Reviewerly/exHarmony huggingface dataset:
Data Visualization Based On Sentiment Analysis to Identify the Quality of Internet Service Providers in Malaysia

Nowadays, most of the people have subscribed to their own internet service providers. However, due to slow of speed, connectivity, quality, and customer satisfaction need a proper evaluation. In addition, the rate of internet adoption is still slow. This project aims to develop a web-based system of data visualization based on sentiment analysis of internet service providers in Malaysia. For the project methodology is conducted in three phases, which include studying the existing problem of the internet service providers in Malaysia, developing a web-based system of data visualization based on sentiment analysis, and the evaluation of the usability of the system. The System Development Life Cycle (SDLC) has been used in this project which are analysis, design, implementation, testing, maintenance, and usability. The quality of internet services is a crucial factor in determining the satisfaction of customers in the telecommunications industry. The data visualization system based on sentiment analysis can assist customers in making informed decisions when choosing the right Internet Service Provider (ISP) by providing a better understanding of the quality of internet services offered by different ISPs. The sentiment analysis approach also done by using the corpus-based approach. The evaluation of the usability result was done by 3 experts through questionnaires using the linear scale indicator to evaluate the usability of the system. The visualization of data is important which can help the user understand the information in detail.
Learning about the Historical Building in Malacca Malay Sultanate Era Using Augmented Reality

Travel to Melaka application used to learn about the historical building in Malacca Malay Sultanate Era by using Augmented Reality (AR) technology. Learn about the historical building is very important. However, there are some students who are bored and not interested in learning History subject. The purpose of this application is to develop an educational application based on Augmented Reality for students to interest them in learning about the historical building. The design of this application is developed using multimedia elements which may interest the students to use this application. This application development was developed using the ADDIE Model. In addition, this research was evaluated by among 35 students from Sultan Idris Education University who pursuing a Bachelor of Education (History) with Honors by using quantitative methods through online questionnaire. As a result of the questionnaire, majority of respondents gave positive feedback and interested with Travel to Melaka application. In conclusion, learning about historical building in Malacca Malay Sultanate Era using Augmented Reality provides better knowledge and understanding.
The Development of the Learning Management System-Based Übungtest Schreiben at Level A1 for Students of PASCH School SMAN 1 Malang

This research and development aim to understand the process of developing Übungtest Schreiben to improve the writing skills of PASCH A1 students using the Learning Management System (LMS) and to find out the effectiveness of its use. By improving students' writing skills, it is hoped that the LMS-based Übungtest Schreiben can assist students in preparing for the German A1 exam. The background of this research and development is the situation of the PASCH A1 students who need a lot of writing exercises before taking the German language exam Goethe-Zertifikat A1 which corresponds to the Common European Framework of Reference for Languages (CEFR). This research and development uses the ADDIE model (Analyze, Design, Develop, Implement, Evaluate) and the product is Moodle-based Übungtest Schreiben that consists of a module, Schreibübungen, and Schreibtest/Modelltest for PASCH students at SMA Negeri 1 Malang. The type of data in this development is qualitative data and supported by quantitative data. Qualitative data is obtained in the analysis, design, development, and evaluation phases with a logbook and a questionnaire for material validation and the data is analyzed using the descriptive analysis technique. Quantitative data is obtained through the adequacy test in the implementation stage and the effectiveness test of Übungtest Schreiben. Based on the results of the material validation, test, and student questionnaires, LMS-based Übungtest Schreiben is very adequate and effective as a digital learning media to improve writing skills.
Improving Problem Resolve Skill and Science Learning Result with Creating Worksheet Based on STEM

The occurring problem, student worksheets have not been created truly in the learning process student worksheets used in the learning process only comes from. The student worksheets are not interactive, so that they have an impact on low learning outcomes and ability to solve science problems for fourth grade students at SDN 05 Binanga Dua. This research includes development research. This development research uses ADDIE development. The results of the development show that the manufacturing process produces the student worksheets on the STEM approach for grade IV students with a quality and valid. The results showed that the validation results of student worksheets feasibility test were based on the assessment of the three validators, namely the validation results of learning material experts of 88.23% with a very feasible classification, the validation results of linguists of 83.03% with a very feasible classification, the results of the presentation design expert validation of 93.75% with very decent qualifications. Furthermore, a feasibility test was carried out in small groups to obtain a data percentage of 84.6% and the trial was carried out again in the large group with a percentage result of 89.25% as a reinforcement of the feasibility assessment carried out by the homeroom teacher with a percentage result of 90%. So based on the validity of the student worksheets product, it shows very valid results which show that the student worksheets product is very feasible to be applied in learning activities.
A Preliminary Study on Examining the Determinants of Cloud Accounting Adoption for SMEs in Sungai Petani, Kedah

