Reviewerly/exHarmony huggingface dataset:
ANALISIS SENTIMEN TWITTER TERHADAP WACANA PENUNDAAN PEMILU DENGAN METODE SUPPORT VECTOR MACHINE

Machine learning plays an important role in managing important issues to classify and predict information that develops ahead of the General Election in Indonesia. Especially in knowing public sentiment on the discourse on the postponement of the 2024 election through Twitter social media. So it is necessary to analyze the discourse by categorizing it as positive or negative. The Support Vector Machine (SVM) model is used for analysis and classification. The sample data used were 100 tweets data which were then scraped in the period January 2022 – May 2022. The processing was done using Python programming and Jupyter Lab tools. Before doing the analysis, do preprocess to eliminate unnecessary words and information so that the level of accuracy of the results of this Twitter sentiment classification can provide a closer picture of reality. As for the results of the grouping carried out positive sentiment in as many as 40 data tweets and negative sentiment in as many as 60 data tweets. The classification test results on tweets data with a good level of accuracy of 92%. These results are expected to be a reference for future researchers who want to improve accuracy or analysis results.
SISTEM INFORMASI MANAJEMEN ADMINISTRASI PKL DAN SKRIPSI (SIMADU) MENGGUNAKAN RAPID APPLICATION DEVELOPMENT (RAD) Studi Kasus: Fakultas Sains dan Teknologi UNIRA

Universitas Islam Raden Rahmat as a university in Malang Regency at the Faculty of Science and Technology in carrying out Field Work Practice (PKL) and Thesis activities still collects documents physically and is required to meet with Lecturers for verification. So in this study the author aims to design and build a system that can assist the administrative process of street vendors and Thesis. The method used in data collection was done by literature study, interviews and observation. The system development method in this study uses Rapid Application Development (RAD) because it can develop software according to user desires and can accelerate development. The method in the system testing stage is using Black Box Testing. This information system was developed using the Laravel framework as the backend of the system. The result of this research is a Sistem Informasi Manajemen Administrasi PKL dan Skripsi (SIMADU) which can help manage administration in the Faculty of Science and Technology. Based on the test results, the system that was built is running well and getting a 100% success percentage. Therefore, the SIMADU that has been built can be implemented at the research location.
<em>Let Them Speak</em>—A New Search Platform for Holocaust Testimonies

The growing eld of digital humanities utilizes cutting-edge technologies to develop new tools to compile, record, and represent documentation and materials in humanities elds and make them easily accessible for a broad range of users.Projects involving Holocaust commemoration, education, and research bene t from these technological developments in a variety of ways.As only a fraction of those who experienced the Holocaust are still with us today, digital humanities provide new means to organize, access, and utilize di erent types of testimonies and documentation.The new project Let Them Speak is a welcome addition to these endeavors.Led by Dr. Gabor Mihaly Toth, this platform of searching and navigating through videographies of survivors' testimonies is a joint e ort by three of the largest visual archives: the Yale Fortuno Archive for Holocaust Testimonies, the University of Southern California Visual History Archive, and the United States Holocaust Memorial Museum.Using interviews that were audio-or videographed in the past few decades, Let Them Speak constitutes an open source for thousands of already existing Holocaust survivors' testimonies, making them available for anyone interested-researchers, educators, and students alike.This visualized, well-thought-out platform boosts users' experiences of searching through the testimonies and processing and utilizing the information they convey.Combining the inventories of three archives, it allows easy, quick access to a large number of testimonies in one place.Free of charge, Let Them Speak relieves users from having to access the inventories via a university subscription, acquiring paid memberships, or
SISTEM PERINGATAN DINI BANJIR BERBASIS MACHINE LEARNING: STUDI LITERATUR

Indonesia is a disaster-prone country and 76% is a hydrometeorological disaster (floods, landslides, tropical cyclones and droughts). Floods that occurred in Jakarta had a negative impact on the community so that it would have an impact on economic losses that made it difficult for communities around areas that were frequently affected by floods to develop more advanced and productively. Therefore, the inhibition of increasing welfare caused by floods that are not immediately handled can increase the number of people's poverty because they always have to spend both on house repairs, health and other things caused by floods. In addition, public facilities and various kinds of infrastructure were damaged. The environment is also negatively affected when floods occur. Clean water is difficult to obtain so it causes many diseases. Floods also cause animals to be killed, thereby disrupting the natural balance of the ecosystem. The existence of flood prediction research will reduce the risk and damage caused by flood disasters and can provide advice and considerations in policy making. Flood early warning system is one of the solutions offered in dealing with flood disasters. Providing actual and real time information, this early warning system is expected to reduce economic losses to fatalities. In an effort to create a resilient city, ESCAP (2008) puts an early warning system in place as an effort to prepare before a disaster occurs and to mitigate floods.
Real Time Sign Language to Text Conversion

