, is any digital or virtual currency that uses cryptography to safeguard transactions and circulates without the authority of a central bank. Bitcoin, the first and most widely used decentralized cryptocurrency, was introduced in 2009. After a few years of unrivalled dominance, it lost its monopoly in 2011, when the first competitive alternative currencies arose. As of November 2022, there are almost 21,000 cryptocurrencies in circulation. Because there is no government credit backup, cryptocurrency prices are typically volatile. The cost of one Bitcoin rose from zero at its debut in 2009 to $13 in 2013 and then to $68789 in 2021, with numerous shifts and fluctuations along the way. The accurate forecasting of the Bitcoin price is critical for investors to make decisions and for governments to create regulatory laws. This paper examines the ability of the models - Prophet, Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost) to predict the price of Bitcoin reliably. Using the performance metrics like RMSE, each model was thoroughly trained and tested to discover which one operates more efficiently. After examining the price of Bitcoin from 2012 to 2021, we concluded that the Long Short-Term Memory (LSTM) model proves to be the most efficient when dealing with variable and difficult-topredict data such as Bitcoin values since it portrays promising results in comparison
Determination of strain rate dependence at intermediate strain rates using acceleration information

Precise stress-strain characteristics of materials for intermediate strain rates need to be utilized for analyzing various events in industry such as metal forging, sheet metal forming in manufacturing processes and automotive crash tests. However, the accurate evaluation of the load is not easy at intermediate or high strain rates, owing to the inertial effect. The present study aims at characterizing the hardening behavior using acceleration data without utilizing the load information with an application to a dual-phase DP980 steel sheet sample. Virtual measurements were obtained from a finite element model to check for the minimum acceleration magnitude necessary for stable identification. The same identification procedure as that used for the experiments was adopted. Also, a high-speed tensile testing equipment for steel sheet specimens was modified to increase the acceleration magnitude to implement the proposed methodology experimentally. The virtual fields method was chosen as an inverse tool to determine the strain-rate dependence of the sheet metal specimens. The stress–strain curve of an advanced high-strength steel at intermediate strain rates obtained from the acceleration was compared with the curve from the load data, and promising results were obtained.
A case study of latent growth curve analysis on creativity of Korean adolescent using SEMtree

As the importance of creativity education is emphasized in a rapidly changing society with the advent of the digital era, empirical data are being collected via creativity assessment and survey along with interest in educational environments and_2 teaching and learning methods to enhance creativity. In this study, an empirical analysis was conducted to identify the trend based on the latent growth model and to explore the related variables for the creativity score of Korean middle school adolescents with the longitudinal secondary data having three data points. By applying the SEMtree method, which is a combination of decision tree techniques and structural equation modeling, the main purpose was to provide practical information to general educators by identifying the characteristics of the model-based recursive method, i.e, SEMtree and exploring the possibility of application to actual educational data.
Digital Forensic on Secure Digital High Capacity using DFRWS Method

As evidenced in the trial, between 2015 and the second quarter of 2022, there were 54 cases involving secure digital high capacity (SDHC) storage hardware as evidenced in trials. In 2021 there will be an increase in cases involving SDHC. The three cases with the highest number are corruption cases, special crimes, and ITE. SDHC is an advanced technology development of Secure Digital (SD) card hardware which functions as storage. SD Card only has a capacity of up to 2 gigabytes, while the largest SDHC capacity is 32 gigabytes. As a storage device that is small, thin, and has a fairly large capacity. this research needs to be done because of the increasingly widespread increase in cases involving SDHC. This study aims to perform digital forensic analysis on SDHC evidence using forensic applications that run on Linux, namely foremost and DC3DD. This study uses the DFRWS method to retrieve valid evidence in court. Based on the research conducted, it was found that the number of files that can be restored at the examination stage using foremost is 77%, and the accuracy of recovered files is 50% with string file hash validation. From this research, it can be concluded that the processing results of DC3DD and Foremost can be used as valid evidence.&#x0D;
COMPARISON IN PREDICTING THE SHORT-TERM USING THE SARIMA, DSARIMA AND TSARIMA METHODS

