Reviewerly/exHarmony huggingface dataset:
The Role Playing Method on the Reading Ability of Elementary School Students

Reading is the most common learning difficulty experienced by elementary school students in the early or lower grades. Early-grade elementary school students have significant problems related to reading and language skills. It impacts the reading ability scores of Grade 2 elementary school students, which are still low, so new methods are needed to improve student's reading skills. This study aimed to analyze the role-playing method's use on the reading ability of Grade 2 elementary school students. This research is a type of experimental research. This study used a pre-experimental research method with a one-group pretest-posttest design (pretest test-posttest single group). The subjects in this study were all 2nd-grade elementary school students, totaling 32 students consisting of 13 girls and 19 boys. The methods used to collect data are observation and tests. The instrument used is a matter of difficulty. Data analysis techniques using inferential statistical analysis. The result of the study show that the role-playing method significantly improves the reading ability of grade 2 elementary students. Thus, giving the role-playing process can improve the reading ability of grade 2 elementary students.
Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing

To enhance the performance of Chinese language pronunciation evaluation and speech recognition systems, researchers are focusing on developing intelligent techniques for multilevel fusion processing of data, features, and decisions using deep learning-based computer-aided systems. With a combination of score level, rank level, and hybrid level fusion, as well as fusion optimization and fusion score improvement, these systems can effectively combine multiple models and sensors to improve the accuracy of information fusion. Additionally, intelligent systems for information fusion, including those used in robotics and decision-making, can benefit from techniques such as multimedia data fusion and machine learning for data fusion. Furthermore, optimization algorithms and fuzzy approaches can be applied to data fusion applications in cloud environments and e-systems, while spatial data fusion can be used to enhance the quality of image and feature data In this paper, a new approach has been presented to identify the tonal language in continuous speech. This study proposes the Machine learning-assisted automatic speech recognition framework (ML-ASRF) for Chinese character and language prediction. Our focus is on extracting highly robust features and combining various speech signal sequences of deep models. The experimental results demonstrated that the machine learning neural network recognition rate is considerably higher than that of the conventional speech recognition algorithm, which performs more accurate human-computer interaction and increases the efficiency of determining Chinese language pronunciation accuracy.
Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan

In marriage, the thing that is most avoided is a divorce. Divorce is the termination of the husband and wife relationship which is carried out legally at the time of trial. From year to year, there is an increase in the number of divorces in Indonesia, including the number of divorces in Kuningan Regency. This study analyzes divorce cases in villages in Kuningan Regency, the analysis is carried out by using data mining clustering methods using the K-Means algorithm. The clustering method is grouping data based on the same characteristics. In determining the number of clusters by using the value of the smallest Davies Bouldin Index, it is hoped that the number of clusters formed can be more optimal. The results of this study are that there are 4 clusters consisting of villages or sub-districts with different divorce rates, namely the highest divorce rate, high divorce rate, medium divorce rate, low divorce rate, and lowest divorce rate
Application of Deep Learning LSTM in Online Power Prediction on Three-Phase Power Transformer

Electrical energy plays an important role in daily life, especially companies. The 3 Phase Power Transformer is one of the important electrical components that is very influential in distributing electrical energy to companies as is the case in PT. Semen Baturaja (Persero). 3-phase power transformers require attention because they are one of the components that are prone to interference, this interference can hinder the effectiveness of using electrical energy as a company support for employees to work. As one of the disturbances for 3-phase power transformers is overload or excessive power usage, overload can raise the temperature at the winding and reduce its service life. Artificial intelligence can be one of the keys to predict the use of power transformers in the future, especially deep learning by utilizing the LSTM algorithm. Optimal power prediction requires a lot of maximum input variables so that in this study, it not only adds an offline learning mode but adds a learning mode that can directly access the company's Power Quality Monitoring (PQM) website online with an average accuracy value of 86.62%.
Patent Relating to Artificial Intelligence and Liability for Artificial Intelligence Application from the us Law Perspectives

