Reviewerly/exHarmony huggingface dataset:
Forecasting Blood Demand Using the Support Vektor Regression Method (Case Study: Blood Transfusion Unit-PMI Central Lombok)

Blood is an important component produced by the human body. Blood is also a very vital part of human survival. When blood levels in the human body are less than they should be, the way to overcome this is by donating blood or blood transfusion. The health facilities that organize blood donations, provide blood and distribute blood are called Blood Transfusion Units (UTD). UTD in carrying out its duties encountered several obstacles, such as blood only having a shelf life of 35 days from donation. If it has passed the expiration date, it cannot be used anymore for blood transfusions. Meanwhile, regarding the demand for blood, the need for blood is greater than those donating. Making it difficult for UTD if the demand occurs when the existing blood stock is not sufficient. And if the stock in UTD experiences an axcess, it can cause losses because the blood is wasted due to expiration. Apart form that. The problem is that in everyday life, many people’s need for blood is reduced. Many of their families intervened directly to find available donors. They even search on social networks or social media such as WhatsApp, Facebook, Instagram and others. And this shows that many of them lack donors. To anticipate these problems. So it is necessary to carry out research on forecasting blood demand using the Support Vektor Regression method at UTD PMI Central Lombok. The aim of this research is to forecast or predict the demand for blood at UTD PMI Central Lombok in the coming period. To reduce the impact of lack or excess blood. SVR is the application of Support Vektor Machine (SVM) in the case of regression to find the best dividing line in the regression function. The advantage of the SVR model is that it can handle overfiting problems in the data. The tests used to measure the best model are Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). The results of this research shows that the best model is Support Vektor Regression (SVR) with a polynomial kernel and based on the tuning results, the parameters used are C=10, degree=1, epsilon=1. The SVR model using a polynomial kernel produces a MAPE value of 18.7502% and RMSE value of 0.6919, which means the model has very good predictive ability. Prediction accuracy was achieved with an R2 value of 0.9936 or 99.36% and an MSE value of 0.4787, which means that the prediction of blood demand data at UTD PMI Central Lombok using SVR with a polynomial kernel function had very good prediction accuracy. With predicted result in january for blood type A it was 1654, B was 920, O was 2205 and AB was 1104
Implementation of Android-Based Tailoring Service Ordering Application with Geolocation Integration

Basic needs are an aspect that cannot be separated from human life, and one of the important aspects of these basic needs is clothing which not only functions to protect the body but also plays a role in the world of fashion. To meet these fashion needs, tailoring services have a key role in providing clothes that suit consumer desires. While this need is important, customers often rely on conventional methods, such as word-of-mouth recommendations or seeking out a familiar tailor. Some tailors may have a sign in front of their house, but it is often difficult for potential customers to find and connect with them. Therefore, this research aims to implement a tailor service ordering application that is integrated with geolocation features and can make things easier for tailors and consumers. This application development uses the Java programming language and Android Studio as the framework and Firebase as the database. The result of this research is an application that allows customers to order tailor services via smartphone anywhere and anytime.
Socialization of Cyber Crime Threats Ahead of the 2024 Election for the Online Ojek Community

Ahead of the 2024 General Election, cyber threats are expected to increase along with the increasingly massive use of cyberspace for political purposes. Members of the Online Ojek Community are a group of people who are vulnerable to being targeted and "perpetrators" of cybercrimes. This is because the online driver community always interacts with digital devices and the general public. The dedication team has carried out the activity stages, starting with a questionnaire of 16 questions, lectures, and outreach to 15 participants, as well as discussions and questions and answers. Related material on how to filter and test the correctness of data and information; how to act and behave in the use of social media and digital devices; how to know the rule of law in the use of digital technology. After the implementation of the activity, there was an improvement in the participants' understanding. Initially, the average participant had an adequate understanding of the number 72, after the implementation of the activity, the average participant's understanding was very good with a score of 92.86. This increased knowledge will reduce the risk of Cyber Security Threats for online motorcycle taxi drivers.
CLUSTERING OF FOOD SECURITY IN PAPUA ISLAND: WITH AN OPTIMIZED SPATIAL FUZZY CLUSTERING APPROACH