Abstract: This paper explores the potential to bridge the communication gap between those who speak sign language and those who do not understand it. Sign language is a visual language that is primarily used by people who are deaf or hard of hearing. It is a unique and complex form of communication that involves a combination of hand gestures, facial expressions, and body movements. However, for those who do not know sign language, it can be challenging to communicate with individuals who use it as their primary means of communication. By using advanced technology, we can convert sign language gestures into written or spoken language, allowing for effective communication between people who know sign language and those who do not. In this way, we can work to break down communication barriers and promote greater inclusion and accessibility for people who are deaf or hard of hearing.
Data-driven Decision Making: Leveraging Artificial Intelligence for Image Analysis

Data-driven Decision Making: Leveraging Artificial Intelligence for Image Analysis
PENERAPAN METODE DECISION TREE C4.5 DALAM MEMPREDIKSI KELANCARAN PEMBAYARAN KREDIT DI BPR WAHANA BERSAMA KPUM

BPR Wahana Bersama KPUM is a lending company. Delay in credit payments is a problem that occurs in credit companies. Customers who don't pay on time can have a bad impact on their credit history. To assess customer profitability, a system is needed that can predict the smoothness of future credit payments in order to assess whether customers are profitable or not. The author uses the Decision Tree C4.5 method in which the method looks for similarities between classes or groups in the data. In this study, the data used is customer data, with training data from 2017-2020 and testing data from 2021. The existence of this system can help BPR Wahana Bersama KPUM in predicting the smoothness of credit payments in the future so that there will be no credit payment jams by customers. , this is proven by the acquisition of the accuracy value using the confusion matrix model reaching 84.18%.
The reliability/ validity of cognitive/emotional approaches to the evaluation of musical performance: Implications for competition juries

Procedures to evaluate the quality of musical performance, like psychological tests generally, vary in reliability and validity. How can both be optimised? The subjective world of jurors comprises input (sensations) and output (thoughts and emotions). Cognitive approaches to performance evaluation, in which different aspects are analytically considered and the results combined, are more reliable than emotional approaches, which are ultimately based on holistic ‘gut reactions’. However, emotional approaches may be more valid. Both depend on the serial order in which performances are presented to evaluators, suggesting a need for independent, computer-controlled procedures in which jurors evaluate performances in different random orders. Jurors can be influenced by performers’ appearance and movements, as well as knowledge about past performances, whether or not they believe they should be; additional blind evaluations (sound only) could help. Evaluations may depend primarily on specific features such as right-hand melodic phrasing in romantic piano music. It would be interesting to systematically track the emotional state of jurors during a competition in order to better understand the interaction between their thoughts and emotions. In general, traditional approaches to performance evaluation might be supplemented (not replaced) by psychologically inspired, computer-based procedures. A ‘superjury’ could compare evaluations from different methods or ‘subjuries’.
Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment

Balancing load in cloud based is an important aspect that plays a vital role in order to achieve sharing of load between different types of resources such as virtual machines that lay on servers, storage in the form of hard drives and servers. Reinforcement learning approaches can be adopted with cloud computing to achieve quality of service factors such as minimized cost and response time, increased throughput, fault tolerance and utilization of all available resources in the network, thus increasing system performance. Reinforcement Learning based approaches result in making effective resource utilization by selecting the best suitable processor for task execution with minimum makespan. Since in the earlier related work done on sharing of load, there are limited reinforcement learning based approaches. However this paper, focuses on the importance of RL based approaches for achieving balanced load in the area of distributed cloud computing. A Reinforcement Learning framework is proposed and implemented for execution of tasks in heterogeneous environments, particularly, Least Load Balancing (LLB) and Booster Reinforcement Controller (BRC) Load Balancing. With the help of reinforcement learning approaches an optimal result is achieved for load sharing and task allocation. In this RL based framework processor workload is taken as an input. In this paper, the results of proposed RL based approaches have been evaluated for cost and makespan and are compared with existing load balancing techniques for task execution and resource utilization..
PEGELOLAAN DATA DAN HISTORI PENGGUNA UNTUK PENGEMBANGAN SISTEM INFORMASI BERKAH BERSAMA BERBASIS WEBSITE