The flow of data and information is growing rapidly and rapidly in various sizes and means which is called Big Data. In the face of a change for the better in the future, a careful analysis and design of a data processing system is needed, in which a predictive framework can formulate the right policy to be one of the efforts to make a good decision. This is one of the appropriate Big Data processing efforts, which can be realized through one of the methods, namely prediction or forecasting is an effort to predict future values or trends as a reference for analyzing conditions in the past. One example of Big Data in the City of Balikpapan, namely the temperature within 2 meters obtained from the NASA satellite published on the website power.larc.nasa.gov. One of the methods used in this research is the ARIMA method and it is developed according to the data used. Based on the data to be used, namely temperature data within a distance of 2 meters in the city of Balikpapan, the development of data processing is carried out to pay attention to three seasonal patterns or the so-called Triple Seasonal ARIMA model. In this study, it can be seen how to build the Triple Seasonal ARIMA model and comparison with alternative models, namely Seasonal ARIMA and Double Seasonal ARIMA, and can see how the results of the Triple Seasonal ARIMA model accuracy when compared with alternative models. The method used in this study is the Seasonal ARIMA, Double Seasonal ARIMA and Triple Seasonal ARIMA methods. The results obtained in this study obtained a comparison of methods in making predictions with a specified time span, the results obtained from the Seasonal ARIMA model that it was very good at predicting a time span of 2 weeks, Double Seasonal ARIMA for a period of 1 month, Double Seasonal ARIMA for a period of 3 months, and Triple Seasonal ARIMA for a period of 6 months.
MULTIVARIATE MULTILEVEL MODELLING TO ASSESS FACTORS AFFECTING THE QUALITY OF VOCATIONAL HIGH SCHOOLS IN SOUTH SULAWESI PROVINCE

This study analyzes the quality of Vocational High Schools (VHS), which have a hierarchical data structure and have more than one response variable. Data gathered for this study is from the Basic Education Data (DAPODIK) in the form of raw data variables of several variables that characterize the quality of VHS and other independent variables in South Sulawesi for four years (2018 to 2021) from the Ministry of Finance Republic of Indonesia (KEMENKEU), and Statistics Indonesia (BPS). The explanatory variable at the regency level consists of four years (2018 to 2021), a multi-year and high-dimensional data structure. Therefore, Principal Component Analysis (PCA) is used to overcome this. The modelling is done by using multivariate multilevel modelling (MVMM) on one-level and two-level structures. This study aims to model the average National Examination and Accreditation scores of Vocational High School in South Sulawesi using MVMM modelling that considers the regency/city level and identifies the factors that influence the average National Examination and Accreditation scores. The results showed that the two-level multivariate model with a random intercept as a hierarchical component was better than the one-level multilevel model based on a minor Deviance Information Criterion (DIC) value. Simultaneously, at the 5% level of significance, variables that contribute significantly to the quality of Vocational High Schools in South Sulawesi Province are produced. The variables that have a significant effect on the quality of Vocational High Schools at the school level are the ratio of the number of students/pupils per study group, the percentage of certified teachers to the number of teachers, the ratio of the number of students/pupils per number of toilets, the ratio of laboratory availability, and the ratio of the availability of supporting rooms. Meanwhile, at the regency level, it was found that the percentage of poverty and Gross Regional Domestic Product (GRDP) had a significant effect on the quality of Vocational High Schools.
Data Mining in Public Security Databases in Belém, Pará, Brazil

Crime is a common social problem faced worldwide that can affect a nation's quality of life and economic growth. With this, the purpose of this paper is to apply data analysis and data mining techniques in public security databases in the city of Belém of Pará, in order to discover hidden patterns and assist security managers in the development of new public policies to try to reduce crime rates. Through this study, it was possible to obtain results that can help public security authorities to understand crime, as well as in making decisions about new security policies.
Preposition Mistakes in English for Specific Purposes: The Case of Indonesian Accounting Study Program Students

Prepositions remain challenging for learners of English as a foreign language. Linguistically speaking, prepositions are part of grammar and are frequently used in speaking, listening, reading, and writing. Thus, this research investigated the mastery of English prepositions of undergraduate accounting program students of batch 2021 at a private university in Yogyakarta. The data of this quantitative research were collected through a close-ended questionnaire using Google Forms distributed to 23 students. The collected data were analyzed to identify preposition mistakes. Results showed three common problems of prepositions, namely the addition of unnecessary prepositions, omission of prepositions, and misuse of prepositions. The findings would assist students, thesis advisors, and teachers in using English prepositions grammatically, particularly in English for Specific Purposes (ESP). It is expected that future researchers utilize larger data sets to obtain more robust results of proper uses of prepositions and future researchers are expected to observe the participants directly to maximize the results of the data.&#x0D; &#x0D;
Comparison of the K-Nearest Neighbor Method and the Naive Bayes Method in Classification of Eligibility for Lending