Abstract While computer programs were purely tools for people to perform their creative activities 50 years ago, computer programs today, so called Artificial Intelligence(AI), can create intellectual products independently from people. The US has a leading role over all other countries in top-tier AI research, as well as most AI-related patent application filing. Does the US protect such inventions? If not, how does the US deal with liability concerning AI application? How does the development of AI call for intellectual property right law reforms in the US? The paper aims at addressing the above questions. Moreover, the paper provides a comprehensive analysis on AI-related patent in the US in comparison with the Vietnamese legal system. Descriptive legal research, analytical approach, comparison of legislation and case studies are employed in the paper.
Penerapan Algoritma Hash Based dalam Penemuan Aturan Asosiasi Penjualan Tanaman Hias

Technology is very influential in the world of increasingly fierce business competition so that business people must find strategies to increase sales results in the midst of business competition. Ornamental plant sellers must be smart in managing stock and making strategies in selling ornamental plants. Transaction data can be processed into information needed to increase sales results, one of which can be used as an analysis of the rules of the buyer transaction association in purchasing ornamental plants so that it can be processed and can support decision making on ornamental plant supplies and can assist officers in recommending other ornamental plants to buyers in a cross selling strategy. Knowing the ornamental plants that are often purchased will be a top priority that must be provided so that there is no stock shortage. In this case, data mining is needed to manage sales transaction data for ornamental plants at the Sindy Flower Shop using a Hash Based algorithm. Hash Based Algorithm that can optimally determine the frequent itemset of candidate itemset. In its application in determining the rules for selling associations of ornamental plants by applying a Hash Based algorithm to obtain frequent itemsets for the 3-itemset Dahlia, Empasen and Melati which are a combination of 3-itemset ornamental plants which are prioritized in sales with a support value of 25% and confidence of 60%
Analysis of Academic and Administration Information Systems Using Servqual and Kano Methods

Academic and Administrative Information System (SIAKAdm) is an online-based information system service for students of Hangtuah University Pekanbaru. With the development of information systems in the academic field, we must also test information systems, there are several problems that users feel that the quality of service of the Academic Information System (SIAKAdm) has not run effectively and efficiently, such as, there are often delays when filling in KRS, color contrast in the system is too disturbing to the user's eyes, there is no edit menu on the student profile, and finally there is no complaint lyanan menu or C3 servicedesk menu. This research was conducted using the ServQual method and the Kano method. The ServQual Method can be said to be a method used to measure the quality of service attributes of a dimension, while the Kano Method can be interpreted as a model built to understand how well their product or service meets the needs of users. This data collection process is by conducting interviews and distributing questionnaires of 98 respondents using the Simple Random Sampling technique. The data was obtained using IBM SPSS 26 and calculated the GAP value using Microsoft Excel. The results of this study The highest gap value was in the Assurance variable, with a GAP value of -4.54. While the lowest gap value is in the Responsivennes variable of -2.51.
Implementasi Algoritma K-Nearest Neighbour dalam Memprediksi Stok Sepeda Motor

PT. Dasatama Cemerlang Motor is a company engaged in the automotive sector. With the increasingly fierce competition among the automotive industry, companies are required to be able to handle inter-industry competition. Sales system PT. Dasatama Cemerlang Motor uses a cash or credit system. For every motorcycle sale, the admin inputs sales data using Ms.Excel. Even though Ms.Excel has many features and functions that are used to process numbers, it cannot predict annual motorcycle sales for the future as a reference in marketing strategy. Because of that, forecasting is needed which will help the company to find out the trend in the number of motorcycle sales for the coming year. The KNN algorithm is one of the methods used for classification analysis, but in the last few decades the KNN method has also been used for prediction. KNN looks for the shortest distance between the data to be evaluated and its K closest neighbors. The results achieved in this study resulted in the number of motorcycles for each brand that will be sold in 2022 obtained from the addition of 5 motorcycles for each sale of each motorcycle brand. Based on the research results, the prediction accuracy rate using the KNN method is 97%.
Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index

In K-Means Clustering, the number of attributes of a data can affect the number of iterations generated in the data grouping process. One of the solutions to overcome these problems is by using a reduction technique on the dimensions of the dataset. In this study, the authors apply the Gini Index to perform attribute reduction on the data set to reduce attributes that have no effect on the dataset before clustering with K-Means Clustering. The dataset used to be tested as a testing instrument in this research is Absenteeism at work obtained from the UCI Machine Learning Repository, with 20 attributes, 740 data records and 4 attribute classes. The results of the tests in this research indicate that the number of iterations obtained from the comparison of tests using the K-Means in a Conversional (Without Attribute Reduction) is obtained by the number of 9 iterations, while the K-Means with attribute reduction with the Gini Index obtains the number of iterations totaling 6 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering in a Conversional (Without Attribute Reduction) is 1391.613, while in K-Means Clustering with attribute reduction with a Gini Index, it is 440.912. From the results of the proposed method, it is able to reduce the percentage of errors and minimize the number of iterations in K-Means Clustering by reducing the dimensions of the dataset using the Gini Index
Klasifikasi Nama Paket Pengadaan Menggunakan Long Short-Term Memory (LSTM) Pada Data Pengadaan

