Reviewerly/exHarmony huggingface dataset:
Introducing programming to ocean mapping students

The next generation of ocean mappers need to master programming skills to meet increasingly higher expectations for timely ping-to-public data workflows. As such, the e-learning Python for Ocean Mapping (ePOM) project was established at the Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center (University of New Hampshire). The project aims to support new ocean mapping students and professionals in reaching a minimum level of programming skills. These skills are then expanded with further powerful capabilities by leveraging the open-source Python scientific stack and the NOAA (National Oceanic and Atmospheric Administration) Office of Coast Survey’s Pydro distribution. To the best of our knowledge, the ePOM project represents the first attempt at creating a set of comprehensive open-source courses providing students with the required initial coding skills for a career in the ocean mapping field.
Penerapan Association Rule Menggunakan Frequent Pattern Growth Untuk Rekomendasi Produk Jersey Sepakbola

The phenomenon of the beginning of the year, what some football fans have been waiting for, is the publication of the latest jersey from their favorite team. When the new jersey was launched, football fans flocked to buy the jersey, but there were several shops available for the new jersey. This was experienced by the Eighteen Sport shop, in fulfilling the wishes of fans, there were obstacles to re-stock the jerseys that were most in demand. So many items that have not been sold. The focus of this research lies in managing jersey sales data in June, July and August, as well as high interest in the demand for club jerseys. The high demand for jerseys is influenced by the achievements of the club itself. This study uses the FP Growth algorithm with the aim of getting a recommendation pattern from the wishes of football fans. Based on the results of the support management, it was found that consumers by buying 1 jersey item will buy back 1 different jersey item as many as 15 patterns. Consumers by buying 2 jersey items will repurchase 1 different jersey item as many as 46 patterns. Consumers by buying 3 jersey items will repurchase 1 different jersey item as many as 37 patterns. Consumers by buying 4 jersey items will repurchase 1 different jersey item for 10 patterns. So that the pol data becomes the owner's recommendation to make a repeat purchase.
Sistem Pendukung Keputusan Pemilihan Biji Kopi Arabika Terbaik Menggunakan Metode SMART

Arabica coffee beans are one of the main varieties of coffee beans developed in Indonesia. In making decisions to determine quality Arabica coffee beans, an appropriate system is needed to analyze problems in solving and efficient and accurate data presentation. Therefore, a computer-based system or method is needed to facilitate the selection of the best Arabica coffee beans. This study uses the Simple Multi Attribute Rating Technique (SMART) method. The SMART method is a decision-making method to solve the problem of choosing a multi-objective choice among several criteria, so that later it will be able to produce an effective and efficient analysis. The input criteria that are the priority in selecting the best Arabica coffee beans are aroma with a weight of 25, color with a weight of 25, taste with a weight of 25, dirt content with a weight of 15, and price with a weight of 10. Of the 25 alternatives tested in this system, Gayo Avatara Natural Arabica coffee beans were the best first alternative, followed by Aceh Gayo Wet Hull, Java Ijen Natural, Java Ijen Honey, and Kintamani Natural. This decision support system for selecting the best Arabica coffee beans provides speed, accuracy, and data accuracy in selecting the best Arabica coffee beans which will be used by coffee lovers to provide coffee with a delicious taste. So the results of the decision from 25 types of Arabica coffee, there are 11 types of Arabica coffee with a rating of "Very Good", 10 types of Arabica coffee with a rating of "Good", and 4 types of Arabica coffee with a rating of "Quite Good".
Penerapan Algoritma Naïve Bayes Untuk Diagnosa Hama Pada Tanaman Aglaonema SP dan Monstera Adansonii

Aglaonema sp and Monstera Adansonii are the most popular ornamental plants during the COVID-19 pandemic. Aglaonema sp and Monstera Adansonii become the target of the keepers because the colors and patterns on the leaves are unique. Despite the high popularity of Aglaonema sp and Monstera adansonii, not many keepers know the problems faced by their plants. The least number of triggers is knowledge of the keepers in caring for their plants. Therefore, to help the community in maintaining Aglaonema sp and Monstera Adansonii plants, an expert or expert in the field of ornamental plants is takes. Due to the difficulty of finding an expert, a system that is able to keep knowledge from an expert is required. An expert system is part of an intelligence that uses a person's knowledge that is summarized and processed carefully to solve problems. One of the statistical data calculation methods is Naïve Bayes. The Naïve Bayes method is a computational technique for predicting probabilities by calculating how many possible sets by adding up the frequency and combined values ​​of the dataset. The software created is able to share understanding with the community about Aglaonema sp and monstera adansonii plants and what treatment should be given to their ornamental plants
Kebakaran Hutan Implementasi Metode CLARA Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Hotspot)

