Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF GENETIC ALGORITHM BASED ON JAVASCRIPT IN OBJECT ROUTING SHORTEST TOUR ON TIMOR ISLAND

The purpose of this study is to apply a genetic algorithm using a programming language that can be used to determine the shortest route to tourist attractions in Timor Island, East Nusa Tenggara. The application of genetic algorithms expected to be able to obtain the shortest route information that is most effective, the route that do not take a long time for tourists, and the presentation of route information for tourists. The method used is genetic algorithm to build the shortest route system for tourist attractions in Timor Island. The results of the system application shown that the optimization of travel routes that have been built can provide convenience in choosing tourist travel routes in Timor Island. Route optimization can help the process of making routes from the first location of departure and the attractions to be visited. The system is also used to assist domestic tourists in getting to know the attractions of Timor Island for solutions the shortest route.
OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION

Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value. A negative binomial regression can overcome an unfulfilled equidispersion assumption where the mean is greater than the standard deviation value (overdispersion). This method is more flexible because it does not require that the variance be equal to the mean. The case studies used in this research are cases of anemia in women of childbearing age (WCA) in 33 provinces of Indonesia. This study aims to apply the Poisson regression method and negative binomial in the case data of anemia in WCA to prove the model's goodness and find the factors that influence anemia in WCA. This data was obtained from biomedical sample data for Riset Kesehatan Dasar (Riskesdas) and data obtained from the website of the Badan Pusat Statistik (BPS) in 2013. By applying these two methods, the result is that negative binomial regression is the best model in modeling WCA cases with anemia in Indonesia because it has the smallest AIC value of 221.72; however, the difference is not too far from the AIC in the Poisson regression model, which is 221.83. It can also be supported that Poisson regression is unsuitable for the analysis because of the case of overdispersion. With a significance level of 10%, the number of WCA affected by malaria per 100 population influences cases of WCA anemia. At the same time, other independent variables have no effect.
SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC

Sentiment analysis is a data text analysis that classifies data into positive and negative sentiments. This study aims to obtain the results of sentiment classification related to Merdeka Belajar Kampus Merdeka policy on Twitter using support vector machine algorithm with Word2Vec feature extraction. Support Vector Machine is a classification algorithm that separates data classes using the optimum hyperplane. Text data used in sentiment analysis must change its numerical form by performing feature extraction. In this study, the feature extraction used is Word2Vec which represents words in vector form. Data in this study are tweets with the keyword "Kampus Merdeka" uploaded on Twitter as many as 10000 tweets. After preprocessing text data, data used to analyze sentiment was 1579 tweets. Sentiment classification resulted in classification model accuracy 89.87%, precision 91.20%, recall 84.44% and F-Measure 87.68%. Classification sentiment using support vector machine with Word2Vec feature extraction in this study produces a good model.
Unsupervised Representation Learning of High Dimensional Structured Data: A Comparative Study

Unsupervised representation learning has emerged as a powerful technique for extracting meaningful features from high dimensional structured data, such as graphs, time series, text, and video. In this paper, we present a comprehensive comparative study of various unsupervised representation learning methods applied to different structured data modalities. We focus on evaluating the performance of these methods in terms of their ability to capture the underlying structure and relationships within the data, as well as their scalability and computational efficiency. Our analysis provides insights into the strengths and weaknesses of each method, and offers guidance for practitioners seeking to apply unsupervised representation learning techniques to their own structured data problems.
ASSOCIATION RULES IN RANDOM FOREST FOR THE MOST INTERPRETABLE MODEL

Random forest is one of the most popular ensemble methods and has many advantages. However, random forest is a "black-box" model, so the model is difficult to interpret. This study discusses the interpretation of random forest with association rules technique using rules extracted from each decision tree in the random forest model. This analysis involves simulation and empirical data, to determine the factors that affect the poverty status of households in Tasikmalaya. The empirical data was sourced from Badan Pusat Statistik (BPS), the National Socio-Economic Survey (SUSENAS) data for West Java Province in 2019. The results obtained are based on simulation data, the association rules technique can extract the set of rules that characterize the target variable. The application of interpretable random forest to empirical data shows that the rules that most distinguish the poverty status of households in Tasikmalaya are house wall materials and the main source of drinking water, house wall materials and cooking fuel, as well as house wall materials and motorcycle ownership.
SUBDISTRICT CLUSTERING IN WEST JAVA PROVINCE BASED ON DISEASE INCIDENCE OF JKN PARTICIPANTS PRIMARY SERVICES