Every year the government always holds procurement of goods and services (tenders) which are informed through the Electronic Procurement Service (LPSE) or the General Procurement Plan Information System (SIRUP). The process of selecting the type of procurement is still manual, namely by selecting the package category so that it is possible for mistakes to occur such as the type of service procurement into the category of goods procurement type or vice versa. Therefore, this research proposes to use the Natural Language Processing (NLP) method that can classify these packages based on existing categories. The method used is Long Short-Term Memory (LSTM) by comparing existing classification methods such as naïve bayes, logistic regression, decision tree, XG Boost, Gradient Boost, Random Forest and Support Vector Machine. The results obtained by the LSTM method have a higher accuracy than other methods, with an accuracy of 90.25%. With a parameter configuration of 100 units in the LSTM layer, epoch 10, batch size 64 and validation step 5
Implementasi Metode Forward Chaining dan Certainty Factor Pada Sistem Pakar Diagnosis Penyakit Sinusitis

Sinusitis is similar to symptoms of minor illnesses such as runny nose, cough, and headache. Mild symptoms cause people to ignore these symptoms often. Besides, limited costs and doctor's practice hours make consultations difficult. Signs of infection that are not treated quickly can cause complications, and the infection can spread to the eye sockets or the brain. One of the ways to diagnose sinusitis early is to use an expert system so that in this study implemented the Certainty Factor and forward chaning methods to create a system that can diagnose sinusitis according to the symptoms felt and provide information about the disease and how to treat sinusitis symptoms early. Forward chaining is used as an inference method, and the certainty factor is used to calculate the level of probability of disease based on the expert's belief value and the symptoms of sinusitis selected by the user. The data used is disease data consisting of four types of sinusitis and fifteen symptoms. Based on the results of black box testing, the system that has been built functions well as expected. Expert systems in diagnosing have an accuracy of 70%.
Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat

The use of the artificial neural network (Backpropagation) method can be used in determining the best architectural model for predicting the number of elementary school students in the Siantar Barat District. The dataset used is a dataset on the number of Elementary School (SD) students in West Siantar District, Pematang Siantar City in 2017-2021 obtained from the Website of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia (https://dapo.kemdikbud.go.id /pd/3/076303). The dataset is then divided into 2 parts, namely the training and testing dataset. In the training datasets, attribute X1 is a dataset for 2017, X2 is the dataset for 2018, X3 is a dataset for 2019, and attribute Y (target) is the dataset for 2020. For the test datasets, attribute X1 is the dataset for 2018, attribute X2 is a dataset for 2019, attribute X3 is a dataset for 2020 and attribute Y (target) is a dataset for 2021. The results obtained from the analysis of the Backpropagation and virtualization methods using the MatLab application can be generated with a valid dataset and produce an accuracy rate of 87.5% in architectural models 3-9-1. So that the Backpropagation method can be used as a prediction method that makes it very easy to find predictions.
Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Warga Penerima Bantuan Sosial

Social Assistance (BanSos) is a government program intended for lower-middle families. Social assistance is assistance given to the community, especially the lower middle class, which is not continuous and selective. Many types of social assistance are provided by the government with the aim of prospering and helping the community's economy. However, the problem that occurs is that there are still many people who receive social assistance that are not people who deserve to receive social assistance, while the lower middle class who should receive social assistance are neglected and do not receive the social assistance. It should be for the distributor or the kelurahan to form groups for residents who are entitled to receive social assistance. The process of grouping the recipients of social assistance can be done by processing the data of residents who have the right to receive the social assistance. The data processing can be done by using data mining. One of the algorithms that can be used to solve problems in data mining is the K-Nearest Neighbor algorithm. After carrying out the overall process with a value of K = 5, it was found that the new data from residents was declared eligible to receive social assistance
Penerapan Metode Forward Chaining pada Sistem Pakar untuk Identifikasi Kerusakan Mesin Ekskavator