Forest or land fires are events that often occur in various countries in the world that require serious handling from all parties because they have an impact on all lines of life. Therefore, early treatment is needed, one of which is by grouping fire-prone areas using hotspot data. Hotspots can be obtained by satellite in this study taking hotspots from NASA satellites. The data used are latitude, longitude, brightness and confidence. The method used is the Clara Clustering method because this method has the advantage of being resistant to outliers and can be used in large amounts of data. This study concludes that the Clara method can process 16,579 hotspot data with the best Shilhoutte Coefficient value of 0.89 with 2 clusters while the potential possessed by cluster 1 is included in high potential with an average brightness of 3670K and a confidence value of 1 or hight. Meanwhile, cluster 2 has moderate potential with an average brightness of 3490K and a confidence value of 3 or nominal.
Klasifikasi Pemberian Bantuan UMKM Cirebon dengan Menggunakan Algoritma K-Nearest Neighbor

The Indonesian government in obtaining Real Time data on MSMEs who are entitled to assistance, accuracy in distributing MSME assistance, and accelerating Indonesia's economic growth through MSMEs, especially the Cirebon Regency area. There are several ways so that cash transfer assistance for micro-scale SMEs from the government is right on target, in this study the authors will use data mining techniques with the k-nearest neighbors method in classifying receiving assistance from SMEs. The data used in this study uses secondary data with attributes of Regency, District, Business Name, Product Name, Business License, Assets and Turnover. The application of the KNN algorithm uses the retrieval operator, cross validation, and in developing the model using the KNN algorithm operator, apply model and performance. The results of the accuracy are 98.46 % with details, namely the Prediction Results are Eligible and it turns out to be true as many as 339 Data. The Prediction Result is Eligible and it turns out to be true Not Eligible as much as 2 Data. Prediction results are not eligible and it turns out to be true as much as 4 data. Prediction results are not eligible and it turns out to be true, 42 data are not eligible. Recommendations for the pattern of knowledge obtained using the K-NN algorithm. Researchers provide recommendations that are feasible to be given assistance for MSMEs as many as 339 MSME participant data spread across the Cirebon district and included in the affected category. Then there are several MSME participants who cannot receive MSME assistance according to the application of the KNN algorithm, which is 42 data, and there are 2 data from participants who are proposed to receive MSME assistance. The hope of the research for participants who receive assistance from the government can survive in conditions like this covid 19
Comparison of Ensemble Methods for Detecting Hoax News

The spread of hoaxes in Indonesia has become a big concern for the public, especially now that the COVID-19 virus pandemic is hitting the whole world. Due to the large number of people who believe the hoax news regarding the COVID-19 vaccination that has spread on social media, many people refuse to carry out the COVID-19 vaccination as a form of government effort in dealing with this pandemic. Therefore, people need to be wiser when reading news on social networks. To help the public not to read hoaxes, it is necessary to classify the COVID-19 vaccine hoax. This study builds a system to classify hoax news on the COVID-19 vaccine. The model was built using the ensemble method by comparing the Random Forest and AdaBoost algorithms to choose a good classification for detecting hoaxes. In this research, there are use two test scenarios. The first scenario is an experiment using the Random Forest algorithm method and the second scenario is an experiment using the Adoboost algorithm method. The experimental results show that the first scenario produces a good accuracy value with the random forest algorithm method of 93.58%.
Sistem Pakar Diagnosa Penyakit Diabetes dengan Menggunakan Metode Bayes Berbasis Web

Diabetes Mellitus is one of the non-communicable diseases with the highest proportion in Indonesia and is the sixth highest cause of death in this country. Especially in Asahan District, the number of people with diabetes who died is increasing over time, so there needs to be effective treatment so that diabetes is no longer feared by the public because it is easy to treat. Many patients who experience diabetes are getting worse because they cannot detect the early symptoms of diabetes, which is still considered trivial. There is no information about diabetes and its symptoms, making it difficult to diagnose the disease. In the diagnosis of DM disease is limited to conventional diagnoses with doctors. it is necessary to build a system on a computer application to help diagnose DM. To determine the level of DM disease, an expert diagnostic system was made with the method used in this case is the Bayes method. This method is an approximation to an uncertainty that is measured by probability. Bayes' approach at the time of classification is to find the highest probability by inputting the required attributes and the probability of the disease and related symptoms. The results of the implementation of the system are the selection of symptoms according to the case of experiencing type 1 diabetes because it has a weight = 2 higher than the weight results of other diseases, the system provides the results of the process the system will provide information on what type of DM he is experiencing in order to get a solution with treatment
Peramalan Penjualan Produk Sepatu dengan Menggunakan Metode Double Moving Average (DMA)

