Reviewerly/exHarmony huggingface dataset:
Solar Radiation Forecasting using Customized Artificial Neural Network

The current electricity situation demands the adoption of renewable resources such as solar energy in order to contribute to the advancement of sustainable development, as excessive use of fossil fuels and their catastrophic impacts on the environment. Unlike fossil fuels, the sun has more energy than is required to power the entire planet, but due to the time-varying nature of solar energy, converting it into electricity economically and effectively is a major challenge. For this, forecasting solar energy a few days in advance for the estimation of the likely energy generation for those few days can be proposed as an effective solution. With the same objective, the present research offers a realistic solution to this problem known as an artificial neural network composed of 6 input layers, 15 hidden layers, and one response layer for solar power generation forecasting. The proposed ANN model has been simulated using solar power irradiance data from Panchkula region of Haryana to validate its performance. The experiment results show that the proposed ANN model is an effective approach to solar power generation forecasting and can also be useful with integration to photovoltaic systems.
Decentralized Network for Cloud Storage

The amount of data loss on corporate servers in cloud environments has increased significantly. In the cloud, there are many security compromises and account hijackings, resulting in severe vulnerabilities for the service. Cloud computing is one of the most popular on-demand services. Over the past decade, cloud computing has been incorporated into various industries, including health care and the military. Security has, however, been a major concern with cloud computing. There have been many approaches and security implications implemented. However, when it comes to the security and privacy of data, there are loopholes. By decentralizing the cloud, which is currently a centralized entity, the authors attempt to improve cloud security. It is possible to solve these problems by deploying a decentralized cloud. Client software encrypts, fragments data, and then a client engine distributes the data among a number of hosting nodes (servers) spread throughout the world. As a result, the user has complete ownership of his data. In their study, the authors found that decentralizing cloud solved most of the problems of centralized cloud, since it increases the security of data by fragmenting and encrypting fragments.
Exploratory Data Analysis And Predict Income Udemy Course Instructors Using Machine Learning Algorithm

Udemy Course as an online course platform that has been recognized by both students and teachers as a medium that facilitates the transfer and receiving of knowledge, while also providing income to the Instructors. This study aims to explore datasets in Udemy Course and predict the income of the Instructors in the Udemy course. Datasets are taken from Kaggle. The research procedure begins with the collection of datasets from Kaggle, followed by datasets pre-processing, then exploratory data analysis is carried out and predicted, the implementation of machine learning random forest techniques is carried out. The results of this study show a description of the Udemy Course datasets for 2010 - 2022, course distribution prices, customer distribution, course content length distribution, number of courses for each category, number of subscribers for each category, and date of publication. In addition, this study shows that the percentage score n_estimator of machine learning random forest techniques in predicting the income of Udemy course instructors is 94%.
Dataset for Face-mask Recognition in Poor Visibility Conditions based upon IoT enabled Robotics

There has been a significant increase in demand and use of facemask after the increasing transmission of the Corona Virus. Wearing face masks can help in reducing the spread of the virus from one person to another. But some people still don’t wear a mask and checking it manually in a huge crowd can be very difficult and tedious. Various face mask detection systems have been made for making this task easy. In poor visibility conditions detecting facemasks becomes more difficult. The ubiquity of haze substantially reduces the quality of images. To restore the quality of hazy image various image dehazing algorithms have been designed by researchers. However, there are not many studies that encapsulate dehazing algorithms and techniques used for spotting objects (here, facemasks) based on deep learning. This paper aims to propose an idea for spotting face masks in extremely poor visibility conditions by creating a dataset of images captured in different densities of haze by a robot using digital image sensors.
Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively; and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively; and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset.
Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively.
Evolutionary Parameter Optimization on Neural Network Models for Earthquake Prediction

Earthquakes are a major obstacle to sustainable development, hindering social and economic growth. This study uses a model to predict the magnitude of earthquakes that occur from the Sunda Strait to Sumbawa Island. Earthquake prediction is important to take preventive measures and predict damage accurately. Several Earthquake Prediction (EQP) approaches have been proposed; however, these approaches only identify anomalies without distinguishing noise, thereby reducing the accuracy of predicting the probability of an earthquake occurring. The proposed model is a Neural Network (NN) optimized using evolutionary parameters to produce a lower and better error rate. Evolutionary Parameter Optimization was chosen because this parameter is superior in hyperparameter selection to achieve more optimal accuracy compared to other parameter models. Evolutionary Parameter Optimization was chosen because this parameter is superior in hyperparameter selection to achieve more optimal accuracy compared to other parameter models. This research aims to determine the best hyperparameter model to increase the accuracy of the Neural Network. The results of this study obtained the Root Mean Square Error (RMSE) value of the M 8 windowing combination using the Neural Network algorithm of 0.823. After increasing accuracy by optimizing using evolutionary parameters, the RMSE results obtained are 0.822. In this study, an increase in accuracy was obtained with a decrease in the RMSE value obtained by 0.001. Optimizing the Neural Network's evolutionary parameters improves the RMSE accuracy value so that the proposed model is better.
Selection of Area and Collect Required Data for Power Prediction of a Solar Plant

