Reviewerly/exHarmony huggingface dataset:
Reconstruction of simulated VLBI data using the SARA method and random raw patches

VLBI (very long baseline interferometry) is used to image astronomical objects. However, the image quality decreases due to incomplete visibility measurements. The visibility is affected by the frequency, baseline length, measurement duration, and some telescopes used. A popular method that can solve this quality degradation problem in VLBI is SARA (sparsity averaging reweighted analysis). This study aims to increase the SARA performance with scoped in improving NRMSE (normalized root mean square error) and SNR (signal-to-noise ratio) on simulated VLBI data. We used random raw patch pairs from the SARA results to get a trained dictionary. In collecting the data for training, we used ten original images of the surrounding objects that we captured. Then we measured it using a VLBI sampling pattern, processed it using SARA, and applied the SARA results as a pre-reconstruction image. After that, we trained these ten pairs of the original and pre-reconstruction images. In reconstruction, we used the dictionary to test two simulated data and compare the results with the SARA. Finally, the results showed that the combination of SARA and random raw patches had a smaller NRMSE and a higher SNR than the SARA alone.
Salary Classification &amp; Prediction based on Job Field and Location using Ensemble Methods

The economy is one of the determinants of how a person can live their life. In this current economic situation, inflation occurs everywhere, causing the prices of necessities to rise. In order to have a decent life, people must find a job with the highest possible salary to fulfill their needs. Various job industries have their salary range. Obtaining the information of salary level for the respective job is helpful for employers and employees to estimate the expected salary. This work aims to classify the salary level of jobs available in Indonesia and determine whether those salaries are decent enough. The learning methods are logistic regression, decision tree, k-nearest neighbor, support vector machine, voting classifier, bagging classifier, random forest, and boosting classifier. Random Forest achieved the best result with an accuracy rate of 72%. Based on the analysis result, factors such as job field, educational background, working experience, working hours, and job location influence salary.
Human Resources Management Information System Requirement Analysis and Development in Humanitarian Foundation

The development of information and communication technology in the twenty-first century has made it possible for information to quickly cross the boundaries of globalization. However, humanitarian foundation that work in the areas of education, treatment, job training, workplaces, and nursing homes for individuals with disabilities still do not employ information system technology extensively. The data collection system is still inefficient, resulting documents are easily damaged or lost. This study uses data collection methods with questionnaires and interviews with the number of samples determined using the Cochran formula. System Development Life Cycle Methodology was used to develop the application. The system is designed based on Object-oriented Analysis and Design (OOAD) methods with the Unified Modeling Language (UML) notation. The conclusion from the simple linear regression hypothesis test is that there is an influence between Human Resource Management on Employee Performance. By designing an adequate Personnel Management Information System to support human resource management, it can improve employee performance.
Imbalanced Classes Treatment in Deep Learning Multi-label Aspect Classification using Oversampling and Under-sampling

Categorising aspects is one of the research in aspect-based sentiment analysis (ABSA). The aspect classification will categorise the issues addressed by the review content. Multi-label categorisation incorporates aspects because each instance will have a unique set of labels or aspects. Aspect classification is one of the ABSA jobs that focuses on classifying the features related to the review text. The most used categorisation strategy relies on balanced data. Unbalanced data can be dealt with in various ways, including algorithm-level and data-balanced methods. This study addresses unbalanced data using three techniques: classification components of SMOTE, ADASYN, and undersampling. This study demonstrates that SMOTE and ADASYN can enhance model performance. However, sampling needs to be more appropriate for LSTM and Bi-LSTM techniques. The results indicate that SMOTE is more suitable than ADASYN for multi-label aspect classifications that employ LSTM or BiLSTM. Comparing the under-sampling approach to using SMOTE or ADASYN, all evaluation scores on LSTM and BiLTSM have been reduced. According to this research, the best model performance for multi-label aspect classification is LSTM with SMOTE implementation for data balancing.
Performance of Known Ratings-Based Multi-Criteria Recommender System for Housing Selection

Housing developments are increasingly massive, and the lack of available information makes prospective customers experience difficulties in choosing a housing. These conditions resulted in the need for a recommendation system to assist consumers in choosing a place to live. In this study, we propose using the Multi-Criteria Recommender System (MCRS) to produce the most recommended housing selection recommendations in a case study of five housing complexes in Malang Raya. The system generates recommendations based on known user rating of 14 criteria and an overall rating (R0) stored in the database. In the experimental stage, the MCRS system in this study used four different methods: cosine, adjust cosine, Pearson correlation, and spearman rank-order correlation coefficient. The test results show that the recommendation system with each similarity method can produce housing recommendations by displaying the three most relevant housing recommendations to the user. Next, we use a confusion matrix to analyze the accuracy of the recommendations generated by the four similarity methods. The results of the confusion matrix calculation show that the average accuracy value for cosine-based similarity is 63.8%, the adjusted-cosine similarity is 70.4%, the Pearson correlation is 88.7%, and the Spearman rank-order correlation coefficient is 75.57%.
Visualizing Texture of Fractal Arts in Unity3D Framework