The Kemala Aman Finance Bengkulu Cooperative also provides loan facilities for consumers, but not all loan applications will be approved. So far, the determination of the feasibility of applying for a loan is seen from several aspects including marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, number of loan applications, length of loan application. These aspects are analyzed manually by the survey team by filling out the form provided, and then the survey results are given to superiors to be followed up on whether the loan application is accepted or rejected. The loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative is used to make it easier to determine the eligibility of prospective borrowers to be given loans based on marital status, number of dependents, age, last education, occupation, income, home ownership, collateral, number of loan applications, length of loan application. This application is made using the Visual Basic .Net programming language which can be accessed by the Kemala Aman Finance Bengkulu Cooperative Admin. Comparative analysis of the K-Nearest Neighbor method and the Naive Bayes method was carried out by looking at the level of accuracy by comparing the classification results of the two methods with the real data from the classification results obtained from the Kemala Aman Finance Cooperative Bengkulu. Based on the processing time, the KNN method is faster than the Naive Bayes method. Based on the level of accuracy, the KNN method has the highest level of accuracy compared to the Naive Bayes method. Based on the tests that have been carried out, the functionality of the loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative runs as expected, and the application is able to display the results of the classification of loan eligibility through the KNN Method and the Naive Bayes Method.
Web-Based Information System For Tourism Promotion In Rindu Hati Village Taba Penajung District Bengkulu Central Regency

Website is an internet service that can display multimedia in the form of (data, text, still or motion pictures, animation, sound, video, or a combination of all of them, both dynamic and static which form an interrelated circuit where each is linked. with page networks (hyperlinks). The website itself has many benefits, one of which is for the promotion of pre-tourism in certain areas. The pre-tourism sector is an important factor for regional and state income because it can provide benefits for the infrastructure that supports it. Rinduhati Village Tourism this is still not well publicized, a website-based tourism information system in the tourist village of Rinduhati Village is indispensable as a medium and means of delivering information to the public or tourists who want to know about tourist objects in Rinduhati village, be it natural tourism. The purpose of this study is to how to design a website for tourism information systems in the tourist village of Rinduhati village based on the web as a promotional media. The research instrument uses a waterall model which includes analysis, design, coding, and testing. This study uses a qualitative research approach. And the object of research is the tourist village of Rinduhati village which is located in Taba Penanjung District, Central Bengkulu Regency. With the existence of a website-based information system for the promotion of tourist villages in Rinduhati village, it is hoped that it can help the Rinduhati village community to promote tourism villages so that people are more aware and interested in doing nature tourism in Rinduhati village.
Analisis sistem informasi akuntansi pembelian di Pt. Suryamustika

This study aims to determine the effect of purchasing components, supporting documents, document writing, and internal management on purchasing accounting information systems. This study used a quantitative method with a purposive sampling technique for 102 employees who are still actively working at PT. Suryamustika. The primary source of research data is primary data obtained from questionnaires. This study uses multiple linear regression analysis with the help of SPSS version 19. The results showed that the purchasing components, supporting documents, document writing, and internal management had an influence on the purchasing accounting information system at PT. Suryamustika. So it is important for companies to improve their accounting information system, which was previously done manually but is now digital because of increasingly advanced technology, so that each purchase order can be made faster and the data provided is also correct according to the updated data.
Video Super-Resolution with Regional Focus for Recurrent Network

Video super-resolution reconstruction is the process of reconstructing low-resolution video frames into high-resolution video frames. Most of the current methods use motion estimation and motion compensation to extract temporal series information, but the inaccuracy of motion estimation will lead to the degradation of the quality of video super-resolution results. Additionally, when using convolution network to extract feature information, the number of feature information is limited by the number of feature channels, resulting in poor reconstruction results. In this paper, we propose a recurrent structure of regional focus network for video super-resolution, which can avoid the influence of inaccurate motion compensation on super-resolution results. Meanwhile, regional focus blocks in the network can focus on different areas of video frames, extract different features from shallow to deep layers, and skip-connect to the last layer of the network through feature aggregation to improve the richness of features participating in the reconstruction. The experimental results show that our method has higher computational efficiency and better video super-resolution results than other temporal modeling methods.
n-Mating Effect on Genetic Algorithm-Based Clustering Performance for Hotspots Data

