Reviewerly/exHarmony huggingface dataset:
Comparison of Heart Disease Symptom Algorithm Using Particle Swarm Optimization

Heart disease is a disease in which there are many sufferers and one of the deadliest diseases in the world, there are heart attacks with a total of 43.30% of new cases, and data on cases of death with an amount of 12.90%. In 2018, the number of sufferers diagnosed with heart disease was 31,462 cases based on Lampung Province. The cause of the increase in death rates every year is due to lack of access to find information about heart attacks. From this problem, researchers want to develop technology science in the health sector, especially using the data mining classification algorithm, namely the Naive Bayes algorithm. In the first experiment the researchers applied the Naive Bayes algorithm without using the Particle Swarm Optimization feature selection to get an accuracy value of 82.83%. In the second experiment the researchers applied the Naive Bayes algorithm using the Particle Swarm Optimization feature selection to get an accuracy value of 89.26%. so it can be concluded that the Particle Swarm Optimization selection feature can be used to optimize the accuracy value of the Naive Bayes algorithm.
Recognition of Real-Time Angklung Kodály Hand Gesture using Mediapipe and Machine Learning Method

The application of machine learning in the arts and culture can help preserve these arts and culture and, in some cases even facilitate access to the arts to the public. In this study, a hand gesture recognition system was developed for the Kodaly method hand gesture to play angklung directly from a personal computer. Many methods of hand gesture recognition still need an additional hardware such as gloves and expensive sensors that may not be accessible to some people. This study proposes a computer vision-based approach utilizing the personal computer internal webcam without additional hardware and employs multi-layer perceptron classifier for real-time recognition and Mediapipe for feature extraction. The developed system achieves an average accuracy of 99% and runs with low latency between recognition and audio output. The effective range of hand gesture recognition is up to 1.5 meters in environment with a light level as low as 25 LUX.
Educational Data Mining Patterns K-anonymity for The Analytics of Student Privacy Data

Along with the times and rapid technological advances, some methods can collect, store, and analyze data with extraordinary capabilities. More recently, academic institutions have also offered open and distance learning programs. Through these efforts, they obtain big data related to information and communication systems that involve their students by combining various modern big data analytical tools and techniques to maintain student data privacy. The main purpose of this research is to test analytical opportunities against a learning method while paying attention and protecting the security of student data. We can achieve balance in our data mining efforts to secure data privacy through the K-anonymization method. The results of this study indicate that, in testing the correlation coefficient on each online activity forum and student achievement scores, the most significant result was 0.298 on the "Total Log" activity platform. After the anonymization process, it is found that the decrease in the number of coefficients must be balanced with a significant enough loss of information.
Hand Gesture Recognition For Tone Classification According To Kodaly Handsign Using CNN

In recent years, hand gesture recognition has received attention for its wide range of applications and its ability to efficiently interact with machines. Hand signs have become part of basic music theory in educational settings, one of which is through the use of Kodaly hand signs. Kodaly Hand Signs are used as a concept in interactive angklung performances to determine the notes to be played. Thus, the automation of hand gesture recognition in playing music is one of the innovations in the field of artificial intelligence and machine learning. This paper discusses a deep learning convolutional neural network model that can classify 8 Kodaly hand signs in real-time. The hand gesture recognition process begins with image segmentation, displays the gray intensity value, resizes the image, then proceeds to the preprocessing stage by converting the image into a vector form, then the image data is trained to produce a model. It is models such as these that will be useful in education in providing a human-computer interface for musical expression. The experimental results show that the proposed CNN model achieves an average accuracy rate of 98.29%, 99.95% precision, 99.97% recall, and 99.96% F1 score.
Sentiment Analysis of Hashtag Activism on Social Media Twitter

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter’s API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research.
Improved VGG-16 for Classifying Thyroid Nodule on Thyroid Ultrasound Images

Thyroid nodule composition is one of the determinants of thyroid cancer malignancy. Nodules in the thyroid can be detected by ultrasonography, which is one of the most sensitive imaging methods. However, imaging methods by ultrasonography are susceptible to doctors’ experiences, levels, and other factors. Therefore, a more objective diagnostic system is intended to assist doctors in creating the decision. This study developed a method to help experts define the composition characteristics. The experts have already cropped the dataset and then moved to preprocess using an adaptive median filter. Subsequently, the data were classified with the improved VGG16 into four categories those are cystic, solid, complex, and spongiform. The testing result procured 99.65% for accuracy, 99.98% for the micro area under the curve, and 99.99% for the macro area under the curve. These results indicate that our proposed method can be used in a small dataset to help doctors or experts identify the nodule’s characteristics.
Revalidating the Encoder-Decoder Depths and Activation Function to Find Optimum Vanilla Transformer Model

