Reviewerly/exHarmony huggingface dataset:
Optimisasi Backpropagation Neural Network dalam Memprediksi IHSG

Covid-19 has become a global epidemic and has spread to many countries in the world, including Indonesia. The COVID-19 pandemic is one source of uncertainty that causes financial data to fluctuate and cause data to be volatile. This outbreak had an impact on financial data, not only on the Rupiah exchange rate but also on the Jakarta Composite Index (JCI). The uncertainty of the JCI makes it difficult for investors, data managers, and business people to predict data for the future. JCI is one indicator of the capital market (stock exchange). The uncertainty of the JCI data causes the need for predictions, so that investors, data managers, and business people can make the right decisions so that they can reduce risk and optimize profits when investing. One of the factors causing the JCI's decline was sentiment caused by investor panic over the rapid spread of COVID-19 in various cities in Indonesia. This research uses Backpropagation Neural Network (BPNN) in making predictions and continues with optimization of BPNN using ensemble techniques. Historical data from the JCI used were obtained from yahoo.finance. The ensemble technique used consists of two approaches, namely combining different architectures and initial weights with the same data and combining different architectures and weights. The results of network performance using ensemble technique optimization show good performance and can outperform the individual network performance of BPNN.Â Keywords: prediction, JCI, Optimization, BPNN, volatile
Research on the Performance Style of Baroque Keyboard Music

This Article aimed to study (1) How to understand and interpret the style of Baroque keyboard music (2) What are the performance characteristics of the harpsichord and piano in Baroque keyboard music. To ensure the objectivity, authenticity, and general validity of the findings, the population sample for this study was selected from piano students, piano teachers, experts, and scholars in music colleges and universities in different regions of China. Receipts were collected by administering a reliability-compliant questionnaire to population sample, and the data were analyzed by SPSS. Analysis data by Descriptive statistics and Content Analysis. The research results were found as follows;An understanding and interpretation of Baroque keyboard music style should include the following elements: instruments, scores, historical perspective, players, and performance techniques 2. The harpsichord mainly relies on the sound plug to adjust its timbre and volume, which makes it impossible to play the extended tone. The playing technique has more decorative tones added. In order to express the Baroque style, piano performance also requires retaining some of the playing skills of the harpsichord.
3D Visual Saliency: An Independent Perceptual Measure or A Derivative of 2D Image Saliency?

While 3D visual saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and has been well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art 3D visual saliency methods remain poor at predicting human fixations. Cues emerging prominently from these experiments suggest that 3D visual saliency might associate with 2D image saliency. This paper proposes a framework that combines a Generative Adversarial Network and a Conditional Random Field for learning visual saliency of both a single 3D object and a scene composed of multiple 3D objects with image saliency ground truth to 1) investigate whether 3D visual saliency is an independent perceptual measure or just a derivative of image saliency and 2) provide a weakly supervised method for more accurately predicting 3D visual saliency. Through extensive experiments, we not only demonstrate that our method significantly outperforms the state-of-the-art approaches, but also manage to answer the interesting and worthy question proposed within the title of this paper.
Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions.Â Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA model
The Current State of Communication Skills in the Multicultural Environment of High School Students

The article discusses the state of communication skills in the multicultural environment of high school students. Data was collected through a survey using a questionnaire from 521 students in 6 high schools across the country. The results show that the state of communication skills in the multicultural environment of high school students is at a moderate level (2.3) with various specific manifestations. There are differences in the manifestation of communication skills in the multicultural environment between urban and rural students. While communicating in a multicultural environment, high school students face difficulties such as difficulty controlling emotions, lack of refusal skills, and difficulty reconciling differences in personality and preferences with their peers.
Let’s get it started: Eye tracking in VR with the Pupil Labs eye tracking add-on for the HTC Vive