Halim Jaya Kisaran shop is one of the trading businesses that sells people's basic needs such as shoes and bags. This store requires forecasting in an effort to process data and generate accurate reports. This is done because the Halim Jaya Kisaran Store to determine the demand for shoes is unpredictable, so it often experiences a lack of shoes to be purchased in order to serve the demand for shoes needed by customers. In designing this system, the author uses the Double Moving Average (DMA) forecasting method for the decision support process in determining the number of shoe stocks to be sold for the next month, using the calculation method. This forecasting application was created using the Visual Basic Net 2010 programming language with Microsoft Access as the database. From making the system an application can be produced that can control optimal and economical demand and with a high level of accuracy and can predict the number of shoe requests at the Halim Jaya Kisaran Store so that the forecast results can help the store to avoid running out of stock of shoe requests at the Halim Jaya Kisaran Store
Seleksi Fitur Menggunakan Eigen Vector Untuk Peningkatan Kinerja K-Means Clustering Dalam Pengelompokan Data

The large number of data set attributes from the data grouping process with K-Means Clustering can affect the number of iterations produced. In this research, Eigen Vector is used to perform feature selection on the data set. The selected data set is then clustered using K-Means Clustering. The data set used in this research is the Wine Quality Dataset obtained from the UCI Machine Learning Repository, with 11 attributes, 4898 data records and 7 attribute classes. Then the South German Credit Dataset was obtained from kaggle.com with 20 attributes, 1000 data records and 2 attribute classes. The results of this research indicate that the number of iterations obtained from the comparison of tests using K-Means without feature selection is that in the Wine Quality Dataset, 11 iterations are obtained, and in the South German Credit Dataset, there are 10 iterations. Meanwhile, K-Means with Eigen Vector feature selection obtained the number of iterations in the Wine Quality Dataset with a total of 5 iterations, and in the South German Credit Dataset with a total of 4 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering without feature selection from the Wine Quality Dataset is 678.5735, while in the South German Credit Dataset it is 1534.3167. While the K-Means Clustering with Eigen Vector from the Wine Quality Dataset is 383.0517, and the South German Credit Dataset is 469.0698. From the results of the proposed method is able to reduce the percentage of errors and minimize the number of iterations on K-Means Clustering with feature selection using Eigen Vector
BRAIN-COMPUTER INTERFACING PROSPECTS IN E-SOCIETY

One of the achievements of modern society is the brain-computer interface (BCI) as a way to organize an intelligent environment. Such systems have already been successfully implemented and contribute to integrating people with disabilities into society, overcoming disorders, diagnosis, and monitoring well-being. Studying mental activity in business interaction, training, consumer activity, and entertainment is becoming increasingly popular. The study of the peculiarities of the functioning of human consciousness contributes to the successful integration of brain-computer interfaces into modern technologies of information systems such as invisible computing. The article highlights the latest trends in the development and implementation of neuro-computer interfaces with the principles of the organization of e-society and the problems and prospects that arise in the conditions of its evolution. The analysis was carried out based on the research of real successfully implemented projects, and the experience of participation in conferences was applied. Possible actual implementations are described, and examples have already been implemented. Particular attention is paid to the issue of neuro-computer inequality and its solution, which is one of the main detachable issues that have been updated in recent years. The implementation of neuro-computer interfaces in combination with popular trends and technologies such as post-quantum programming, consciousness uploading, cloud and fog computing, and enter civilization communication are investigated. The nearest electronic society stage (2035-2050) based on Z and α generations will acquire critical skills in mental management of intelligent environments, increasing the efficiency of information systems of any complexity and purpose. The first steps to this point of development that have already been done are the legalization of BCI technology and mass user implementation, which continue to be investigated along with the rapid growth of generation and the performance of new ideas in accordance with the latest opportunities and conditions.
INFORMATION SUPPORT FOR MANAGING THE COMPUTER INFRASTRUCTURE OF COMPLEX DISTRIBUTED SYSTEMS

Management of complex distributed systems, at present, is carried out using information technologies designed to collect and manage a large amount of heterogeneous information of an organization or institution. The computer infrastructure of such systems is also a complex distributed system, and information about the location and mutual arrangement of its individual components, as well as the location of the associated infrastructure and main users, is essential for its high-quality functioning. This paper proposes a method for mining spatial data to formalize the process of creating specialized databases for the implementation (creation) of individual subsystems as part of complex distributed information systems. The results of the application of this method for the construction of one of the subsystems, namely, the subsystem of information support for the management of computer infrastructure, as part of a complex distributed information system, are presented on the example of the geoinformation system of the Ufa State Aviation Technical University. The proposed approach to intelligent analysis and representation of spatial data can be used to build complex information systems in significantly distributed organizational and infrastructural systems.
TRAINING ON MAKING HAND SOAP AS AN ALTERNATIVE INCOME DURING THE COVID-19 PANDEMIC IN KARANG TARUNA YOUTH IN MAYANGAN VILLAGE

