Reviewerly/exHarmony huggingface dataset:
Theoretical Analysis of Standard Selection Sort Algorithm

Sorting algorithms plays an important role in the computer science field. Many applications use sorting algorithm. There are several sorting algorithms proposed by experts, namely bubble sort, exchange short, insertion short, heap sort, quick short, merge sort, standard selection sort. One well-known algorithm of sorting is selection sort. In this journal, discussion about standard selection sort is given with thorough analysis. Sorting is very important data structure concepts that has an important role in memory management, file management, in computer science in general, and in many real-life applications. Different sorting algorithms have differences in terms of time complexity, memory use, efficiency, and other factors. There are many sorting algorithms exist right now in the computer science field. Each algorithm has its benefits and limitations where a trade-off exists between execution time and the nature of the complexity of the algorithm itself. The method is theoretical analysis. Three theoretical analyses are given with deep explanation and analysis. Each with six index arrays, namely with six data on it. The numbers are sorted in ascending order. Pseudo code is also given, to understand this algorithm more thoroughly. It is concluded that this theoretical analysis explained the algorithm more clearly, by using process iteration by hand.
Scrum Framework Implementation of Fish Mobile Auction Module in Pasar Iwak Marketplace

Lelang Ikan mobile application is an online auction in the marketplace platform of Pasar Iwak based on Android platform. Scrum framework is applied and consists of determining the product backlog, creating sprint planning and sprint backlogs, and conducting sprint reviews and sprint retrospectives. The product backlog resulted 14 backlog items based on the results of system and user requirements for user auctioneers. Sprint planning and sprint backlog are divided into four sprints, namely front-end and back-end development, system integration process and system implementation. Sprint reviews are carried out by implementing two types of testing, namely blackbox testing and user acceptance testing (UAT). Blackbox testing emphasizes testing application functions or features, while UAT is applied to measure the level of user acceptance. The results of blackbox testing showed that the features provided by the application are in accordance with the predetermined requirements. Whereas UAT showed the result of 66.8%, which means that the application is in the appropriate category and can be accepted by users. The application development process ends at the sprint retrospective stage which is a suggestion or feedback after the application testing. The suggestions obtained are in the form of adding tracking features, payment features with payment gateways, and application development with the iOS platform.
Drowsy Detection in the Eye Area using the Convolutional Neural Network

Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.
Analyzing Image Malware with OSINTs after Steganography using Symmetric Key Algorithm

Steganography is the practice of hiding a message or information within another file, such as an image (Singh &amp; Singla, 2022). OSINT (Open Source Intelligence) involves using publicly available information for intelligence gathering purposes. In this research, the asymmetric key algorithm will be applied to the steganography method, using 10 images with different sizes and dimensions. Images tested for steganography are in tiff, gif, png, jpg, and bmp format. A combination of steganography and OSINT could involve analyzing and decoding images found on publicly available platforms, such as social media, to uncover hidden messages. On the other hand, steganography within OSINT can also be used to protect sensitive information from prying eyes. Overall, the combination of Symmetric Key Algorithm steganography and OSINT can be a powerful tool for both intelligence gathering and secure communication. Here in this work, malware is developed, and using that malware the victim’s machine is exploited. Later, an analysis is done via freely available OSINTs to find out which is the best OSINT that gives the best results. OSINTs have been very helpful in identifying whether the URLs and files are malicious or not. But how binding an image with the malware makes it difficult for OSINTs to identify they are malicious or not is being analyzed in this work. The analysis shows that the best OSINT is VirusTotal which has a greater number of engines that could detect the malware whereas others don’t have a variety of engines to detect the malware. Also, when it comes to malware afore binding it with an image is easier to detect whereas for an OSINT it was difficult to identify and detect the malware after binding with an image
Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu

Transportation is an activity of moving things such as humans, animals, plants and goods from one place to another. To be able to implement transportation, we need a means of transportation that suits our needs. For in Indonesia, people are more inclined to land transportation. That's because land transportation already has a lot of vehicles. Land transportation already has many vehicles that can be used, both for private and for the public. Each vehicle has its uses and risks as well. Therefore we will do a data cluster from the trains. We chose the train, because the risk from using the train is very small, meaning that there is a lot of public interest in trains. So we want to do a cluster on rail passengers. The cluster that we do is to group passenger data based on the similarity of passenger data. We will do the cluster using the K-Means method. The K-Means method is very suitable when used to perform a cluster. K-Means will process widgets that are made according to the needs of the research. So after we enter the method in the widget pattern, the widget will process it to output the results from the cluster that we created. The cluster process using the K-Means method will be applied using the orange application. After we apply it, the data will later be clustered, we will cluster data as many as 3 clusters. Then the incoming data will appear in clusters 1, 2 and 3, both from business and executive classes
Blockchain Technology For Circular Economy In Plastic Bank

With the use of blockchain technology, this research sought to understand the applications, benefits, and limitations faced by circular economy-based businesses. This research was conducted at the Plastic Bank Company, which used a digital conference room to allow interviews that could not be conducted in person, as well as the researcher's residence for online data gathering and document review. Five management members of the Plastic Bank Company comprise the sample population. The information used is first-hand information derived from interview findings. In order to acquire data, several methods including interviews, document analysis, and observation were applied and tested by Triangulation. The findings of this study revealed: 1) Companies with a circular economy may employ blockchain technology to change supply chain operations, tracking, and tracing. 2) Blockchain technology has benefits for businesses based on the circular economy, including easier distribution management, less duplicate papers, increased cost effectiveness, and the ability to turn plastic trash into digital cash. 3) The general public is still unaware of the use of blockchain technology for businesses that rely on the circular economy. Furthermore, the company's success is constrained on a small scale due to the absence of finance from affiliated parties. Therefore, in order to grow the use of technology on a big scale, this circular-based economy firm for plastic banks has to strengthen its performance and efforts.
Implementation of the K-Nearest Neighbor (kNN) Method to Determine Outstanding Student Classes

Education being one factor supporting students / I to be able to increase their knowledge. Each student has their own potential that they have obtained in the world of education. Therefore, every school has created an education program that functions to increase the potential of high achieving students. The program is a flagship class program. What is meant by a superior class program is a process of selecting and classifying students to be placed in the classroom superior (grade student / I achievement). Therefore, this study aims to implement classification on student data using the K-Nearest Neighbor (kNN) algorithm. K-Nearest Neighbor (kNN) is a method used to classify data based on training data (data set). The data that the writer will use is student data of 60 student data. In this classification using the kNN method aims to classify data on students who are eligible to enter the superior class (class of outstanding students). The first step is the process of determining data requirements. Then cleaning or pre-processing and the next is to design a widget model of the kNN method on the orange application to carry out the data classification process. The test results using 60 student data using the KNN method and using the Confusion Matrix obtained an Accuracy value of 91.6%, then a Precision value of 89.2% and a Recall value of 92.5%. The conclusion is that this study succeeded in obtaining a method that the best and also get the best results for Classification of superior student classes.
Travel Management Information System Employee Service at the Office of Industry and Trade of Provsu

Technological developments and advances in science are currently developing rapidly, especially in the management of data that has used computers. The use of computers today is also used by individuals and groups that can facilitate their work. With the development of this information technology, there is an increasing need for an information system that can complete work in a systematic and efficient manner so as to facilitate the work of individuals or groups in the form of data management.The official trip itself is a work program at the agency in the form of a field activity that functions to find out and share experiences in accordance with the field of the program being carried out. With this official trip, every time a business trip is carried out, a trip report is needed to find out what activities have been carried out so as to produce a new work program for each existing agency. However, the BPK (Corruption Supervisory Agency) is often scrutinizing official travel itself, which is because every official trip is carried out, a budget will be given according to the stipulated budget, and the budget is quite large. Therefore every employee must also report official travel activities in a systematic and realistic manner.
Comparison of the K-Means Algorithm and C4.5 Against Sales Data