The transformer model has become a state-of-the-art model in Natural Language Processing. The initial transformer model, known as the vanilla transformer model, is designed to improve some prominent models in sequence modeling and transduction problems such as language modeling and machine translation. The initial transformer model has 6 stacks of identical encoder-decoder layers with an attention mechanism whose aim is to push limitations of common recurrent language models and encoder-decoder architectures. Its outstanding performance has inspired many researchers to extend the architecture to improve its performance and computation efficiency. Despite many extensions to the vanilla transformer, there is no clear explanation of the encoder-decoder set out depth in the vanilla transformer model. This paper presents exploration results on the effect of combination encoder-decoder layer depth and activation function in the feed-forward layer of the vanilla transformer model on its performance. The model is tested to address a downstream task: text translation from Bahasa Indonesia to the Sundanese language. Although the value difference is not significantly large, the empirical results show that the combination of depth = 2 with Sigmoid, Tanh, and ReLU activation function; and d = 6 with ReLU activation shows the highest average training accuracy. Interestingly, d = 6 and ReLU show the lowest average training and validation loss. However, statistically, there is no significant difference between depth and activation functions.
Blockchain-based loyalty system: feasibility testing

A loyalty program brings benefits to both companies and customers. In this paper, we consider the use of loyalty program integration in blockchain technology, one of the most promising advanced technologies, where trust is of prime significance and customer identification will no longer require a physical certificate. Based on the essential characteristics of the loyalty program, 4 possible approaches that integrate into a blockchain platform are identified. An analysis through several observations helps to determine the most suitable one to create a loyalty program that benefits all stakeholders. The performance of the proposed system related to the most suitable approach is evaluated according to the criteria that consider the feasibility of practical usability.
Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.
Feature Extraction in Hierarchical Multi-Label Classification for Dangerous Speech Identification on Twitter Texts

Dangerous speech is a strong hate speech that causes negative impacts, such as violence, crime, social pressure, trauma, and despair, and can lead to conflicts between groups. Raw data of Twitter texts need the necessary preprocess to obtain the proper training datasets for hate speech or even dangerous one. One reason is how to express hate speech related to mentions or hashtags. Because of the variants of context messages in raw Twitter posts which could be hate speech or not, the problem here is hierarchical and multi-label classification with three label types of hate speech status, issues, and dangerous levels. The issues in this work are about religion, ethnicity, and others. After handling preprocess, the word embedding includes data under-sampling because the dataset is not balanced. Additional stop-word dictionaries to overcome language-related vocabularies are also incorporated. To observe the preprocess effects in the classification problem, some methods of machine learning and deep learning, such as SVM, Bi-LSTM, and BERT are explored. Then we examined after hyper-parameter settings with performance indicators of subset accuracy and Hamming lost for imbalanced, in addition to F1 scores of micro and macro averages.
Simple Additive Weighting in the Development of a Decision Support System for the Selection of House Construction Project Teams

Every time they get a project, planning consultants and building implementers always work with contractors to work on the project. However, in project work, there are often several obstacles to the selected contractor, such as delays in work, workmanship not according to plan, and inappropriate building specifications to unskilled experts. This happens because the contractor selection process that is not right causes the construction phase to be not optimal it affects the quality of the resulting building. The selection of contractors involves a complex multi-criteria where each criterion used has different importance and the information about it is not known precisely so a method is needed to overcome these problems. For this reason, it is necessary to have a system in supporting contractor selection decisions, known as the Decision Support System. This system was built using the Rapid Application Development (RAD) design method with the stages of Requirements Planning, User Design, Construction, and Cutover. The construction of this DSS uses one of the methods in making decisions, namely Simple Additive Weighting (SAW) to make an assessment based on predetermined decision-making criteria. From this research, a website-based decision support system was obtained using the javascript programming language, with a score of 79.7% for the ease of use of the system, and a 93.3% score for the usefulness of the system.
A modification of the Distance Formula on the K-Nearest Neighbor Method is Examined in Order to Categorize Spices from Photo Using the Histogram of Oriented Gradient *

Spices are biological resources that have long played a very important role in everyday life. Spices have characteristics, shapes, and colors that are almost similar and it is difficult to distinguish one spice from another. To assist in recognizing the characteristics of existing spices, the author tries to do research with the title. "Spices Classification Using the K-Nearest Neighbor (K-NN) Method and Using Histogram Oriented Gradient Feature Extraction. The method used in this study is the K-Nearest Neighbor and uses the Histogram Of Oriented Gradient feature extraction. In this study, the dataset used was 2250 image samples and divided into two categories, namely training data and testing data with a ratio of 80%: 20%. The results of this study indicate that the most optimal testing distance formula, namely the Manhattan distance formula, obtained an average accuracy of 87%, 87% precision, 87% recall, 87% f1 score, 87% Fbeta score, and 77% Jaccard score. These results indicate that feature extraction greatly influences the number of types in extracting information, the Histogram of Oriented Gradient works optimally when the number of types extracted is small and not optimal when used in a large number of classification types.
An Intelligent Calibration Testing of Electricity Meter using XGBoost for Manufacturing 4.0