An image is a visual representation of a person or art. There exists a type of image called fractal art. It generates a disorganized but precise shape in any calibration. Fractal art often appears in digital images resulting from fractal calculation. The study of this paper focuses on visualizing fractal art with texture within the Unity3D framework. This study proposes a way to apply UV coordinates with Julia set iteration. It manifests in the form of a Unity3D shader code. The proposed code generates the Mandelbrot set and some Julia sets. It is used in conjunction with a batik color palette to generate batik fractal art to be analyzed. The code generates a fractal visual with another added detail. It shows how mapped texture adds more motifs based on the iterated texture coordinates. The fractal visualization is also affected by other factors. Change in iterated UV coordinates either by increment or logarithm remarkably transform generated motif. Textures with different compositions can slightly modify the generated motif other than changing colors. Texture wrap mode provided by Unity3D also influences how the texture is sampled when it is out of bounds. Unity3D automatically handles any calculation error. The result of this study is hoped to be beneficial in visual effects creation.
A Comparison Between Interpolation Method and Neural Network Approach in 3D Digital Imaging and Communications in Medicine

Higher image reconstruction with excellent structural detail allows experts to perform accurate analysis, especially on the smallest organ details. The interpolation method that approaches the problem of medical image reconstruction, especially 3D, still causes serious problems. The medical image produced by the interpolation method produces blurred or smooth lines on some parts of the organ. This can cause errors in the medical analysis that will be carried out if the reconstruction results are problematic. For this reason, a method is needed that can reconstruct images well without producing blur but does not require very large computer resources. This study aims to evaluate and compare the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures in the DICOM data format. This study evaluates and compares the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures. The test scenario was performed using images from the ADNI dataset and comparing the output results using a variational autoencoder and a multi-level densely connected super-resolution network on 3D data with existing interpolation methods. The evaluation was done using two metrics, i.e., SSIM and PSNR. The results showed that the variational autoencoder method has the highest SSIM and PSNR values, indicating it has the highest image quality among the three methods, while the mDCSRN method has the lowest SSIM and PSNR values, meaning it has the lowest image quality.
Gesture-Controlled Robotic Arm

Robotic arms are highly effective for industries that demand quick and reliable performance. These efficient devices are essentially automated systems that, unlike humans, do not get tired or need a rest. These machines have been used for many years but have recently progressed significantly with the advancement of complex sensors. Robotic arms of today come with various sensors that let them move around and react quickly in their working areas. This paper introduces a human hand gesture-controlled automatic low-cost robotic arm. In this proposed system, an Arduino Mega microcontroller gets the information from all the sensors and correctly manages the servomotor with the help of the value of sensors. All the sensors required to control the various servos on the robotic arm are placed into a hand glove. The robotic arm is operated in this system by two flex sensors. One flex sensor is linked to the glove’s forefinger section to manage the arm’s claw, and another is attached to the middle finger section of the glove to regulate the arm’s wrist. A gyroscope is also pinned to the glove to track the movement of the forearm and base. As a result, the base servo moves clockwise or counterclockwise depending on whether the hand glove is angled right or left. However, if the hand glove is angled upward or downward, the gyroscope data will cause the forearm servo to rotate either clockwise or counterclockwise. The sensors’ values are converted to the servo motors’ rotational degrees. The sensors’ values are converted to the servo motors’ rotational degrees. The claw, wrist, forearm servos and base of the proposed robotic device can rotate up to 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> , 45 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> , 120 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> and 180 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> degrees, respectively.
Comparative Analysis of Elliptic Envelope, Isolation Forest, One-Class SVM, and Local Outlier Factor in Detecting Earthquakes with Status Anomaly using Outlier

