Reviewerly/exHarmony huggingface dataset:
The Challenge of Understanding—From Protozoa to Ecosystems

We live in the information age, but we still do not fully understand how the surrounding nature exchanges, collects, and manages information—both at the ecosystem and cellular levels. Still, it is crucial to possess a dynamic and developing comprehension of the functioning of nature in order to understand the phenomena occurring in our ever-changing world. Sometimes we encounter an attitude that there is no need to investigate anything further, as everything has already been researched. This is a very dangerous attitude. Without information, we should not actually talk about the age of information, and it is worth reviewing some knowledge that is deeply ingrained in us from time to time. Occasionally, it is worth taking a step back and considering whether we ourselves may have become trapped in dogmas that may hinder our understanding. This presentation focuses on various aspects of understanding nature, starting from single-celled organisms and ending with ecosystems. The topics that will be discussed among others are (1) infochemicals (how do they actually affect the biota?); (2) the remarkable process of cellular computation (does this imply that cellular materials can show a primitive intelligence?); and (3) the ability of plants to influence wind patterns and bring in more moisture from the ocean (what will happen to us when we excessively deforest?).
GAMIFICATION CONCEPT OF FIVE SENSES IN LEARNING RECEPTIVE LANGUAGE SKILL IN THE GERMAN LANGUAGE EDUCATION STUDY PROGRAM

Fun learning has an impact on students and lecturers. To create fun learning, it is necessary to have a game concept which contributes in learning. Gamification is not a new thing in the learning process but an important part that needs to be implemented. Receptive language skill is a subject with two language skills namely reading comprehension (Lesen) and listening comprehension (Hören). The learning process that is being carried out is quite boring because of boredom and difficulties in understanding written texts and audio texts. This research was conducted with the aim of knowing the application and perceptions of students about the concept of gamification "Five Senses" its contribution in learning. The sample in this study was 12 students. The method used was descriptive qualitative with descriptive statistical data analysis techniques. The instruments used in this study were questionnaires and observation sheets. The results of the study show that students are very motivated and enthusiastic in learning with this concept. In addition, learning using this concept creates a fun learning atmosphere and students are actively involved in building communication.
Major determinants of Bitcoin price: Application of a vector error correction model

Research in recent years has shown that Bitcoin is a virtual asset that is used as a medium of exchange and investment tool other than shares and bonds, the development of the digital era has opened up opportunities for Bitcoin to be chosen as part of an investor’s portfolio. The focus of this study is to examine the impact of nine key determinants on Bitcoin price. The data used in the study are daily data starting from January 1, 2018 to January 1, 2022. The main data source is taken from Investing.com, and the estimation method applied is the Vector Error Correction Model (VECM). The main finding shows that Bitcoin Volume impacts Bitcoin Price negatively, which is in line with the demand theory. Another finding is related to the substitute effect of Ethereum Volume, Litecoin Volume, and Gold Volume, each of which influences Bitcoin Price positively, suggesting that these three commodities are substitutes to Bitcoin. In contrast, whereas Oil Volume has an insignificant effect on Bitcoin price in the short term, it has a negative significant impact in the long term. In addition, LQ45 stock index Volume influences Bitcoin Price positively in the short term, suggesting that LQ45 stock index and Bitcoin substitute for each other. Moreover, Google Trends impacts Bitcoin price positively in the long term. In terms of the income effect, either the Indonesian GDP or US GDP has a strong positive effect on Bitcoin price in both the short and long term.
THE INFLUENCE OF PARENTS INCOME AND PEER FRIENDS SUPPORT ON STUDENTS STUDY INTENTION FROM COLLEGE STUDENTS AT RAANAN BARU

In this era, there are many factors that influence students' study intention, especially for college students. In this study, the object is college students at Raanan Baru and the factors in this study that influence student study intention are parents income and peer friends support. The purpose of this research is to analyze the influence of parent’s income and peer friends support on students study intention partially and simultaneously. This research use quantitative method with data collection using questionnaire with Likert scale method was processed in SPPS application, multiple linier regression as the tool of analysis. The sample of this study is 60 respondent. Based on the results of research, the parent’s income and peer friends support influence students study intention from from college students at Raanan Baru. Keywords: parents income, peer friends support, students study intention
Sikarju: Expert System of Major Recommendation to Increase the Chances of Being Accepted by University

Major is one of the important factors in the world of lectures. Along with the increasing need for knowledge and skills required in the world of work, increasing the number of majors offered by tertiary institutions. The number of considerations from prospective students regarding the selection of majors causes students to be confused in determining the best major they will choose to continue their education. The research aims to design an expert system-based website that will be used to provide major recommendations. The method to be used is the forward chaining method, where this method works by matching data based on predetermined facts, then obtaining results based on matching the data. Based on the black box testing that has been done, the results show that the designed expert system is by the expected functionality. Therefore this expert system can be said to be feasible to use.
Implementation of RESHOT Method to Create a Good User Experience in an Application