The Berkah Bersama Information System is an information system for manage various data of division general and finance, logistics, also production and marketing used in the Berkah Bersama Company. However, there are still some data management that are not yet included in the system and are still carried out computerized but not integrated through Microsoft Excel. Some of these data include data recording, RHPP (Daily Recapitulation of Farmer Maintenance), drug programs, chick in profit and loss, and user history. So, it is necessary to develop so that data management in the system becomes more complex. This research uses an incremental model, ERD (Entity Relationship Diagram), UML (Unified Modelling Language), and uses the CodeIgniter framework. The results of this research are in the form of Data Management and User History for the Development of a Website-Based Berkah Bersama Information System which useful for managing user data and history. System testing is carried out using the Black-Box Testing method, and the system run according to its functions.
A Hyper-parameter Tuning based Novel Model for Prediction of Software Maintainability

Software maintainability is regarded as one of the most important characteristics of any software system. In today's digital world, the expanding significance of software maintenance is motivating the development of efficient software maintainability prediction (SMP) models using statistical and machine learning methods. This study proposes a hyper-parameter optimizable Software Maintainability Prediction (HPOSMP) model using the hybridized approach of data balancing and hyper-parameter optimization of Machine Learning (ML) approach using software maintainability datasets. The training dataset has been created with object-oriented software namely UIMS and QUES. To balance the dataset, Synthetic Minority Oversampling Technique (SMOTE) technology has been adopted. Further, Decision Tree, Gaussian Naïve Bayes, K-Nearest neighbour, Logistic Regression, and Support Vector Machine are adopted as Machine Learning and Statistical Regression Techniques for training of software maintainability dataset. Results demonstrate that the proposed HPOSMP model gives better performance as compared to the base SMP models.
ANALISA USER EXPERIENCE TERHADAP FITUR DI APLIKASI ZENIUS MENGGUNAKAN HEART FRAMEWORK

Learning facilities in the current digital era have developed far, including the learning institution PT.Zona Edukasi Nusantara (Zenius) which has begun to adapt by launching an application for learning facilities called Zenius. Currently, Zenius has become one of the edu techs in Indonesia that has a large number of active service users. To ensure the convenience of application users, it is necessary to measure User Experience to find out whether there is still need for improvement in its use. In this study, the HEART Framework is used as a framework for measuring the Zenius application using the questionnaire method. The results of this study indicate that there needs to be some improvements in various aspects such as redesigning the display by paying attention to aspects of user comfort and interface display, feature innovation, adding features, and socializing features.
E-Commerce Website based on Chatbot

Abstract: Nowadays, traditional markets are gradually are being replaced by many online marketplaces. Fierce competition in the online market demands excellent service from both sellers and buyers, so many online stores offer his 24-hour service. This service would certainly cost a lot of money if done manually. Chat bots can be used as a solution for automated shopping online. Second, the bot must be able to answer accurately and quickly. In this study, we propose an intelligent chatbot system based on AIML (Artificial Intelligence Markup Language) that can be used as an e-commerce assistant. Input questions from users are processed in her three phases: parsing, pattern matching, and AIML crawling the data. In the AIML process, user requests fall into his three categories: general questions, calculations, and inventory checks. Where compute requests intercept the order and payment process
Peningkatan profesionalisme guru melalui pelatihan model belajar bahasa inggris daily language

Because English is a global language, mastery of it is a necessity, especially for academics such as teachers and lecturers. English as a foreign language is not used as a daily language like English as a second language; it is only used and taught in schools during class hours. The purpose of this community service is to train teachers to improve their daily English language skills with the daily language application. The method of implementing the activities is divided into preparation, implementation, and monitoring of community service. This service is carried out online using Zoom meetings with 20 teachers. English as a foreign language is not used as a daily language like English as a second language; it is only used and taught in schools during class hours. Daily language is a mobile learning tool, so it is flexible for teachers and can be adapted to their hobbies in practice.
SISTEM INFORMASI PENJUALAN ONLINE SAUS DAN SAMBEL EXPRESS