In Indonesia, large earthquakes happen frequently. Due to the location of Indonesia, over three active tectonic plates, fractures and trenches have formed on both the land and ocean floor. Over 1,250 large earthquake occurrences have occurred in Indonesia during the past 120 years, beginning in 1900. Based on data from the BMKG (Indonesian non-departmental government organization for meteorology, climatology, and geophysics) official website, up to 12,351 earthquakes occurred in Indonesia in 2021. Not every earthquake was fatal; many of them were considered common. In this work, the author compares Elliptic Envelope, Isolation Forest, One-Class SVM, and Local Outlier Factor. 4 types of anomaly detection algorithms to locate earthquakes whose status is abnormal or out of the ordinary utilizing outliers. The application of anomaly detection for each algorithm will be tested in two ways; the first test will only use the default parameters, and the second test will use the parameter (Tuning Hyperparameters) at the contamination/nu parameter of 0.025 or 2.5%. Contamination/nu is a value that indicates the amount of data considered an anomaly—Accuracy, Recall, Precision, F1-Score, AUC, and Specificity. Experiments have shown that Isolation Forest has a higher value than all other experiments in detecting anomalous status in earthquakes. In comparing performance using default parameters, Isolation Forest had a precision of 13.02%, recall (sensitivity) of 99.35%, specificity of 82.80%, accuracy of 83.24%, AUC of 91.08%, and F1-score of 23.03%. After tuning the hyperparameters, the performance of each model increased, with Isolation Forest had a precision of 37.21%, recall (sensitivity) of 36.85%, specificity of 98.41%, accuracy of 98.42%, AUC of 67.63%, and F1-score of 37.03%.
The Role of Detection Rate in MAPE to Improve Measurement Accuracy for Predicting FinTech Data in Various Regressions

Prediction is included in the data mining process to predict future data based on learning from past data. Various techniques are used in making predictions. The Regression method also includes techniques for making predictions. Various regressions such as Linear Regression, Ridge Regression, Lasso Regression, and Multivariate Adaptive Regression Splines (MARS) are regression techniques that are fond of being used in predicting data in business. Every prediction is always measured success with several formulations. As MAPE is a measuring tool in obtaining accuracy, so it is trying to be designed with the role of Detection Rate (DR) in order to get a smaller error value in obtaining accuracy. In this paper, the process of obtaining accuracy in Linear Regression is carried out to obtain a MAPE of 0.15874361801345002 % and the role of DR in MAPE is 0.1410249900632677 %. At Ridge Regression get a MAPE of 0.15820461185453846 % and the role of DR in MAPE is 0.14077739389387 %. On Lasso Regression get a MAPE of 0.14793925681569248 % and the role of DR in MAPE is 0.1370143839961479 %. On MARS get a MAPE of 0.16209808399129746 % and the role of DR in MAPE is 0.14528079908718253 %.
Detection of Kidney Cysts of Kidney Ultrasound Image using Hybrid Method: KNN, GLCM, and ANN Backpropagation

This research is aim to detect kidney cysts from human kidney Ultrasound (USG) 2D Images. This research uses data from Hospital patients as many as 25 Ultrasound images of the human kidney in the format image .jpg. This research uses the K-Nearest Neighbor (KNN) method for image classification of ultrasound images then using Gray Level Co-Occurrence Matrix (GLCM) method for image extraction to detect cyst and non-cyst regions from the result of classification after that using Artificial Neural Network (ANN) method type Backpropagation for image detection to find cysts from human kidney Ultrasound (USG) 2D Image from the result of image extraction. The result of this research is producing the algorithm to implement the method and the tool software application to detect kidney cysts from ultrasound 2D images. The accuracy of this tool is 84% which can detect with accurate 21 kidney cysts from 25 kidney ultrasound 2D images that validate of a Urology Specialist Doctor.
Comparative Models of Price Estimation Using Multiple Linear Regression and Random Forest Methods

The house is one of the essential humans needs as a place to gather and do activities with family, and shelter, as well as a means of investment. The growth rate of people's demand for housing, especially houses in an area, is influenced by the rate of population growth in that area. Some regions in Indonesia with a reasonably high population growth rate are Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek). On the other hand, property entrepreneurs must be able to project house prices because businesses engaged in the property sector are currently very competitive. This study aims to model and compare several machine learning methods to estimate house prices in Jabodetabek based on facilities, year of construction, location, land and building area, number of rooms, condition of house construction, and legality documents. This modeling uses Multiple Linear Regression and Random Forest methods. The results of the modeling evaluation where the Random Forest model has an accuracy rate of 95.6%, while the Multiple Linear Regression model has an accuracy rate of 75%.
Classification of Customer Satisfaction in Marketplace

Customer satisfaction is one of the most important things in the marketplace to evaluate the customer’s impression of the service in the marketplace. Customer satisfaction is measured in five aspects, i.e., the application user interface, application features, discounts and promotions, ease of access to the applications, and customer service. This paper used three classification algorithms in data mining: KNN, Decision Tree, and Naïve Bayes to classify customer satisfaction. Calculation of the algorithms performed in this paper using the Rapid Miner. There are two experiments on each algorithm using the RapidMiner application, but the method used to calculate data is undoubtedly different. There are 549 data taken from marketplace user surveys, namely Shopee, Tokopedia, and Lazada. The highest accuracy result obtained was 96.36%, with a precision of 96.97% and a recall of 98.96% of the classification using the Decision Tree algorithm through the second experiment. From these results, it can be inferred that Decision Tree is the most accurate algorithm in this paper because it has the highest accuracy when performing experiments. According to the results of this research, we desire to be an accurate reference source that can be used by marketplaces to obtain information on customer satisfaction and improve their service.
Predicting the Success of Garment Sales on Transaction Data using the Classification Method with the Naïve Bayes Algorithm