Food needs are primary needs that must be met by everyone to maintain their survival. The condition of stable food security is still not evenly distributed throughout Indonesia. Papua Island is one of the regions that has low food security conditions. In identifying the characteristics of food security in Papua Island, it can be done using cluster analysis. The cluster analysis method used in this research is classical FGWC and FGWC with optimization of HHO. This method has considered the spatial element in the grouping process. The purpose of this study is to classify the level of food security in Papua Island and determine the effectiveness of using optimization in FGWC. Based on the evaluation results, it was found that the FGWC-HHO method was the most optimal method with a total of 3 clusters. Cluster 3 consists of 18 regencies is a cluster with a low level of food security, so this cluster can be used as a priority in handling the problem of food security which is still low in Papua Island.
​​RESEARCH ON THE SATISFACTION OF EDUCATIONAL PRACTICE IN NORMAL COLLEGES A SURVEY OF STUDENT LEADER TEACHERS AND INTERNSHIP SCHOOLS​

Educational practice is an effective form of cooperation between universities and primary and secondary schools. Teaching practice can promote the improvement of teaching quality in primary and secondary schools, and also promote the integration of college students' theoretical knowledge and practical experience, and promote their professional development. From the perspective of practice leader teachers and practice schools rarely involved at present, this study analyzes and understands the satisfaction survey of practice leader teachers and practice schools on the subject of educational practice (students, teachers and schools) through questionnaire design and data collection. The survey results show that the satisfaction of the intern and the intern leader teachers is between very satisfied and satisfied. The student leader teachers' satisfaction with the school and students ranged between very satisfaction and satisfaction, and more tended to be satisfied. The satisfaction with the school (C school) Educational practice management was between very satisfaction and satisfaction, and some contents were between satisfaction and basic satisfaction. It shows that the practice leader teachers and practice schools are basically satisfied with the subject of Educational practice and Educational practice.
SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS

Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. Meanwhile, LSTM is a network architecture with the advantage of learning long-term dependencies on data. LSTM can remember long-term memory information, learn long-sequential data, and form information relation data in LTM. The combination of LSTM and GRU aims to overcome RNN’s weakness in LTM. The LSTM-GRU is combined by adding GRU to the data generated from LSTM. The combination of LSTM and GRU creates a better performance algorithm for addressing the LTM problem.
COMPARING GAUSSIAN KERNEL AND QUADRATIC SPLINE OF NONPARAMETRIC REGRESSION IN MODELING INFECTIOUS DISEASES

The regression curve for nonparametric regression is assumed to belong to some infinite-dimensional collection of functions, which allows great flexibility in the form of the curve. This research intends to compare the Gaussian Kernel and Quadratic Spline regressions in four infectious diseases in Indonesia by 2021. The data used is secondary data from the Central Bureau of Statistics and the Ministry of Health, Indonesia, and the sample consists of four infectious diseases in Indonesia by 2021 (Tuberculosis, Diarrhoeal, Pneumonia, and COVID-19). Considering the correlation value, it was found that the independent and dependent variables of the four infectious diseases are all highly correlated (r values are more than 0.7). Furthermore, the scatter plots for four infectious diseases do not follow a particular pattern; due to this, parametric regression cannot be used to analyze the data. Therefore, nonparametric regression was applied in this research . According to the analysis, the Gaussian Kernel is the best regression technique for modeling four infectious diseases in Indonesia by 2021, which its R2 values are 99.85% (Tuberculosis), 100% (Diarrhoeal), 99.91% (Pneumonia), and 99.99% (COVID-19).
APPLICATION OF FUZZY ANALYTICAL NETWORK PROCESS IN DETERMINING THE CHOICE OF AREAS OF INTEREST

The Untan Statistics Study Program offers students a choice of areas of interest to develop competencies, attitudes, and skills. This study aims to analyze the decision to determine the choice of field of interest according to lecturers and students using the Fuzzy Analytical Network Process (FANP) method. A combination of ANP methods and Fuzzy logic, FANP is used to model and analyze complex networks of several factors determining the choice of areas of interest. The step in this study begins with the determination of the criteria and sub-criteria used for tissue formation. Then a comparison was carried out in pairs using the Fuzzy scale, so that the calculation of the global weight value of each criterion and sub-criteria was obtained. The resulting weight can be used for decision making. Data in research affects the opinions of lecturers and students. The decision obtained using the FANP method in this study is in the opinion of lecturers and students that the fields of business and finance are priority alternatives with the highest weight of 44.5%. The second priority with a weight of 37.5%, namely social and industrial interests, and the environmental and disaster sector occupies the last priority with a weight of 18%.
MIXED ESTIMATORS OF TRUNCATED SPLINE-EPANECHNIKOV KERNEL ON NONPARAMETRIC REGRESSION AND ITS APPLICATIONS