Combining eye tracking and virtual reality (VR) is a promising approach to tackle various applied research questions. As this approach is relatively new, routines are not established yet and the first steps can be full of potential pitfalls. The present paper gives a practice example to lower the boundaries for getting started. More specifically, I focus on an affordable add-on technology, the Pupil Labs eye tracking add-on for the HTC Vive. As add-on technology with all relevant source code available on GitHub, a high degree of freedom in preprocessing, visualizing, and analyzing eye tracking data in VR can be achieved. At the same time, some extra preparatory steps for the setup of hardware and software are necessary. Therefore, specifics of eye tracking in VR from unboxing, software integration, and procedures to analyzing the data and maintaining the hardware will be addressed. The Pupil Labs eye tracking add-on for the HTC Vive represents a highly transparent approach to existing alternatives. Characteristics of eye tracking in VR in contrast to other head-mounded and remote eye trackers applied in the physical world will be discussed. In conclusion, the paper contributes to the idea of open science in two ways: First, by making the necessary routines transparent and therefore reproducible. Second, by stressing the benefits of using open source software.
Nonlocality of the energy density for all single-photon states

The nonlocality of single-photon states has been analyzed from several different but interrelated perspectives. In this article we propose a demonstration based on the electromagnetic energy density observable and on the antilocal property of the frequency operator $\mathrm{\ensuremath{\Omega}}=c{(\ensuremath{-}\mathrm{\ensuremath{\Delta}})}^{1/2}$. The present proof is based on the standard quantization of the electromagnetic field, which can be formulated equivalently in the momentum representations or in the position representations of Landau and Peierls [Z. Phys. 62, 188 (1930)] and of Bia\l{}ynicki-Birula [edited by E. Wolf (Elsevier, Amsterdam, 1996)]. Our proof extends to all single-photon states the results of Bia\l{}ynicki-Birula, which were formulated for two particular classes of states, those involving a uniform localization [Phys. Rev. Lett. 80, 5247 (1998)] or alternatively states that are electrically or magnetically localized [Phys. Rev. A 79, 032112 (2009)]. Our approach is formulated in terms of Knight's definition of strict localization [J. Math. Phys. 2, 459 (1961)], based on the comparison of expectation values of single-photon states of local observables with those of the vacuum.
Computer Network Optimization Using Queue Tree and Peer Connection Queue (PCQ) Method at SMK Negeri 1 Sumbawa for Learning Support

Bandwidth struggles and non-optimal internet networks can result in connection problems, such as slow connections and allowing users not to connect to the internet. therefore it is necessary to optimize computer networks to be able to maximize service to users, both used to find information, download and upload data. This study uses the Queue Tree and PCQ methods which aim to optimize computer networks at SMKN 1 Sumbawa Besar and improve the quality of computer networks in terms of delay, jitter, throughput, and packet loss. This type of research is qualitative and quantitative research. Research methods include data collection (observation, interviews, literature study), analysis, design, implementation, and testing. Based on the test results after using the Queue Tree and PCQ methods, the average values of delay, jitter, throughput, and packet loss are more optimal than before applying the Queue Tree and PCQ methods. The delay measurement in data rate before applying the Queue Tree and PCQ methods is 1414925 m/s and after applying the Queue Tree and PCQ average delay is 3,165 ms. Test Jitter on the data rate before applying Queue Tree and PCQ methods is 1414957 ms and after applying the queue tree and PCQ jitter is 3,164 ms, Throughput on data rates before applying the Queue Tree and PCQ methods is 5043 kbps and after applying the queue tree and PCQ throughput is 2486 kbps, Test packet loss on data rates before applying the Queue Tree and PCQ methods is 7,911% and after applying the queue tree and PCQ methods is 0,030%.
Sistem Informasi Promosi Berbasis Web Pada CV. Indoestri Supply

CV. Indoestri Supply is a company that sells Thaitea beverage equipment. Problems were found, namely: the promotion process using conventional promotion through the installation of banners, word of mouth promotion, and added to promotion through social media advertising. The purpose of this research is to analyze and design a web-based promotional information system. The research method uses a prototype model and a UML model, using use case diagrams, activity diagrams, and class diagrams. The results of this study are a prototype of a web-based promotional information system that can help CV. Indoestri Supply in overcoming the problems faced.
Detection of fake news using Naive Bayes and Passive Aggressive Classifier