One of the efforts that can be done to optimize health services and the distribution of facilities and infrastructure efficiently in a wide scope is by profiling and clustering areas in the province of West Java to the scope of sub-districts that have similar characteristics of disease category. The methods that will be compared to get the best clustering are hierarchical clustering and ensemble clustering. The data used as the object of research is the BPJS Kesehatan capitation primary service sample data for the 2017-2018 period. Some of the important variables used include: primary disease diagnosis data (ICD-10) of patients at the puskesmas, service time, type of visit, and location of service sub-district. This study uses several evaluation metrics Silhouette coefficient, Dunn index, Davies-Bouldin index, and C-index to determine the optimal number of clusters formed. In addition, descriptive analysis and visualization of the clustering results are also used as considerations in selecting the optimal cluster. Based on the evaluation results, the optimal method is hierarchical clustering with complete linkage. This method produces three clusters: cluster 1 consists of 5 sub-districts that have a high/dominant mean value in almost all disease categories, cluster 2 consists of 26 sub-districts that have a medium mean value, and cluster 3 consists of 589 sub-districts that have a low mean value. Most of the members of clusters 1 and 2 are sub-districts located in the districts/cities around the national capital (DKI Jakarta) and the provincial capital (Bandung) while the members of cluster 3 are mostly sub-districts located in suburban districts/cities or far from the central government.
Towards patterns of responsible consumption and production: an exploration of do-it-yourself practitioners through motivations, conceptions, and fabrication processes

Defined as a way for practitioners to conceive and fabricate products by and for themselves, do-it-yourself (DIY) has recently garnered interest as a practice that may encourage patterns of responsible consumption and production (RCP). However, conceptualising the DIY practice as one that reduces resources, energy and waste is still debated among the scientific community as it may require the use of new materials and technologies with higher energy consumption. In this context, this study has the following objectives: 1) to explore practitioners' environmental, social and ethical motivations to DIY; and 2) to document their DIY process. To this purpose, 37 semi-structured interviews were conducted with practitioners from Quebec, Canada. Based on the findings, three personas were developed to categorise practitioners and examine their profile types. Finally, this paper proposes a definition of responsible DIY to guide practitioners towards patterns of RCP in their practise of DIY.
LOGISTIC MODELING TO PREDICT THE INTEREST OF THE INDONESIAN PEOPLE FOR BUYING FLOOD IMPACTED INSURANCE PRODUCTS

Indonesia is a country located on the equator and in the form of an archipelago. It has a high potential for various types of hydrometeorological-related disasters, such as floods, flash floods, droughts, extreme weather, etc. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. According to BNPB (2013), flooding is a disaster that relatively causes the most losses. Losses caused by floods, especially indirect losses, may rank first or second after an earthquake or tsunami. Floods cause so many losses, and it is necessary to have disaster mitigation efforts to minimize the possibility of flood risks. One risk mitigation due to natural disasters is buying insurance products. However, not everyone buys flood-impact insurance products due to economic and social factors. This study aims to create a model with the Logistics Regression Model to determine the factors influencing Indonesian people's interest in purchasing flood-impact insurance products. The research data is from 140 households in East Jakarta, Indonesia, using a non-probability purposive sampling technique. Furthermore, with a significance level of 10%, the logistic regression model obtained 14 significant regression coefficients. In the end, the obtained model is evaluated based on its level of accuracy. The results showed that the accuracy rate was almost excellent, namely 89.3%.
APPLICATION OF THE BACKPROPAGATION METHOD TO PREDICT RAINFALL IN NORTH SUMATRA PROVINCE

Natural disasters are to blame for the high level of community loss. This is due to the community's lack of information about potential disasters around them. As a result, public understanding of disaster response is extremely low. As a result, weather information is critical for the smooth operation of human activities and activities, such as determining the amount of rainfall. The goal of this research is to identify the best model for predicting rainfall in North Sumatra Province and to forecast rainfall trends for the coming year. The rainfall time series data used in this study were collected from six stations in North Sumatra Province over the last ten years, including the Sibolga Meteorological Station, Aek Godang Meteorological Station, and Silangit Meteorological Station. Backpropagation is used in this study. Backpropagation is one of the methods used in artificial neural networks, which are usually divided into three layers: an input layer, a hidden layer, and an output layer connected by weights. During the training stage, the learning rate, iteration, and number of nodes in the hidden layer were all tested. Following the training process, the best model will be used for testing. The best model was obtained using rainfall data from North Sumatra Province, with an optimal iteration of 1000 iterations, an optimal learning rate of 0.1 in the learning rate trial, and the best number of hidden 5 nodes. During the testing, the MSE values were 0.047 and 0.022, respectively, and the MSE squared value was 0.0022 and 0.00049.
REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL

Health is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there were 8 extraordinary events due to infectious diseases which made this province become relatively high infectious diseases. One of the efforts that can be made to identify infectious diseases in an area is to form a grouping of locations into a group that has similarities and same characteristics. This is intended to provide information related to health in each region. Cluster analysis is one of method that can be used to grouping the data. Cluster analysis is the process of dividing data into a group based on the degree of similarity. Data with similar characteristics will be gathered in one group. One of the algorithms in cluster analysis is Fuzzy Gustafson Kessel which can produce relatively better groupings compared to the basic algorithms in cluster analysis. This study will use data on infectious diseases in Central Sulawesi Province with several recorded infectious diseases. From 13 regions, 5 clusters were formed. Clusters 1, 2 and 3 each consist of 3 regions, while clusters 4 and 5 each consist of 2 regions.
PREDICTION OF THE POOR RATE K-MEANS AND GENERALIZED REGRESSION NEURAL NETWORK ALGORITHMS (CASE STUDY: NORTH SUMATRA PROVINCE)

Poverty reduction is a crucial issue and the primary The North Sumatra Provincial government's main concern is lowering the poverty rate, which is a crucial issue. The Province of North Sumatra in Indonesia, one of many nations affected by the Covid-19 pandemic, is particularly troubled economically. In this study, poverty levels were mapped using the K-Means algorithm, and GRNN was then utilized for modeling and prediction. The data source used is time series data from 2010 to 2020 from the Central Statistics Agency (BPS), which includes variables X covering population, health, education, unemployment, and asset ownership and variable Y representing poverty level. The goal of this study is to choose the best model for estimating poverty levels in North Sumatra Province. The districts and cities of Deli Serdang and Medan have the greatest rates of poverty, according to the K-means algorithm's mapping of poverty levels. Additionally, the results of the predicting produced MSE values of 0.004659 and RMSE values of 0.00002108. The value of the smoothness parameter is 0.01.
REGRESSION NONPARAMETRIC SPLINE ESTIMATION ON BLOOD GLUCOSE OF INPATIENTS DIABETES MELLITUS AT SAMARINDA HOSPITAL

This study used a biresponse nonparametric regression method with truncated spline estimation that used two response variables. Nonparametric regression method is used when the regression curve is not known for its shape and pattern.One of the nonparametric regression model approaches that is often used is the spline. The truncated spline approach has a segmented polynomial function that provides flexibility. The data used in the study were blood glucose levels in patients with diabetes mellitus, cholesterol levels, and triglyceride levels in 2020. From the results of the study, the best nonparametric biresponse spline truncated regression model with three knot points has been obtained where the minimum GCV value is and has the an value of .
CLOUD COMPUTING

This journal review provides an overview of the current state of cloud computing, including its definition, benefits, and challenges. It examines the various cloud computing models and architectures, and discusses the security and privacy issues associated with the cloud. It also looks at the potential of cloud computing to transform the IT industry and provide new opportunities for businesses. Finally, the review looks at the future of cloud computing, including potential use cases and applications Cloud computing is a paradigm that has revolutionized the way we store, process and access data and applications. At its core, it involves delivering computing resources such as servers, storage, and applications over the internet, allowing users to access them on-demand from anywhere and at any time. This technology has numerous benefits, including cost-effectiveness, scalability, flexibility, and ease of maintenance. It has become a crucial enabler for many businesses and organizations, offering them the ability to streamline their operations and remain competitive in a constantly changing market. In this abstract, we explore the fundamentals of cloud computing, its key features, and its potential impact on the future of technology.. KEYWORDS Cloud computing, technology, advantages, disadvantages, security, challenges, opportunities.
APPLICATION OF RANDOM FOREST ALGORITHM ON WATCH PRICE PREDICTION SYSTEM USING FRAMEWORK FLASK

In the modern era like today, watches not only function as timepieces, but have become a fashion trend for the community, especially teenagers. The increasing market demand for watches opens up opportunities for counterfeit watch sellers to sell their products by claiming that the watches they sell are genuine watches by offering relatively cheaper prices compared to genuine watches. This is very detrimental to consumers and also the watch industry. To minimize fraud committed by fake watch sellers, it is necessary to know the price of the original watch in advance, before buying the desired watch. Therefore, the purpose of this study is to predict the price of watches using the Random Forest method and will be developed into a web system using the Framework Flask. The results of the study using 3337 trees obtained an accuracy rate of 84,98% with a MAPE of 15,02%. The most influential variable on the price of watches is the material variable with the level of importance obtained at 0,359. After getting the best model, the model is then developed into a web system using the help of the Framework Flask and Heroku which can later be accessed online.
EXPERT SYSTEM DESIGN TO DIAGNOSE PESTS AND DISEASES ON LOCAL RED ONION PALU USING BAYESIAN METHOD