The data processing system is one of the functions of administrative activities in providing services to customers and to meet any information demands of the manager. A good information system can always solve problems that occur and can produce fast, precise, and accurate information. The computer is a tool for producing the right information with access to human work. In the midst of this technological development is to analyze and design a system of selling sauce and chili sauce. This system is made to make it easier for office staff to check incoming orders and make it easy for customers to order goods. Problems often occur, namely the lack of information for incoming orders. The method used for data collection is analysis (by surveys, interviews and literature studies). The result of this research is a sales system that can be used by office staff and managers during working hours. Can improve efficiency in activities so that the service is better than the previous system. The conclusion is that this system has designs such as product names, purchase confirmations, customer lists and stock items.
TEXT MINING DAN KLASIFIKASI MULTI LABEL MENGGUNAKAN XGBOOST

The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.
SIMULASI MONTE CARLO DALAM MEMPREDIKSI PEMAKAIAN OBAT PENYAKIT GIGI DAN MULUT PADA RUMAH SAKIT

The use of drugs in patients with dental disease is a necessity that needs to be considered by the hospital in providing medical services to patients. Adequate and well-managed drug supply prevents shortages or excess drug stocks. So it needs good planning in managing and monitoring drug stocks appropriately. This study aims to make predictions in the use of dental disease drugs by using a monte carlo simulation. The data used is data on the use of drugs for dental diseases from 2020 to 2022. The data on drug use processed were 12 types of drugs. The data will be processed based on the Monte Carlo simulation stages. The results of using the Monte Carlo Simulation are to obtain predictions of the use of dental disease drugs with an accuracy value reaching 89.14%. Based on the accuracy value obtained, the Monte Carlo simulation can be used to predict drug use in the future. So that the supply of dental disease medicine is maintained.
Accelerometer-Based Gesture-Based Wheelchair Control

Abstract: There are approximately 6 million paralysed individuals in the world who desire wheelchairs, according to a study to improve their mobility.In the past, wheelchairs needed to be transported and externally supported using any method. Joystickcontrolled wheelchairs are being enhanced to help combat this. In the past, wheelchairs needed to be transported and externally supported using any method. Joystick-controlled wheelchairs are being enhanced to help combat this. However, joystick-controlled scooters appear to be challenging to use on a daily basis. The use of the joystick is made more challenging by the hard buttons and the joystick's unidirectional use, particularly in the case of a paralysed individual. We attempted to create a "gesture-controlled wheelchair" that can be propelled with a small hand tilt in order to get around these issues. Control flows up the character's lower back and can be applied to either hand.
Safeguarding your Data from Malicious URLs using Machine Learning

Abstract: Malicious URLs host a wide range of unwanted content and can be extremely dangerous to potential victims. Thus, a quick and effective detection method is required. The topic of identifying harmful URLs based on data gleaned from URLs using machine learning methods is the main subject of this thesis. The simplest method of obtaining sensitive information from unwitting people is through a phishing attack. The goal of phishers is to obtain crucial data, such as username, password, and bank account information. Cybersecurity professionals are now looking for stable and reliable detection techniques to detect phishing websites. In order to distinguish between legal and phishing URLs, this article uses machine learning technology. It extracts and analyses many aspects of both types of URLs. Algorithms such as Support Vector Machine, Decision Tree, and Random Forest are used to identify phishing websites. By evaluating each algorithm's accuracy rate, false positive and false negative rates, the study aims to identify phishing URLs and identify the best machine learning method.
MULTIFACETED FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: BOOTSTRAP PANEL CAUSALITY APPROACH

A large body of both theoretical and empirical literature has been conducted to show the relationship between financial development (or called financial depth) and economic growth. However, for Central Europe and Baltic countries, the existing empirical evidence is scarce. This paper revisits whether financial development stimulates economic growth in the context of eleven Central Europe and Baltic countries during the 2000-2019 period. By incorporating broad characteristics of financial institutions (such as banks and insurance companies) and financial markets (including stock markets, bond markets, and derivative markets) into the finance-growth nexus, this paper not only attempts to underline the potential importance of financial depth for economic growth but also tests the hypothesis that financial access, efficiency, and stability promote economic growth or vice versa. This paper applies a panel bootstrap approach to Granger causality testing, which considers cross-sectional dependence and heterogeneity issues. Empirical results show limited support for the supply-leading and demand following hypotheses.