Research on innovations in the statistics and statistical computing program systems implemented in the health sector. The development of a mixed estimator model is an innovation of nonparametric regression analysis by combining two approaches in nonparametric regression, namely the truncated spline estimator and the Epanechnikov kernel. The urgency of this study is that there are often cases where there are different data patterns from each predictor variable. In addition, by using only one form of the estimator in estimating a multivariable regression curve, the result is that the estimator obtained will not match the data pattern. The research objective was to find a mixed estimator between the truncated spline and the Epanechnikov kernel and the estimator results were applied to Dengue Hemorrhagic Fever case data. The unit of observation is a province in Indonesia and This study relied on secondary data received from the Central Statistical Agency (BPS) and the Health Office. Based on the analysis results, it was found that the best model of nonparametric regression with a mixed estimator of the truncated spline and Epanechnikov Kernel is a model with 3 knots with a combination of variables. The coefficient of determination (R2) is 98.11%. We can conclude that the mixed estimator tends to follow actual data and represents a nonparametric regression model with a mixed estimator that can predict the number of Dengue Hemorrhagic Fever Cases in Indonesia
Improving Firebase BaaS Service Security in Counseling Chat Applications: AES-256 and CBC Approach for End-to-End Encryption

The activities of using the internet and exchanging information and sending messages have used a lot of internet media, one of which is chat message media, but over time data and information security problems in chat messages that are often encountered are active and passive wiretapping. In this research, the author wants to create a counseling chat message security application using the AES (Advanced Encryption Standard) algorithm cryptographic method combined with the CBC (Cipher Block Chaining) technique which is an advanced development of the ECB (Electronic Code Book) technique. AES basically uses a block cipher with a length of 128 bits as the default operation, and the key length size varies from 128, 192 and 256 bits. so AES uses a 4x4 matrix equation with each section having a size of 1 byte. From these problems, research will be conducted to develop an application to accommodate the counseling process using a chat application that has the main focus of securing messages with image types and stored in the Firebase database service (Backend as a Service). As well as using the End-to-End service principle so that users do not need to do the encryption or decryption process directly because the process has been carried out by the system, this will also provide more security aspects in terms of confidentiality of key data and initialization vectors. So that the process of exchanging information using the media chat counseling application is secured and avoids tapping by irresponsible parties.
Kesadaran Masyarakat Dalam Mengurangi Bencana Banjir Dilihat Dari Aspek Hukum Di Sumatera Selatan

Indonesia is a country that is prone to disasters. Based on BNPB data, from 2018 to 2019 there were 3,999 disasters. The total potential area of ​​flood hazard in South Sumatra Province as a whole is 6,476,932.00 Ha and is in the High class. The extent of flood hazards is broken down into 3 hazard classes. In reality, the survey results show that knowledge of disasters in Indonesia is still not accessible to the general public. There is a gap between disaster knowledge and existing knowledge at the community level. This is proven by the community's lack of responsiveness in protecting the surrounding environment, their lack of response to natural phenomena and their tendency to wait for the government to handle disasters. The success of this strategy is expected to increase public awareness of disasters so that they actively participate in flood prevention. The data collection technique used is in the form of a questionnaire or questionnaire. The results show that children who have environmental awareness in Plaju District are only around 50.57%. The value of children's environmental awareness based on the results of the questionnaire is still low, namely 49.86%. And from the survey results, the government must raise environmental awareness among the people of Plaju sub-district, which must be done by the government, the managers (RT/RW), one of which is the need for support and motivation from related parties such as the government in the form of counseling and providing capital to encourage its citizens to process waste into something useful. useful so that it can convince residents by creating jobs and minimizing waste.
Assessing the Level of Job Engagement among Employees in Major Municipalities in the Gaza Strip