Bayesian is a method that can be used to overcome the uncertainty of a situation or data. The information obtained must be continuously updated so that it can foster trust as a result of the uncertainty of those conditions. In this study, the application of the Bayesian method to detect early symptoms of diseases on local red onion Palu plants based on the symptoms that appear will be carried out. Information about pests and diseases that attack local red onion Palu is needed to help farmers. As a result, they can deal with attacked diseases quickly and precisely. This is crucial conducted by considering that this plant is one of the mainstay commodities for farmers in Central Sulawesi Province whose production must continue to be increased. Pests and diseases can be diagnosed through visible symptoms.The sample is local red onion Palu that affected by pests and disesases which planted in the AIAT of Central Sulawesi by experiment. As a result, through these symptoms an expert system can then be created to do a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The created expert system to diagnose diseases uses the Bayesian method to calculate the probability of an event occurring based on the obtained results from observations and experts. An expert system for diagnosis of pests and diseases is built on a web-based basis. This expert system has features and functions including the diagnosis of pests and diseases of the observed plants, viewing the results of the diagnosis and printing the results of the diagnosis. In addition, users can view information on pests and other diseases that attack plants. From the results of system testing that conducted by experts, this shows that the expert system is feasible to use to diagnose local red onion Palu plants which affected by pests and diseases with an accuracy point that has the largest percentage value.
HOW TO CREATE AN ARTIFICIAL THOUGHT AND INTELLIGENCE? AND THE MATH OF LETTERS

The construction of a thought and an artificial intelligence is possible with the language of numbered letters. Language that arose through the creation of the book "New mathematics of letters, triumph with mathematics" updated with the title "New mathematics of letters 2nd edition". Books that expose the language of letters and a mathematics of letters where addition, subtraction, multiplication and division of letters are found, with examples and their corresponding mathematical tables. With the mathematics of letters, any type of mathematical operations can be done, since it is one more mathematics. With the language of numbered letters in order, which represent letters, words and sentences numbered in order, a robot with artificial intelligence through the numbers of words numbered in order could acquire endless all kinds of information obtained by any artificial sense. Numeric information that would have to be transformed into binary numbers.
IMPLEMENTATION OF THE STEP FUNCTION INTERVENTION AND EXTREME LEARNING MACHINE FOR FORECASTING THE PASSENGER’S AIRPORT IN SORONG

This study aims to forecast the number of passengers departing at the domestic departure terminal at Domine Eduard Osok Sorong Airport in 2022 using the Autoregressive Integrated Moving Average (ARIMA) method, ARIMA with Step Function Intervention, and Extreme Learning Machine (ELM). The knowledge of the number of passengers can help the airport prepare facilities. The residual ARIMA model (0,1,0) has no serial correlation (random walk) based on the Ljung-Box test. The MAPE value of the ARIMA model (0,1,0) is 65.47% which means poorly fitted. Because of it, the researchers propose an intervention in the ARIMA model. The RMSE and MAPE ARIMA Intervention ​​(1,0,0) (0,1,0) [12] were 9,027.671 and 35.86%, respectively. Besides, this study also employed the ELM method, which has a MAPE error measurement value of 30.64%. The ELM method has the lowest error measurement results among the three methods. Therefore, the ELM method is suitable for forecasting the number of passengers with predicted values ​​from June to September 2022 as follows: 47985, 37821, 31247, and 33578. On the other hand, intervention in ARIMA can reduce MAPE by 45%.
Systematic Observation of Emotional Regulation in the School Classroom: A Contribution to the Mental Health of New Generations

Emotional regulation is a developmental milestone, as it promotes well-being throughout life. Children between 10 and 12 years old are expected to reach capacities that allow them to regulate themselves emotionally, the school context being a natural setting for this challenge. With the objective of analyzing the forms of expression and regulation of emotions that are observed in the school classroom, this research was conducted through a mixed methods study that systematically observed nine classes during five sessions each. The design was Nomothetic, Follow-up and Multidimensional; the observations were recorded on audio and in person and were later transformed into data by coding them according to an ad hoc designed instrument. The concordance of the records was evaluated, a sequential analysis of delays (GSEQ5) was performed to detect regularities and existing sequences, and a polar coordinate analysis (HOISAN) observed the relationships between the categories. Finally, the presence of multiple cases was detected. The results detail the ways in which different actors express emotions and interact, regulating the emotions of other people. The results are discussed based on the need to foster educational intentionality and allow students’ emotional self-regulation.