In recent years User Experience (UX) has become something that must be implemented in making applications. Every application design practitioner has applied this discipline, but often they wonder how to simplify a user's journey flow by using applications and make it easier, faster, and simpler. This research aims to explain a method that can simplify the User Experience comprehensively. RESHOT is used (Refine the challenge, Remove, Shrink, Hide, Organize, Time). This method will make applications pay more attention to aspects that can increase user satisfaction. As a result, this study contributes to an explanation of simplifying the flow of uploading donor data files using the RESHOT method in the X blood donor data collection application. The results of streamlining the flow of uploading donor data files have been tested on five respondents and have a 100% user success rate in completing tasks with an average processing time of 14.5 seconds. In testing, there is a misclick rate of 10.7%. This is because the user wants to explore the designed application. And this is also a limitation of this study, namely not making the overall application design interaction.
Analysis and Implementation of the Apriori Algorithm for Strategies to Increase Sales at Sakinah Mart

Sakinah Mart is a retail business that focuses on determining the layout of goods based on perceptions and implementing a discount system for specific items, but without offering bundling packages. This research aims to provide recommendations using the apriori algorithm as a decision-making tool for analyzing the layout of goods and bundling packages. The apriori algorithm is a data mining technique used to discover association rules and analyze customer purchases, specifically identifying the likelihood of customers buying item X along with item Y. The algorithm consists of two main components: support and confidence. The research applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, utilizing the apriori algorithm to analyze sales transaction data. The dataset includes 2000 sales transactions with two attributes, resulting in the identification of 2 and 3 itemsets. The findings include 16 rules with a minimum support value of 42% and a minimum confidence of 85% for the layout of goods. For bundling packages, 5 rules with a minimum support value of 40% and a minimum confidence of 90% were generated. These results offer valuable recommendations to the company, using the apriori algorithm for analyzing the layout of goods and bundling packages.
Application of the Minkowski Distance Similarity Method in Case-Based Reasoning for Stroke Diagnosis

A Stroke is a cerebrovascular disease characterized by impaired brain function due to damage or death of brain tissue caused by reduced or blocked blood and oxygen flow to the brain. Expert systems can be used as learning aids for medical students to diagnose stroke. Medical records of stroke cases can be reused as a reference for diagnosing stroke when there are new cases, known as the case-based reasoning (CBR) method. This study implements the Minkowski distance similarity method in CBR to calculate the similarity value between cases, where each similar case has the same solution. This study uses the Minkowski distance similarity method in CBR to obtain the most optimal value of r and the most appropriate threshold value in the expert system for stroke diagnosis. The diagnosis process is carried out by inputting the patient's condition, symptoms, and risk factors. Then the system will calculate the similarity value and take the case with the highest similarity value as the solution, providing that the similarity value must be greater than or equal to the threshold value. Based on system testing, the best accuracy value was achieved by applying a threshold value of 75 with an r value of 3 or 4, with an accuracy rate of 88,89%, a recall value of 88%, and a precision of 100%.
Implementation of Particle Swarm Optimization on Sentiment Analysis of Cyberbullying using Random Forest

Social media has exerted a significant influence on the lives of the majority of individuals in the contemporary era. It not only enables communication among people within specific environments but also facilitates user connectivity in the virtual realm. Instagram is a social media platform that plays a pivotal role in the sharing of information and fostering communication among its users through the medium of photos and videos, which can be commented on by other users. The utilization of Instagram is consistently growing each year, thereby potentially yielding both positive and negative consequences. One prevalent negative consequence that frequently arises is cyberbullying. Conducting sentiment analysis on cyberbullying data can provide insights into the effectiveness of the employed methodology. This research was conducted as an experimental research, aiming to compare the performance of Random Forest and Random Forest after applying the Particle Swarm Optimization feature selection technique on three distinct data split compositions, namely 70:30, 80:20, and 90:10. The evaluation results indicate that the highest accuracy scores were achieved in the 90:10 data split configuration. Specifically, the Random Forest model yielded an accuracy of 87.50%, while the Random Forest model, after undergoing feature selection using the Particle Swarm Optimization algorithm, achieved an accuracy of 92.19%. Therefore, the implementation of Particle Swarm Optimization as a feature selection technique demonstrates the potential to enhance the accuracy of the Random Forest method.
Comparison of Data Mining Classification Algorithms for Stroke Disease Prediction Using the SMOTE Upsampling Method