In general, the process of collecting and grouping data requires a long process. And if it has to be grouped manually it takes a very long time. Therefore, data mining is a solution for clustering data - a lot of data to classify it. In this research conducted at CV.Togu - Togu On Medan Branch, data mining is applied using the K-Means process model and the C4.5 algorithm which provides a standard process for using data mining in various fields used in classification because the results of this method easy to understand and easy to interpret. . The K-means method is a non-herarical method which is an algorithmic technique for grouping items into k clusters by minimizing the distance of the SS (sum of square) to the cluster centroid. In the K-means method, the number of clusters can be determined by the researcher himself. And the testing methods used to measure cluster quality are the Silhouette Coefficient and the Elbow Method. Based on the research conducted, there are significant differences before and after using the two methods. The results of the K-Means algorithm will be compared with the results of the C4.5 algorithm in the form of rules (decision trees). This research produces data on goods that have the highest level of sales/behavior
Analysis of the Decision Tree Method for Determining Interest in Prospective Student College

Education is learning science, skills that are carried out by a person or a group of people. The education level starts from Elementary School Education, Junior High School and High School. Apart from that, the highest level of education is college. Lectures are further education carried out by people to gain knowledge and degrees. In college education everyone can choose their respective majors, according to their wishes and desires. With college education, there will be many prospective students who will go to college. But the interest of prospective students to study varies, there are some prospective students who want to study in public and there are some who want to study privately. Therefore the author will make research about prospective students' interest in college. This study aims to see the college interest of prospective students. For this research a data classification of prospective students will be carried out using the Decision Tree method. For this research stage using the Decision Tree method, the first is data analysis, then data preprocessing, then the Decision Tree method design and finally data mining testing. The classification was carried out using the Decision Tree method using 65 prospective student data. From the results of the classification using the Decision Tree method, the results of the Classification of prospective students who are interested in studying are 46 prospective students. The classification results above show that many prospective students are interested in studying.
Implementation of the Naïve Bayes Method to determine the Level of Consumer Satisfaction

Satisfaction is a feeling of pleasure at something you like, you get it from goods and services. Satisfaction becomes an important assessment when someone sells goods or services. This is because satisfaction will be an assessment of the goods purchased by consumers or services that will be received by consumers. Therefore the authors make research about the level of consumer satisfaction in shopping. This research was made using the Naïve Bayes method and used consumer data as sample data which used 49 consumer data. By using the Naïve Bayes method, this study aims to see the level of consumer shopping satisfaction, it is made to see the results of a consumer's satisfaction, sometimes there are some consumers who are dissatisfied with the reason the product is not good and some are satisfied with the reason the product is still new and good. Therefore this research was made. This research was conducted using the naïve Bayes method with the first stage being data analysis, then data preprocessing, then naïve Bayes algorithm and finally system testing. After system testing is carried out, classification results will be obtained using the naïve Bayes method. Classification results stated that as many as 47 consumers were satisfied shopping and as many as 2 consumers were not satisfied shopping. The conclusion is that a lot of consumers are satisfied with shopping, meaning that the place is very good and liked by many consumers.
Beyond Darwin: General Theory of Evolution of Everything, From the origin of life to the market economy

The basic concern of the general theory of evolution is to understand the entire evolution from the origin of life to the biological, technological, social, and economic structures of the present from a unified point of view and structure. The general theory of evolution can be seen as a comprehensive generalization and extension of Darwin's theory of evolution. It goes far beyond conventional extensions of Darwinian theory, such as synthetic evolutionary theory or multistage selection or evo-devo or epigenetics. Essentially, it views evolution from the perspective of the evolution of information. It expands the Darwinian terms of "biological species," "genotype," "phenotype," "mutation," and "selection" and replaces them with much more general terms. These conceptual extensions make it possible to describe evolutionary developments in quite different areas from a unified point of view and within a unified time frame.The basic idea is to understand the evolution of everything as the emergence of new information types and new information technologies in the following sense:- A new type of information is associated with the emergence of a new storage technology- For each new type of information, 3 information technologies emerge one after the other:Storage technology, Duplication technology, processing technology.With this concept, the chronology of the entire evolution can be divided in a natural way into 7 ages with 3 sub-ages each, which correspond to the 7 information types with their corresponding 3 information technologies. Better and better information technologies are the basis for the fact that more and better targeted variation mechanisms could be formed. This explains the exponential increase in the speed of development and why development is probably heading for a singular point.The following topics represent a selection of new ideas presented in detail in the paper:- Evolutionary theory of information- Link between the evolutionary theory of information and the general theory of evolution.- Megatrends of evolution- Evolution of the driving forces- Targeted variation mechanisms as essential elements of evolution- Constraints as essential elements of evolution- The illusion of free will as an evolutionary trait of success- The documentation of debt relationships (especially in the form of money) as a catalyst for win-win and cooperation mechanisms- The difference between individual utility optimization and total utility maximization- From Artificial Intelligence 1.0 to Artificial Intelligence 2.0
Teachers as Digital Immigrants: Does Hybrid Identity Facilitate Integration in the Digital World?