The manufacturing company with a core business of manufacturing electricity meters has a testing system for the metrology industry, namely calibration testing. The part of calibration testing on an electricity meter is the verification test, which uses the method of comparing it to the standard meter test bench to calculate the accuracy error of the measurement. However, the problem is that carrying out this test requires a long cycle time, and it is difficult to increase production capacity without adding a standard meter calibration test bench which has an expensive investment. The smart factory concept that supports industry 4.0 can open up opportunities for research on information technology using artificial intelligence with machine learning models to solve this problem with the idea of creating intelligent testing. This research requires data collection on a calibration test bench machine, then processed to find model predictions so that they can be implemented into an intelligent test using the XGBoost Regression with Hyperparameter Tuning and Optimization methods as the Goal of this Research. In the results of this research, the evaluation of the XGBoost using the Hyperparameter Tuning and Optimization method, which is implemented in this case, could improve the accuracy and RMSE data testing modelling comparing other scenario models as defined before in the literature review. So, this can be an excellent solution to be applied in metrology manufacturing, especially verification tests in Manufacturing Calibration Testing on Electricity Meter, which is faster and low-investment testing with the implementation of an Intelligent Manufacturing Calibration Test.
Exploring the Effect of Activation Function on Transformer Model Performance for Official Announcement Translator from Indonesian to Sundanese Languages

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia’s population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss.
Hate Speech Detection on Twitter Using BERT Algorithm

Hate speech on one social media platform, Twitter, is uncommon. Users on the Twitter platform can freely obtain, exchange information, and express opinions. This is one of the main factors that a person can be exposed to hate speech on Twitter. Victims who are exposed to hate speech may suffer from mental health disorders because most victims of hate speech are attacked verbally or emotionally. However, the lack of countermeasures against the detection of hate speech on the Twitter social media platform is still rare. In this study, a simulation was carried out using the website, along with testing and analyzing the detection of hate speech. The test is done by inputting a text on the hate speech website, and then the website will do a preprocessing and analyze this text using the BERT algorithm to classify whether the word is hate speech or not. The training results found that the detection of hate speech on Twitter user accounts using the BERT Algorithm has a 78.69% accuracy, a 78.90% precision, a 78.69% recall, and a 78.77% F1 score against the classification of hate speech groups. Thus users will more easily detect hate speech on Twitter by using the hate speech website.
Determining The Delivery Of Goods Using The K-Nearest Neighbor Algorithm And The Saving Matrix Method To Obtain The Optimal Route And Save Costs

Delivery of goods is a transaction or agreement between the seller and the buyer that produces an item to be transported and sent to the buyer. This study found problems with the late delivery of goods, namely limited vehicle capacity which resulted in delays in delivery of goods by sellers, delays in receiving goods by customers, and expensive transportation costs as a result where customers became dissatisfied with delivery services, decreased number of purchases, decreased delivery of goods and losses incurred suffered by the company or the seller in terms of costs or profits. The k-nearest neighbor algorithm is a classification technique by choosing a distance based on the size of the shortest distance and a saving matrix method that will form a delivery route taking into account vehicle capacity and saving on transportation costs. And later it will be formed into several new routes that aim to facilitate the delivery of goods with the advantage of providing satisfaction to customers and being able to save on transportation costs. So this research was developed for the route of delivery of goods by taking into account the capacity and number of vehicles which in previous studies regarding delivery routes. The results of this study solve the problem quite optimally at 12.67% or Rp. 757,356 cost savings while the delivery of goods resulted in 6 shipping lines instead of 11 shipping lines which cut 5 shipping lines or 11.11% for a total of 1532.15 Km.
Optimization of the Use of Artificial Neural Network Models for Accuracy Data Measurement Palm Oil Production Prediction Rate

Predictions are necessary in modern times if the amount of production, profit and loss analysis, and development must be calculated on a specific scale. The average prediction only determines a certain period using data series, so it will be very helpful on certain production scales, especially palm oil production as a commodity with a very large amount of investment. Palm oil production is Indonesia's production potential, which requires a prediction pattern for a certain period. The autoregressive integrated moving average (ARIMA) model is used to calculate production rates by performing predictive data analysis of palm oil production development over a period of time. To prove the level of foresight in the prediction process, the next step is to calculate the level of foresight of the prediction process error through the Mean Square Error (MSE) model. To get the accuracy value, the Artificial Neural Network (ANN) algorithm approach is carried out using the MATLAB tool. There are several ways to test the level of accuracy; the results are compared with the calculation results using MS Excel as well as the Statistical Product and Service Solution (SPSS) tool. The calculated accuracy of SPSS (0.015) and MS Excel (0.0201) are known, while the ANN algorithm is more observant with a value of 0.0055.
Offline Task Assignment and Motion Planning Algorithm Considering Agent's Dynamics

The aim of this study is to develop an algorithm for task assignment and movement planning to a destination by considering the acceleration and deceleration patterns of automated guided vehicles (AGVs). In this study, we propose an algorithm for task assignment and motion planning for AGV kinematics with multiple search trees based on the conflict-based search with optimal task assignment (CBS-TA) of Hönig et al. By devising the options for AGV actions and settings for standby actions, the algorithm can perform optimal motion planning for each AGV, considering its dynamic characteristics.