Stroke is a circulation disorder in the brain that can cause symptoms and signs related to the affected part of the brain and is the leading cause of death and disability in Indonesia. Everyone is at risk of experiencing a stroke, and it is important to recognize and manage risk factors. Data Mining techniques can help in the extraction and prediction of information, as well as finding hidden patterns in stroke medical data. The dataset used in this research comes from Kaggle and is imbalanced, so the SMOTE Upsampling technique is used to address this imbalance issue. The results of the study conclude that the use of SMOTE technique in the C4.5, NB, and KNN algorithms can increase precision, recall, and AUC. The C4.5 algorithm and SMOTE technique as the best performing algorithm were selected for testing new data, and the results show that the model created can predict stroke risk more accurately than the C4.5 model without SMOTE. However, it should be noted that based on the author's interview with one of the medical practitioners, the model cannot be directly used in medical practice because the observations in the medical field to determine factors related to stroke are highly complex. Thus, a new understanding revealed that predicting stroke in a practical setting is highly complex. While data mining can be used as a predictive tool in the initial stage for predictions in the general population, it is strongly recommended to undergo direct examination by doctors in a hospital to obtain more accurate and comprehensive medical evaluations.
Facebook Prophet Model with Bayesian Optimization for USD Index Prediction

Accuracy is the primary focus in prediction research. Optimization is conducted to improve the performance of prediction models, thereby enhancing prediction accuracy. This study aims to optimize the Facebook Prophet model by performing hyperparameter tuning using Bayesian Optimization to improve the accuracy of USD Index Value prediction. Evaluation is conducted through multiple prediction experiments using different ranges of historical data. The results of the study demonstrate that performing hyperparameter tuning on the Facebook Prophet model yields better prediction results. Prior to parameter tuning, the MAPE indicator metric is 1.38% for the historical data range of 2014-2023, and it decreases to 1.33% after parameter tuning. Further evaluation shows improved prediction performance using different ranges of historical data. For the historical data range of 2015-2023, the MAPE value decreases from 1.39% to 1.20%. Similarly, for the data range of 2016-2023, the MAPE decreases from 1.12% to 0.80%. Furthermore, for the data range of 2017-2023, there is a decrease from 0.80% to 0.76%. This is followed by the data range of 2018-2023, with a decrease from 0.75% to 0.70%. Lastly, for the data range of 2019-2023, there is a decrease from 0.63% to 0.55%. These results demonstrate that performing Hyperparameter Optimization using Bayesian Optimization consistently improves prediction accuracy in the Facebook Prophet model.
Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Biclustering is a method of grouping numerical data where rows and columns are grouped simultaneously. The Cheng and Church (CC) algorithm is one of the bi-clustering algorithms that try to find the maximum bi-cluster with a high similarity value, called MSR (Mean Square Residue). The association of rows and columns is called a bi-cluster if the MSR is lower than a predetermined threshold value (delta). Detection of people's welfare in Indonesia using Bi-Clustering is essential to get an overview of the characteristics of people's interest in each province in Indonesia. Bi-Clustering using the CC algorithm requires a threshold value (delta) determined by finding the MSR value of the actual data. The threshold value (delta) must be smaller than the MSR of the actual data. This study's threshold values are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. After evaluating the optimum delta by considering the MSR value and the bi-cluster formed, the optimum delta is obtained as 0.1, with the number of bi-cluster included as 4.
Multiplayer Game Guessing Sunda’s Proverb Using Socket.Io And Node.Js

Game development is currently quite rapid. Now games can be played by various groups, because many games now contain not just games, but there are also games with educational content. The educational game that will be made in this study is a website-based Sundanese proverb game, this type of game will be multiplayer so that players can compete with other players. The purpose of this research is to make a Sundanese proverb educational multiplayer game that can be played simultaneously with many players, so that it can introduce the regional language, namely Sundanese, to the wider community. The technology used in making this game is Socket.IO and Node.JS, using these technologies can make end users interact in real time. In making this game using the Game Development Life Cycle (GDLC) methodology with the stages of initialization, pre-production, production, testing, beta and release. The results obtained in this research are website-based Sundanese proverb educational games that can be used without taking up much space on the device.
THE RELATIONSHIP BETWEEN STUDENTS' INTERNAL AND EXTERNAL FACTORS AND THEIR ACADEMIC ACHIEVEMENT

This research aims to determine the relationship between students’ internal and external factors, such as student background, school characteristics, teacher factors, and internet use and the academic achievement of high school students majoring in science and social studies in Indonesia. Employing a quantitative approach, the research utilizes panel data from all 34 provinces in Indonesia over a span of 3 years, from 2017 to 2019. The data sources used are from the Ministry of Education, Culture, Research, and Technology and the Central Bureau of Statistics. The data is analyzed using random effect model through linear regression analysis of panel data. The results show that student background factors, such as student participation and scholarship have a positive and significant relationship with academic achievement. Additionally, school characteristic factors, specifically, being a private school and the use of the Internet have been shown to have a positive and significant relationship with academic achievement. However, the variables related to teacher factors, specifically qualifications and certification do not have any significant relationship with students’ academic achievement.
Sentiment Analysis of the Public Towards the Kanjuruhan Tragedy with the Support Vector Machine Method