This study adopted a model from the immigration literature and viewed the transformation of teachers into the digital world as immigration -resembles geographical immigration.The study focused on teachers' hybrid (digital and traditional) identity as digital immigrants during their immigration and examined the role of innovative school climate and technological training as moderators.Data was collected from 145 teachers from junior high and high schools.The findings indicated that the hybrid identity significantly predicted objective and subjective integration of the teachers as digital immigrants, especially when the innovative climate of the school and digital training of the teachers were low.
Book Review: Doing Ethnographic Research Online

In the field of consumer and marketing research, netnographies have become a widely accepted form of research. Our social worlds are going digital, with perhaps hundreds of millions of people interacting through various online communities and their associated cybercultures. Netnography is a form of ethnographic research adapted to the unique contingencies of computer-mediated social interaction. This book aims to provide a set of methodological guidelines, a disciplined approach to the culturally—oriented study of that technologically—mediated social interaction that occurs through the Internet and related information and communications technologies. The methods that these various fields have used to investigate these topics are still somewhat uncertain and in flux. Most of this type of work written after the term was coined uses the guidelines and techniques that have been published about the netnographic approach.
Designing Persuasive Bin to Facilitate Waste Segregation in the Household Sector Through Design Thinking Application

The increase in population causes an increase in the amount of public consumption, increasing the amount of waste. Based on databoks.katadata.co.id, households are known to be waste contributors. Then, based on the Katadata Insight Center (KIC) survey in 2019, it was found that 50.8% of households did not sort waste. The behavior of households is an important problem to be solved because mixed waste cannot be separated and recycled again. Seeing these conditions, it is necessary to design a waste bin to persuade households to perform waste-sorting activities. The waste problem is considered wicked, and design thinking is the appropriate method to approach it. Research with design thinking is carried out through understanding, observing, defining, creating ideas, prototyping, and testing stages. In testing the prototype, the persuasive level of the waste bin design was assessed using the Perceived Persuasiveness Questionnaire (PPQ). This research results in a waste bin consisting of four boxes equipped with an adjustable bulkhead, QR-Code, and removable sorting card that can make a sound when not throwing garbage
ACTUAL PROBLEMS OF THE ETHICS OF PROFESSIONAL RELATIONS

The concept of professional ethics is considered, the problems of ethics of professional relations are indicated. The necessity of applying the principles of professional ethics in organizations is substantiated
Identification of IT Governance Capability Level of COBIT 2019 at The KOMINFO City of Bitung, North Sulawesi

Effective information technology governance (ITG) is vital for managing risks and ensuring proper oversight of IT in government organizations and enterprises. However, many organizations struggle with implementing effective ITG strategies, resulting in a higher likelihood of cybersecurity breaches, operational inefficiencies, and financial losses. This study addresses the urgency of improving ITG by assessing the capability level of ITG within the Ministry of Communication and Information Technology (KOMINFO) in Bitung, North Sulawesi, using the widely recognized COBIT 2019 framework. By conducting interviews with key IT personnel, the study quantifies the capability level of eleven core models and highlights areas that require improvement. The results underscore the critical importance of designing and implementing effective IT governance to mitigate risks and enhance IT oversight in government organizations and enterprises. The study's findings can serve as a foundation for future efforts to improve IT governance in KOMINFO and other organizations, ultimately contributing to a safer and more secure IT environment.