The Covid-19 pandemic isn't over yet. Soap is an alternative to maintain health. The purpose of this PKM is to provide training on making hand soap as an alternative income during the Covid-19 pandemic for youth youth in Mayangan Village. The method of implementing PKM is divided into: (1) The preparation stage which is divided into socialization and introducing the tools and materials used; (2) The implementation stage which is divided into a training stage with practical methods and interactive dialogue. The results of the training produced 100 bottles of hand soap, it is assumed that the net profit for each bottle is Rp. 4,860. Hand soap in the form of liquid soap on the market can be sold at a price of IDR 6,000 to IDR 7,000, so it is assumed that the profit that can be obtained per bottle is IDR 2,000 and intensive assistance so that participants can make hand soap independentlyand (3) the evaluation stage, in which the PKM evaluations are: (1) The support and participation of the participants greatly supports the success of the activity; (2) The presentation of materials, demonstrations at the training made many participants understand in detail all the materials and practices being taught, so that PKM is said to be running smoothly according to the plans and targets
TRAINING ON MAKING CLEAN WATER PROCESSING UNITS FOR PERMANENT RESIDENTIAL COMMUNITIES I NANGBELAWAN VILLAGE I SIMPANG EMPAT DISTRICT KARO REGENCY

The eruption of Mount Sinabung in Karo Regency from 2010 to 2018, forced residents around the slopes of the mountain to evacuate to a safe place. There are 5 sub-districts affected, namely Simpat Empat, Naman Teran, Payung, Munthe and Tiganderket sub-districts. This mountain has never erupted since 1600. As a result of this eruption the government has designated 3 Temporary Residential Locations (Huntara) and 33 locations designated as Permanent Residential (Huntap). Huntap I Nangbelawan Village I is one of 33 independent relocation sites known as Permanent Residential (Huntap) which has been provided by the Karo Regency government until 2019. The Karo Regency Government has carried out a lot of development in Huntap refugee camps such as entrance facilities, electricity , drainage, communal, worship facilities, health facilities and clean water. The clean water facility built by the government is in the form of drilled wells. Based on oblivion observations, the levels of lead (Pb) in community bore well water caused the existence of air shelters for several residents visited by the team. Based on these problems, training activities for making clean water treatment units were carried out in permanent residential communities I Nangbelawan I Village, Simpang Empat District, Karo Regency. Solving the problem of water quality from drilled wells for the shelter I community, Nang Belawan Village, Simpang Empat District, Karo Regency by the community service team from Poltekkes, Ministry of Health, Medan, Department of Environmental Health, was carried out by suggesting that a building/water treatment unit be constructed using a combination method of sand filter and up-flow activated carbon. " The advantages of this unit are, among others, it can reduce the turbidity and solid form of solid materials, as well as in general it can reduce the metal content of Lead (Pb), Iron (Fe) and Manganese (Mg). With the flow from the bottom to the top (up-flow) so that the constraints and difficulties in processing can be done easily by the displaced community.
Women Political Leaders’ Participation in Local Decision-Making Structures in Tanzania

Purpose: This paper envisioned examining the participation of women political leaders in the local decision-making organs in Tanzania. Specifically, the study aimed to examine the number of women represented in the study local government structures (LGSs), the motives behind women participating in the LGSs and the duration of service in the study LGSs.&#x0D; Design/Methodology/Approach: The study used a cross-sectional descriptive research design. A cluster sampling technique was used to obtain the study villages and Mitaa. Both quantitative and qualitative research approaches with different data collection methods were used. To collect quantitative data, questionnaires were administered to 390 household heads both men and women in the study villages/Mitaa, while qualitative data were gathered using an interview guide, Focus Group Discussion (FGD) and non-participant observation checklist. Quantitative data collected were analyzed and presented in tables and figures while the qualitative ones were thematically analyzed.&#x0D; Findings: The study revealed that the number of women's participation in various local committees in the study of local government structures has increased by 50% (Dodoma City) and 30% (Bahi District). However, motives for women leaders’ participation are constrained by limited continuity of tenure in the political office. Therefore, suitable interventions are recommended to address the existing capacity gaps.&#x0D; Research Limitation: The study targeted to involve an equal number of men and women household heads in each study village and Mitaa, however in some villages it happened to have less number of women than men household heads.&#x0D; Practical implication: This study recommends that more spaces should be given to women to participate in local decision-making structures and hold seats to enhance gender equality in politics.&#x0D; Originality/Value: This study focuses on the gender parity framework in local politics. It brings an understanding to the various government departments, ministries responsible for women and other stakeholders regarding the opportunities available for women to participate in local politics.