Solar energy is an economical, user-friendly, and practical solution to the global energy dilemma, electricity generates by using solar energy. It can be possible by using two methods- direct method and indirect method. In the direct approach, Photovoltaic modules convert solar irradiation into the form of electrical energy and by indirect technique, concentrated solar power (CSP) collects thermal energy using facilities such as parabolic troughs and Linear Fresnel collectors. To improve the strength of solar power systems, this research focuses on the improvement of accuracy neural network algorithms like NARX and Back Propagation (BP). NARX and BP are utilized for solar energy prediction and solar energy costs can be minimized when improvement is provided to the solar plant efficiently. According to the simulation findings, NARX based neural network increases the solar energy efficiency and better prediction of power when compared to previous or BP methods.
Predicting Priority Time Patrol using Triple Exponential Smoothing and AHP

Prediction is a systematic process of determining what will happen in the future based on current historical data, which reduces inaccuracies. The purpose of this study is to predict the number of crimes in the future so that it can be used as a basis for decision support to determine time priorities for the police to carry out patrol duties. Researchers use Analytical Hierarchy Process (AHP) as one of the methods used to make decision support and Triple Exponential Smoothing as a method used to predict the number of crimes. The study's results used the Triple Exponential Smoothing method with a vulnerable time when the crime occurred at dawn, afternoon, evening, and night with an average accuracy of 63.60%. In the AHP method, the pairwise comparison assessment for each hierarchy consistently gives a CR value of less than 0.1. Thus, the priority of patrol time generated by the two methods is acceptable.
Comparison of Decision Tree and Support Vector Machine for Predicting Jakarta Air Quality Index

The people in the capital city of the Republic of Indonesia, Jakarta, have been living for years with air pollution. Air quality has been a concern for a long time due to the health risks it has on people, especially those at risk. Using classification algorithms, we would like to implement data mining for the air quality index and make predictions from our capital city, DKI Jakarta. This research aims to compare Decision Tree and Support Vector Machine in predicting Air Quality Index in DKI Jakarta in 2020. The dataset was collected from the Jakarta Open Data Web, and the results of this research highlight the effectiveness of data mining techniques in predicting air quality index levels in Jakarta and emphasize the importance of selecting the appropriate classification algorithm for data mining applications. This research shows that SVM, compared to the Decision tree, has a better result with an accuracy score of 87.86%. On the other hand, Decision Tree has a score of 90.56%.
Exploring Shifted Domain Problem within MNIST Dataset

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.
In-House Facial Data Collection for Face Recognition Accuracy: Dataset Accuracy Analysis

The face recognition system is a field of research that can help identify a person for either a security system or a search system because faces are distinctive from each person. The development of a human face recognition model requires several main components, such as scale, light, and expression. This study aims to collect accurate samples of Indonesian faces for face detection algorithms. Determining these facial features is challenging to identify Indonesian faces because Indonesia consists of various tribes. In contrast, the classification of tribes in Indonesia consists of Malay and non-Malay tribes. Therefore, this study uses an in-house dataset of Indonesian people's faces which will be tested using the FaceNet algorithm. The results of this study indicate that high accuracy is obtained even when using simple devices. The benefit of this study is that using the in-house method used by researchers can improve the accuracy of test results even though using an in-house dataset.
Implementation of the K-Means Algorithm for Clustering the Characteristics of Students Receiving Kartu Indonesia Pintar Kuliah (KIP-K)

The limited quota of recipients of the Kartu Indonesia Pintar Kuliah (KIP-K) causes the host universities to select applicants to get students who are eligible to receive KIP-K based on academic achievement, non-academic achievements, and family economic conditions. However, after the lectures started, some students who received KIP-K lacked discipline in undergoing lecture procedures and experienced a decrease in their achievement index (IP). Therefore, it is necessary to explore knowledge about the characteristics of KIP-K recipient students by conducting clustering modeling. So, in this study, clustering modeling was carried out on student data receiving KIP-K at a university by applying the Cross-Industry Standard Process for Data Mining (CRIPS-DM) method and the k-means clustering algorithm. This study chooses a clustering model with a value of k=2, which has the smallest Davies Bouldine index (DBI) value of 0.35. This clustering resulted in 2 clusters where student characteristics showed significant differences in the attributes of the distance from home to the campus location and relatively minor fluctuations in IP from the first semester to the fourth semester. From mapping the characteristics of KIP-K recipient students, knowledge can be used as material for higher education decisions in selecting KIP-K registrants to minimize the future academic problems of KIP-K recipient students.
Voice Classification of Children with Speech Impairment Using MFCC Kernel-Based SVM