The widespread presence of false information in the form of fake news poses a significant problem for society today.It is crucial to develop effective techniques to automatically identify and combat this issue.This research paper presents a comprehensive method for detecting fake news that combines the Naive Bayes theorem, TF-IDF vectorizer, and an aggressive passive classifier.By leveraging the Naive Bayes algorithm, our approach utilizes a probabilistic framework to analyze the data.The TF-IDF vectorizer helps in capturing the importance of words within documents, aiding in the detection process.Additionally, the aggressive passive classifier enhances the accuracy of classification by incorporating a self-training mechanism.To validate our proposed approach, we conducted experiments on a benchmark dataset.The results demonstrated promising performance, surpassing existing methods in terms of accuracy, precision, recall, and F1-score.This research contributes to the advancement of fake news detection methods and offers a valuable solution to the challenges posed by the proliferation of false information in modern society.
Encrypted epigraphy - the case of a mysterious inscription in the Neapolitan church of Santa Maria La Nova

This paper contains all steps regarded as necessary and relevant towards the decryption of the epigraph placed in the "Turbolo Chapel" of the Neapolitan Church of Santa Maria La Nova.The inscription has been processed by means of Python-written procedures in combination with the decryption software AZdecrypt, thus displaying linguistic features converging unequivocally to the hypothesis that its clear-text be encrypted by monoalphabetic substitution from a natural language, possibly alongside with transposition and polyalphabetism.A preliminary analysis on characters n-grams and vocals-consonants combinations has not succeeded yet in individuating any clear-text language among all the analysed corpora.
Book Reprography in Copyright

Books are evolving into one of the most important mass communication tools on the planet. As a result, buying books through manufacturing and distribution is a crucial form of mass communication. The research’s purpose is to explore book reprography in copyright. The researchers used qualitative approach with literature study as the research design. The data were generated from different literature, mainly relevant books and scientific articles. The researchers discovered that, under copyright law, the creator's exclusive right arises immediately based on the declarative principle after a work is realized in tangible form, subject to limitations in accordance with statutory and regulatory conditions.
Neural Network and Random Forest Algorithms for Estimation of the Waiting Times Based on the DES in ED

This study aimed to predict patient waiting times for an ED (emergency department) unit byintegrating DES (discrete event simulation) model and ML (machine learning) algorithms. The healthresources in the DES model were kept constant. However, the results were obtained by including thestatistical distributions of the processes in the DES. Length of stay (LOS), resource efficiency rates, patientgenders, walking distance, time of processes, and age were considered input factors that affect patientwaiting times. Prediction data were calculated using Neural Network (NN) and Random Forest (RF) modelsfrom ML algorithms. Testing and training phases of ML algorithms are set to 20% and 80%. The RF modelperformed best with low RMSE, MSE, MAE, and high R2values. This model's RMSE, MSE, MAE, andR2values were calculated as 2.81, 1.67, 0.88, and 0.996, respectively. As a result, this study suggestedintegrating DES and ML models to overcome many factors, such as satisfaction, cost, and quality, with theintense human factor in service sectors with dynamic structures.
Twitter Sentimental Analysis Using Python

Social media websites have Emerged as one of the platforms to raise users’ opinions and influence the way any business is commercialized. Opinion of people matters a lot to Analysis how the propagation of information impacts the lives in a large-scale network like Twitter. Sentiment analysis of the tweets determine the polarity and inclination of vast population towards specific topic, item or entity. These days, the applications of such analysis can be easily observed during public elections, movie promotions, brand endorsements and many other fields. In this project, we exploited the fast and in memory computation framework 'Apache Spark' to extract live tweets and perform sentiment analysis. The primary aim is to provide a method for Analysis sentiment score in noisy twitter streams. This paper reports on the design of a sentiment analysis, extracting vast number of tweets.
K-Means and J48 Algorithms to Categorize Student Research Abstracts

Text mining is a rapidly growing field in computer science that is used to extract meaningful information from text data. This information can be used for various applications, such as categorizing research abstracts based on their content. This study focuses on the use of text mining techniques. The goal was to determine which algorithm was more accurate in categorizing the research abstracts. The results of the study indicated that the J48 algorithm outperformed the K-Means algorithm in terms of accuracy. This suggests that the J48 algorithm is a more effective method for categorizing research abstracts based on their content. Additionally, the findings provide insight into the use of text mining techniques for categorizing research abstracts in specific fields, such as computer science. Overall, the study demonstrates the potential of text mining techniques for analyzing and categorizing large volumes of text data. As the field of text mining continues to grow, it is likely that more applications will emerge, making it easier to extract valuable information from unstructured text data. The findings of this study can be used to improve the efficiency and accuracy of text mining techniques, particularly for categorizing research abstracts in specific fields.
Criminal Tendency Identification Using Deep Learning Approaches: A Novel Approach for Security Protection