A tragedy occurred in the Indonesian football world during the Arema vs. Persebaya match on October 1, 2022, resulting in the loss of approximately 714 lives, including 131 fatalities and 583 injuries. The tragedy is believed to have been caused by tear gas in the spectator stands and the closure of exits at the Kanjuruhan stadium. This event sparked a diverse range of public responses on social media, which can be analyzed through sentiment analysis. In this study, we employed the Support Vector Machine (SVM) algorithm, known for its speed and accuracy in text classification, to process and analyze tweets from October 1 to 31, 2022, as well as YouTube comments related to the Kanjuruhan tragedy from October 1 to November 20, 2022. Among the different SVM kernels, the RBF kernel exhibited the highest accuracy, precision, recall, and F1 scores, reaching 76.40%, 75.74%, 76.40%, and 75.18% respectively, when predicting data with three labels. Furthermore, the RBF kernel showed the best performance for data with two labels, achieving the highest accuracy, precision, recall, and F1-Score, which increased to 81.54%, 81.56%, 81.54%, and 81.56%, respectively.
Leaderboard Application as A Ranking Media for Internet Users

The technology of utilizing hotspot networks has developed quite rapidly. In its development, internet technology uses a more flexible Mikrotik hotspot because it provides convenience for administrators and users. The object of this study is the hotspot network of Hayam Wuruk University (UHW) Perbanas. The goal is to develop a leaderboard design as a medium for monitoring internet use through the UHW Perbanas hotspot. Its application is through the integration of mikrotik with the web service API as a ranking of internet users against three categories of activities, namely downloads, uploads and internet usage times on each day and month. Each of these categories has 20 users. The test method uses a black box. Hasil testing states that the system is successfully operating, so that it can be implemented in the context of decision making by the management of UHW Perbanas.
Meningkatkan Prestasi Belajar dan Bernalar Kritis Peserta Didik Fase F Pada PAKBP Menggunakan Model PBL Berbantuan Media Youtube

The teacher is one of the main components that really determines the success of the teaching and learning process. Thus, teachers should create the right situations and conditions to enable the learning process in students by directing all resources and using appropriate teaching and learning strategies, which include approaches, methods and learning techniques. To overcome the low learning achievement of students, researchers conducted Classroom Action Research on class XII SMKN Catering Services students. 1 Simanindo by applying the Problem Based Learning learning model assisted by YouTube. This research aims to improve learning achievement and critical reasoning abilities in students. This research was conducted in two cycles. Each cycle consists of several stages, namely planning, implementing actions, observing and reflecting. Data collection techniques were carried out by means of tests and observations, with a descriptive approach. The results of the research increased from cycle 1 to cycle 2. In cycle 1 there were still students in the appropriate category in the learning achievement variable and the category began to develop in the critical reasoning variable, then entering cycle 2 there was an increase with no students in the appropriate category in learning achievement variable, and categories began to develop on the critical reasoning variable.
An Interactive Lambda Calculus Execution Engine

In this poster, we present an interactive Web application that allows the student to input lambda expressions and interactively evaluate these expressions, one beta-reduction at a time. This tool will provide a platform for students to better understand how functional programs execute.
Modeling Model-View-Controller (MVC) Architecture Pattern as System of Systems (SoS) to Aid Comprehension and Reasoning

As software systems become increasingly complex, it is important that students develop software-is-system mindset. Our research aims to help engineers comprehend architecture patterns better using systems approach. We postulate that such an understanding is aided when we enhance pattern descriptions with a System of Systems (SoS) model of the pattern to better explicate the dynamics. We demonstrate this for Model-View-Controller (MVC) pattern, a common architecture pattern for interactive applications. Such an SoS model of the pattern can also be beneficial in the teaching-learning process in a software engineering classroom.
Is Transition Systems Approach of Modeling Software Systems Hard to Learn and Use?

Modeling software systems as transition systems can improve system comprehension for novice engineers and graduating students. However, this requires them to learn the vocabulary of transition systems and its use. We hypothesize that it is not hard for them to do so. To test this, we did a very short intervention in a software engineering course for two cohorts of students. Based on the transition systems knowledge demonstrated by the cohorts, we conclude that it is not hard for them to learn the vocabulary of transition system and its use. This result gives confidence to start designing longer intervention and study their effectiveness.