In facing business competition, one of which is the fast-growing garment business, companies must maintain the continuity of the business they run and meet consumer needs. Companies must be able to predict what items are selling well from processing previous transaction data so that the results can help the company know what goods must be produced in the following year to meet consumer needs. Because of that, this research reprocesses sales transaction data for 2020 to classify goods sold and not sold using the Naïve Bayes algorithm, a classification algorithm using probability and statistical methods proposed by British scientist Thomas Bayes. Sales transaction data for 2020 will be processed using existing processes in the Knowledge Discovery Database (KDD), such as data selection, preprocessing, transformation, data mining, and evaluation. The supporting application used to process sales transaction data is Knime. Based on the partition from three ranges of training data and data testing (70%:30% | 60%:40% | 50%:50%), the results of this study show are the dress and pants category shows the highest significant value; these dresses and pants need to be further increased in production for the coming year that the accuracy level from the confusion matrix with the Naïve Bayes algorithm is above 90%, which means the Naïve Bayes algorithm can be used to predict garment sales so that it can be a reference for companies to increase sales in the following years of goods that are classified as buyable by consumers using the Naïve Bayes algorithm.
Development of Real-Time Face Recognition for Smart Door Lock Security System using Haar Cascade and OpenCV LBPH Face Recognizer

Face recognition is a technology that is widely used in security systems. In a door security system, facial recognition can be used to open the door simply by recognizing the face of the door owner. This study aims to develop a real-time facial recognition system for smart locking doors using Haar Cascade and OpenCV LBPH Face Recognizer. The purpose of this project is creating security system to limit people who can access a room. The Haar Cascade method is used to detect faces in images, while the OpenCV LBPH Face Recognizer is used to recognize detected faces. This system was developed using the Python programming language and the OpenCV library. The test results show that this system can detect and recognize faces with an accuracy of 62.7% with our dataset and can be improved by adding more datasets and using deep learning algorithms to train the recognizer model. Thus, the developed real-time facial recognition system can be used as a smart locking door security solution with high accuracy.
Acute Respiratory Infections Diagnosis Using Learning Vector Quantization

Acute Respiratory infections are a disease that is quite widely suffered throughout the world, moreover, every year in the world 13 million children under 5 years old die 95% of them are from developing countries and one-third die due to acute respiratory infection. based on a report from the Ministry of Health in 2020 There are 34.8% of children suffering from this disease in Indonesia. Many people do not understand the symptoms that arise when suffering from ARI, especially if the incident happened at home without medical assistance. For this problem, a system is needed that can help people to diagnose acute respiratory infections. This study implements the learning vector quantization method to diagnose the symptoms experienced whether they belong to the classification of common cold and flu, asthma, pneumonia, or tuberculosis, data that has been entered by the user will be compared with previously created models using a training dataset. As for the testing in this study using the classification accuracy method and from the results of this study, the accuracy of the best method was obtained at 97.50% at split validation of 80:20 and an average accuracy of 85.92%.
Classification of Television Programs Based on Public Opinion in Social Media Using Random Forest and Decision Tree

One of the electronic media that provides a source of entertainment and information for its audience is television programs. The quality of television programs is assessed based on ratings. In addition, public opinion also has a crucial role, and this is because this opinion can be used as data to carry out sentiment analysis by classifying whether the opinion is included in the category of positive or negative sentiment. The problem requires an efficient decision-making system, such as a machine learning algorithm. This study aims to develop a classification of sentiment analysis using Random Forest and Decision Tree algorithms. The dataset used is a public opinion tweet dataset on Twitter for four television programs with five attributes: the number of retweets, id, sentiment, tv show, and tweet text. The results of testing the two algorithm models show that the Random Forest and Decision Tree models have different performance levels. The Decision Tree has lower performance with an accuracy of 74.66%, precision of 76.13%, recall of 78.75%, and an f1-score of 76.73%. Meanwhile, Random Forest has higher performance with an accuracy of 84.06%, precision of 83.65%, recall of 82.80%, and f1-score of 82.32% with a comparison of test data and training data of 20:80. Based on these results, the Random Forest model has the best performance compared to the Decision Tree in classifying television program sentiment based on user opinions in social media.