The application of information systems is handy for business needs as well as the needs of human activity. One form of an information system is an expert system, which is used to transfer human thinking abilities to computers, capable of solving problems like how experts think. The problem that often occurs with mechanics or excavator operators is the limited number of experts to provide consultations to mechanics who need solutions to damage that occurs to machine parts. This study uses the forward chaining method to describe the identification, diagnosis, and explanations of excavator engine damage. The knowledge representation used is IF-THEN rules and combining rules that can produce a conclusion. Forward Chaining has a fact-based search technique which is then matched to find directions. The forward chaining method in several studies is able to produce good accuracy values. In making this system, it is hoped that experts in the field of heavy equipment engines and several manuals about excavator machines will get accurate data about machine damage information. Applying the forward chaining method to this expert system can produce an accuracy rate of 90% through 10 tests
Implementation of Neural Machine Translation for English-Sundanese Language using Long Short Term Memory (LSTM)

In this modern era, machine translation has been used all over the world for solving humankind’s problems such as it deals with language. Machine translation is almost used by people who want to translate their native language into their foreign language. The international language being used is the English language. Machine translation is the task to translate a source language to another language. The input of it is a word or a sentence from the source language and it will be translated into another language. The input of it is a word or a sentence from the source language and it will be translated into another language. There are many purposes for using machine translation such as learning another language, communicating, finding a certain or better word to use, and even writing something in a book or another article. Several methods have been conducted to do the machine translation task such as the statistical approach and the neural approach In terms of Sundanese machine translation, there are several methods or several approaches that other researchers have conducted. However the study about Sundanese machine translation, none of the research conducted the English into Sundanese language. In this study using the encoder and decoder LSTM architecture achieve a good result regarding building a model for machine translation task. The performance of this model has achieved 0.99 accuracies in both training and testing as well as less than 0.1 loss value to both training and testing data. This model also achieves more than 0.8 average BLEU score for both training and testing data.
Kmeans Clustering Segmentation on Water Microbial Image with Color and Texture Feature Extraction

Image segmentation is one of the analytical processes for digital image recognition, where this process divides the digital image into several unique regions based on homogeneous pixels. The process of homogeneous grouping images is based on several colour, texture and shape features. Colour in digital image processing is very important because colour has many information humans can easily understand. Colour has various features, combining colour intensity and grey (grayscale) and binary (black and white) values. However, the colour feature extraction process has many weaknesses. If the object used has a very small si[1]ze and range area, the use of colour features needs to be combined with extraction, and the segmentation process can be maximized. This study uses colour and texture features in the extraction process. It uses bacterial objects (microbes) from water, with limited image quality and objects that tend to be difficult to identify. The colour space feature extraction process is combined with a Gabor filter so that the segmentation process produces high-quality accuracy. Good. The Gabor filter used in this study is combined with the L*a*b space vector to increase accuracy in the segmentation process. The results showed that the use of texture features resulted in an increase in accuracy of 17.5% by testing the cluster value of 1.2.
Klasifikasi American Sign Language Menggunakan Convolutional Neural Network

Communicating is a necessity for all groups or individual because each individual should communicate with their surroundings. Communicating can also make us get information so that it can be used as a reference to be able to adapt. Verbal language used by speaking out loud is a way of communicating with individuals, but not all individuals can communicate with it, especially there are some individuals who have hearing limitations. Because of these limitations, another program that can be used is through sign language. Language requirements are languages that are usually used by people with disabilities in terms of hearing or speaking and sign language also has a fairly well-known sign language standard, namely the American Sign Language (ASL) standard. Unlike languages in the world, sign language is also often of little interest to most people because people's interest in sign language is still lacking so that most people are unable to understand their language. Sign language has many types, one of which is sign language by using hands to form letters and numbers. In overcoming these problems, the solution is to create a system that can be used to recognize sign language, the system developed is a system that used machine learning technology. This study will propose an ASL classification approach through data preprocessing and a convolutional neural network model. The proposed model can classify ASL hand posture images to be translated into the alphabet. The result of this study is an model with accuracy of 99.8% obtained from the process of merging preprocessing data and the convolutional neural network model.