The aim of this study was to explore the reality of Job engagement and its components (job involvement, job satisfaction, and job enthusiasm) from the perspective of employees in the major municipalities in the Gaza Strip. The descriptive-analytical approach was used, with a study population of 2,714 employees. The sample consisted of 336 employees selected through stratified random sampling. A questionnaire was used as a data collection tool, and 227 questionnaires were retrieved, representing 75.89% of the study sample. Data analysis was conducted using SPSS software to obtain the results. The study revealed several findings, the most prominent of which was the presence of Job engegement in the major municipalities in the Gaza Strip at a high level, with a rate of 71.7%. The study recommended that municipal management involve employees in decision-making processes and encourage them to contribute ideas and suggestions that contribute to work development through various competitions. It also recommended that municipal management pay more attention to the work environment, create a well-prepared environment, provide financial incentives in addition to motivational incentives, and provide opportunities for employees to attend training courses to enhance job satisfaction. Furthermore, the study emphasized the importance of addressing the psychological aspects of employees by designing stimulating programs aimed at increasing enthusiasm and dedication to work, as well as promoting cooperative work through organizing informative and educational seminars to ensure the continuous provision of services to the community.
English Learning Interest of the Junior High School Students: A Mixed Methods Study

The pandemic has provided a significant change in the learning system. It changed the teachers and students’ methods in learning. During pandemic, the learning process through screen and learning management system. That condition gave positive and negative effect in learning. The positive impact of the pandemic is that teachers and students are able to used online learning methods and improve their skills in using internet and IT, while the negative impact is that students decreased their interest in learning because students tend to confused and misunderstood on the teachers’ explanations. The purpose of this study was to analyze how the interest in learning English of nine grade students, why is the student on that level of interest, and whether there is a significant relationship between students' learning interest and English mastery. The research method used was a mixed-method study with 36 participants of Junior High School students. The convergent parallel design used in this study. The instruments used are questionnaires, interview, and students’ final score in the first semester in academic year 2022/2023. Based on the data obtained, there are 9 students who have a high level of interest in English, 26 students have a medium level of interest in English, and 1 student has a low level of interest in English. The result showed that there is a significant relationship between students' English learning interest and students’ English mastery and a positive effect on students' English skills.
The Effect of Teacher Scaffolding and Peer-Scaffolding on Reading Comprehension and Vocabulary Ability

This study aims to analyze the effect of teacher and peer scaffolding on students' reading comprehension and vocabulary mastery based on a sociocultural perspective. The quasi-experimental research method was used in this study and involved 300 high school students. Participants were divided into an experimental group (the group that received the teacher scaffold and the peer scaffold) and a control group that received the traditional intervention. The pretest was conducted to find out their initial vocabulary skills and reading comprehension skills and ended with a posttest. Descriptive statistical analysis and one-way ANOVA analysis were used to analyze the research data. The findings of the study indicated that the experimental group that received the intervention of the teacher scaffold and the peer scaffold experienced more significant improvements in reading comprehension and vocabulary skills than the control group. The experimental group with the peer scaffolding intervention showed better ability improvement compared to the experimental group that received the teacher scaffolding intervention. This study has implications that peer scaffolding can be used to support the effectiveness of teaching reading.
SEGMENTATION OF LUNG CANCER IMAGE BASED ON CYTOLOGIC EXAMINATION USING THRESHOLDING METHOD

Lung cancer is the most dangerous cases which mostly attacks the man with the biggest causes of smoking. This cancer threatens the second largest death after heart attack, lung cancer cases increase significantly every year in various countries. Several methods have been established to detect lung cancer, including Computed Tomography of the thorax, sputum examination and cytology examination. The most decisive examination is through cytologic examination of the pleural fluid. However, the current state of biopsy performed by doctors does not always get a lot of specimens, making it difficult to determine the presence of cancer cells in the lungs. Cytological examination through the pleural fluid has difficulty in detecting cell images. The image of pleural fluid that has a high density between cells will produce an image with low detail, while an image with a low density will produce an image with high detail. Image segmentation is an important part in determining the cellular anatomy of pleural fluid to characterize images with cancer or normal categories. We propose the methodology of research by using group images to separate objects from other objects by highlighting important parts using image segmentation on pleural fluid of patients suspected of having lung cancer. Thresholding method used to see the comparison is Adaptive Thresholding, binary thresholding and Otsu Thresholding. The classification results of the three methods show a high accuracy of 99% on binary thresholding, then 97% accuracy on otsu thresholding and the lowest accuracy of 96% on adaptive thresholding, the three methods are considered to increase in proportion to the addition of the epoch parameter.