This study aims to explain the effect of variations in the number of individuals mated with father (n-mating) on the performance of Genetic Algorithm Polygamy (GAP) clustering. GAP clustering is clustering method based genetic algorithm. The steps of this method are same as GA clustering steps, but the crossover process is done with polygamy. One selected father is mated with more than one mother. We evaluate the performance of GA-based clustering for hotspot data with three clustering evaluations, namely Sum Square Error, Davies-Bouldin Index, and Silhouette Coefficient. Based on experimental result, GA Polygamy clustering outperforms GA clustering based on Sum Square Error (SSE) evaluation and Silhouette Coefficient (SC) evaluation. The n-mating in the crossover process of GAP clustering affects GAP clustering performance also performance of time to convergence of GAP clustering.
Application of Ceemdan and Wavelet Threshold Denoising in Perimeter Security of Fiber Grating

To improve the observability of noise in distributed optical fiber perimeter security system, a denoising algorithm based on CEEMDAN and wavelet transform is proposed. The CEMMDAN is used to decompose the noise signal, and the noise signal and the intrusion signal are decomposed. The wavelet transform is used to denoise and remove the Gaussian noise component. Finally, the CEEMDAN and wavelet denoising are combined to remove the noise, and the denoising signal in line with the subsequent steps of demodulation is obtained. Experiments show that the proposed algorithm can effectively remove Gaussian noise and effectively reduce the interference noise and inherent electronic noise in the optical fiber perimeter security system.
IMPLEMENTATION OF A WEB-BASED VALUE MANAGEMENT INFORMATION SYSTEM AS AN EFFORT TO DIGITALIZE THE ACADEMIC SECTOR

The research was conducted in the implementation of a web-based Achievement management information system, where in this system there are 3 actors, namely admin, teacher and principal. The admin actor is in charge of managing data for teachers, students, classes, subjects, grades, extracurriculars, OSN and a list of recommendations for students with academic and non-academic achievements. In the homeroom teacher actor manages student data and grades and prints report cards. While the principal actor can see data on teachers, classes and subjects. With this system, it is hoped that it will make it easier to manage report cards and achievements. Then the homeroom teacher can only enter the grades of students who are only in their class. Subject teachers do not need to test students or assess students manually because the system will recommend students based on subject scores from daily grades, UTS and UAS with a weighted score according to the requirements that have been determined by the school. For extracurricular activities supervisors, there is also no need to test students one by one because the system will recommend students based on several aspects of predetermined values ​​for students to represent the school in non-academic competitions such as pencak silat, volleyball, basketball and others. After that the system can automatically recommend students to represent the school in academic (OSN) and non-academic competitions, according to the provisions of the competition criteria held with report cards and student skill scores
KLASIFIKASI MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK DETEKSI AWAL RISIKO DIABETES MELITUS

Diabetes Mellitus is one of the four leading causes of death and therefore possible treatments are of crucial importance to the world leaders. Prevention and control of Diabetes Mellitus are often done by implementing a healthy lifestyle. Thus, both people with risk factors and people diagnosed with Diabetes Mellitus can control their disease in order to prevent complications or premature death.. For a proper education and standardized disease management the early detection of Diabetes Mellitus is necessary, which led to this conducted study about the classification of early detection of Diabetes Mellitus risk by utilizing the use of Machine Learning. The classification algorithms used are the Support Vector Machine and Random Forest where the performance analysis of the two methods will be seen in classifying Diabetes Mellitus data. The type of data used in this study is secondary data obtained from the official website of the UCI Machine Learning Repository consisting of 520 diabetes patient data taken from Sylhet Diabetic Hospital in Bangladesh with 16 independent variables and 1 dependent variable. The dependent variable categorizes the test result into positive and negative Diabetes Mellitus classes. The results of this study indicate that the Random Forest classification algorithm produces a better classification performance on Accuracy (98.08%), Recall (97.87%), Precision (98.92), and F1_Score (88.40%).
Identifikasi Ras Wajah dengan Menggunakan Metode Deep Learning Model Keras

Humans have various types which are called races. The human race can be identified by many things, from the shape of the body to the color of the body. Of the many ways to identify the human race, the author also thinks whether a computer can identify the human race based on skin color. Nowadays, computers have developed in such a way that there are many possibilities that computers can also do this.