Speech-impaired children really need attention in education and socially in everyday life. Exceptional education grows from an early awareness that some children need a different education from ordinary education to reach their potential. A speech disorder is the inability of a person to speak. This is caused by the lack or non-functioning of the organs for speaking, such as the oral cavity, roof of the mouth, tongue, and vocal cords. In addition, there are also deficiencies in the sense of hearing, delays in language development, and damage to the nervous system and muscle structure. Therefore, researchers designed a speech classification system for speech-impaired children using SVM based on the MFCC kernel. The purpose of this research is to detect and classify the voices of speech-impaired children using the SVM method with the MFCC kernel. In the tests carried out, it can be concluded that the classification of speech-impaired children can use the Linear kernel SVM method. The results of the classification of blind children are very high, reaching 84.7%. The Linear SVM method can help improve the accuracy of the voice classification of speech-impaired children properly. So that in the classification of speech-impaired children using the Linear SVM method, it can help to be used in communicating with speech-impaired children.
Public Sentiment Analysis of KOMINFO Data Leaking by Bjorka using Support Vector Machine

An account with the name Bjorka claims to have obtained billions of SIM card registration data in the form of Identity Card and Family Card Nuber from the government database of the Ministry of Communication and Informatics (Kemkominfo), people start questioning the cybersecurity of the government database. The appearance of the Bjorka hacker caused various responses on Twitter, some supported Bjorka’s action and some disagree. Hence the need for sentiment analysis to determine public sentiment is more towards negative or positive, so the government can do evaluation as well as strategic planning to deal with future data leaking incidents. This study uses tweets that contain public responses to predict negative or positive sentiment using Support Vector Machine algorithm. From a total of 1017 public response data, have been found 97.35% (990 tweets) to have negative sentiment and 2.65% (27 tweets) have positive sentiment, so it can be known that public responses are towards negative about data leaking by Bjorka. In conclusion, education to the public about data leaks by Bjorka is not the main priority to do for the government. The government can focus more on dealing with other sectors such as improving the security of the data itself.
Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.
Discord Bot Design for Hate Speech Sensor Using Convolutional Neural Networks (CNN) Method

Discord is growing in popularity, makes hard for an admin of Discord server maintaining their member in their everyday chat activity in their server. This no longer an issue if there is Discord bot that can detect hate speech feature in text message that member send and automatically censor them. The classifier for this experiment is using Convolutional Neural Network (CNN) method. The dataset for training and validation model are containing total 6 category of hates speech, abusive language, religion, race, gender, physical, and non-hate speech. The Discord bot program only can classify a message in Indonesian language. The dataset used for training and validation models was obtained from Kaggle and for additional data taken from Discord server messages totaling 18,986 sentences which will be divided by 80% training data and 20% test data. The final results of the training model experiment, this CNN model can classify test data with an average precision value of 89%, 90% recall, and 88,33% F1 score. The CNN model is integrated into a bot application which will be tested on messages sent from the test Discord server. Out of 279 messages, the designed Discord bot can obtain an accuracy of 70.6%.
Classification of Emotions on Song Lyrics using Naïve Bayes Algorithm and Particle Swarm Optimization

A song is a unity of sound that contains a tone and lyrics. A song can contain a variety of emotions. Emotions in the song can arise because of the combination of lyrics and tones that create a beautiful sound and harmony. This research is about the emotional content of the song lyrics. This research began with collecting datasets in the form of song lyrics from kapanlagi.com, liriklaguindonesia.net, and liriklaguanak.com as a provider of song lyrics. Then preprocessing data consists of case folding, tokenizing, stop removal, and stemming. After that, the part of speech (POS) tagging process automatically labels the word in the text according to the word class. Labeling a word, whether it's a verb, adjective, or description, to be able to determine the song's emotional lyrics according to what we listen to takes the right method. The method used is the Naive Bayes Classifier and Particle Swarm Optimization methods, as methods used in performing text classification. In some studies, it was mentioned that the Naive Bayes Classifier method shows good results in the case of the classification of Indonesian text information, with an accuracy of 90%–96% using an inertia weight score of 1.0.
Javanese Batik Image Classification using Self-Organizing Map

Batik has been officially recognized as one of Indonesia’s cultural heritages by UNESCO, and Indonesia now has a batik day which is always celebrated every 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> October since 2009. The diversity of Indonesian batik patterns makes it difficult to recognize. So, Batik becomes an object of research related to pattern recognition and classification. This study proposes a method for classifying batik motifs using a Self-organizing Map (SOM) on an Artificial Neural Network (ANN). This study aims to classify Javanese batik motifs through computation using artificial intelligence. The samples of the batik motifs used in this study were the Kawung, Megamendung, and Parang motifs. The amount of data used is 150 images, where the number of each motif is 50 images. In pre-processing, we convert all images to grayscale and then perform segmentation to anticipate images that are not suitable. Feature extraction is done through three algorithms, namely Gray Level Co-occurrence Matrix (GLCM), RGB (red, green, blue), and HSV (Hue, Saturation, Value). The features obtained will be divided into training and testing data. SOM is used as a classifier. The highest accuracy of 77% is obtained by using the HSV feature. When combining all the features for classification by using the same portion of data for training, we obtained an accuracy of 90%. This result showed the potential of the SOM algorithm when classifying a large number of batik patterns.