A crime is punished by the state based on its seriousness, but it may also be dealt with by other authorities if it causes bodily injury, mental suffering, or property loss to another person or property. The definition of crime is as follows: Law enforcement is under increasing pressure to provide new deterrents as criminal activity continues to grow. It is impossible for traditional criminal justice procedures to be effective in today’s quickly changing criminal environment. Investigating the methods of foreseeing or predicting crime in great detail may lead to the development of a machine that supports police personnel while also assisting them in the prevention of crime. We believe that machine learning and machine vision are excellent places to begin. Our analysis included a few instances of these strategies, which piqued our interest and piqued our want to learn more about them. In the past, authorities have used similar strategies, and they will continue to do so in the future. One of the objectives of this research is to investigate how computer vision and machine learning may be used to assist law enforcement detect and stop crimes more quickly and efficiently. The use of cutting-edge artificial intelligence and computer vision technologies may also be beneficial to law enforcement.
An Application for Genuine Reviewer Identification Using Deep Learning Techniques

People are widely using Internet reviews like an information source before making each and every kind of purchases. Genuine evaluations usually have a big affect on subsequent brand purchases. With our approach, we go out of our way to look at posted evaluations from customers to better grasp distinct behavioural features. On the alternative side, using different natural language processing (NLP) techniques and sentiment analysis (SA), it is crucial in a number of ways to identify fraudulent reviews and remove them from the database. A hybrid CNN-LSTM deep learning (DL) model that employs SA model that can predict whether a review is positively or negatively trained on the assessment data set. The true reviewer is identified based on the crowd sourcing concept’s overall score. The e-commerce industry and various sites also depend on item ratings. Consumers can have confidence in the company’s products prior completing the purchase. In order of big e-commerce businesses including Myntra, Nykaa, Flipkart, etc., for tackling the problem of false feedback and eliminate fraudsters and phoney evaluators, maintaining users’ faith with digital shopping sites, a solution should be found. The “Amazon Yelp data set”, which is a useful set of data for training on a relatively modest range and may be utilized to acquire maximum flexibility and accuracy, is employed to train the algorithms in this study.
APLIKASI KALENDER KEHAMILAN DAN PERHITUNGAN MASA USIA KEHAMILAN BERBASIS ANDROID MENGGUNAKAN ALGORITMA NAEGELE

ABSTRACT : Pregnancy process becomes the center of special for families who are waiting for the presence of a loved child. Therefore, pregnant women often checj with the obstetrician to determine the development of fetus in the womb. The problem occurs mother who for prenatal check-ups are only given the fetus underweight. Determining the start of pregnancy and gestational age is important to be able to know within the progress of pregnancy. One of the ways to determine gestational age by knowing the first day of the last menstrual period (LMP). With the naegele method, tjis calculation can also be used to calculate the estimated day of birth. As an easy way to estimate the weight of the fetus is to measure the height of the uterine fundus (TFU). Therefore, based on the problems above, an android-based pregnancy calendar and calculation application of gestational age were made by using the Naegele algorithm. The pregnancy calendar and calculation application of gestational age are designed to make it easier for pregnant women to find out the estimated day or birth. The gestational age and normal fetal weight according to gestational age on an android smartphone.&#x0D; Keywords: Naegele, HPHT, estimated birth, gestational age, fetal weight.
PENERAPAN CI (CODE IGNITER) UNTUK PENJUALAN BATU DI DESA SENGKUANG (STUDI KASUS CV QUARI SENGKUANG)

Abstract—The business world will always develop along with the times, with various kinds of technology that are so sophisticated and diverse that the competition in the business world is getting tougher. The business world engaged in stone sales has a lot of bookkeeping because the bookkeeping is carried out every day, such as administrative bookkeeping carried out by the cashier when recording sales transactions with buyers and also inventory data bookkeeping carried out by the factory section which records inventory. In the research conducted for the development of a web-based sales transaction application using the Code Igniter framework. Code Igniter is one of the widely used frameworks, where the main goal is to minimize code during website creation. So that CV Quari Sengkuang can apply the concept of inventory control which is defined as the number or quantity of goods ordered with minimal costs or often referred to as the optimal purchase amount.&#x0D; Keywords: Stone, Codeigniter, Implementation, Website