A preliminary study was carried out to determine the variables affecting SME business adoption of cloud accounting. A convenience purposive sample was used in this study to choose the targeted SME companies in Sungai Petani, Kedah. From May to June 2021, data was collected online using a Google form. A total of 77 respondents were noted out of the 100 Google forms that were disseminated. Pearson’s correlation analysis and multiple linear regression analysis were performed on the data. The results of this study demonstrated that adoption of cloud accounting among SME enterprises was significantly influenced by cost and security. According to the findings, SME enterprises should be targeted for intense cloud accounting promotion in order to take advantage of the system’s cost saving and optimum security. This will increase awareness of the benefits of cloud accounting in transforming a traditional business into a digital business. Due to the nature of this study, the findings are not generalizable. It is suggested that a more comprehensive study in Malaysia on cloud adoption should be conducted in the future.
Comparative Analysis of ISO27001 and NIST CSF

Recent developments in Information and Communication Technology (ICT) have had a significant impact on commercial organizations in achieving their goals and objectives. However, the introduction of ICT introduced new cyber risks and threats as well. To mitigate cyber threats, various cybersecurity frameworks and standards are available e.g., ISO/IEC27001 and NIST Cybersecurity Framework (CSF). These frameworks can be used to measure/ audit the maturity level of an organization’s cybersecurity status. In this study, we have compared the ISO 27001 and NIST CSF and map these frameworks with each other.
Autonomous Mobile Robot Navigation Using Lower Resolution Grids and PID-Based Pure Pursuit Controller

In the modern era, mobile robots are gaining special attention in various intralogistics operations such as warehousing, manufacturing, electrical high-voltage substations, roads, etc. These vehicles should be capable of effectively recognizing their routes, avoiding singularities and obstacles, and making a decision according to the environment. Hence an advanced control mechanism is required for path planning and navigation to work effectively in that dynamic environment. Keeping insight, into the challenges faced by the mobile robot this study aims to develop path planning using global (A* and Dijkstra) and local planners (Pure Pursuit) in a 2D navigation system, utilizing g-mapping for Simultaneous localization and mapping (SLAM) and Adaptive Monte Carlo Localization (AMCL) for probabilistic localization, to solve this issue. The system is designed to be compatible with the Robot Operating System (ROS) ecosystem. Path planning is carried out on a lower-resolution grid covering the navigable areas and the Pure Pursuit approach is enriched with a Proportional-integral-derivative (PID) controller. The results have shown that proposed schemes give superior performance in challenging obstacle-based warehouse systems, compared to publicly available ROS navigation planners.
Satellite Images Classification using MobileNet V-2 Algorithm

Satellite imagery is an invaluable source of visual information for environmental monitoring and land mapping with high resolution and wide coverage. In this modern technological era, advances in Deep Learning technology have brought great benefits in utilizing satellite images for various purposes. One of the efficient Deep Learning models for satellite image classification is MobileNet V-2, which is specifically designed for devices with limited resources such as smartphones. This study aims to develop an accurate satellite image classification model using Convolutional Neural Network algorithm and MobileNet V-2 model. The data used is taken from the RSI-CB256 dataset developed through crowdsourcing data. This research resulted in the performance of three deep learning models, namely ResNet50, MobileNet V-2, and VGG-16. ResNet50 is the highest model performed best during the training phase, achieve an accuracy of 98.40%. MobileNet V-2 and VGG-16 followed with 95.64% and 96.62% accuracy, respectively. The evaluation results demonstrate the model's strong ability to accurately classify satellite imagery and strengthen the model's ability to generalize well. With high accuracy and the ability to run on smartphone devices, this model has the potential to provide valuable information for governments and scientists in preserving the earth and better responding to environmental changes.
Implementation of Continous Delivery using Jenkins And Kubernetes with Docker Local Images

The need for software has increased and the development process has also become more complex as technology has developed which resulted application will take a long time to deployed, which can be completed in weeks or even months. One reason is the large number of teams involved in application development, especially the Development Team and Operations Team. These teams can cause the application delivery process to the user's side to be hampered if a conflict occurs, so the term DevOps appears. Support for DevOps continues to be improved, so there is CI/CD (Continuous Integration and Continuous Delivery/Deployment). Banyak penelitian mengenai CI/CD yang sudah dilakukan dan menggunakan tools Jenkins, Kubernetes, dan Docker. Namun penelitian yang sudah ada menggunakan repository DockerHub untuk menyimpan Docker Image This research focuses on the local implementation of the docker image which is then run with Kubernetes Orchestration so it can reduce the platform used by CI/CD. This implementation requires conversion from docker image to Kubernetes Image Cache. The results show that the Continous Delivery implementation using Kubernetes with the Local Docker image is successful and can run well. The results show that the average time required to create a docker image is 649 seconds (10 minutes 49 seconds) and image conversion process which takes an average time of 275 seconds (4 minutes 35 seconds). Research can be developed further by researching techniques to shorten build time, minimize resource utilitation and minimize time conversion from docker images to Kubernetes Image Cache.
Identification of Public Library Visitor Profiles using K-means Algorithm based on The Cluster Validity Index

The existence of a public library in the Gampingan village has a positive impact, such as increasing the literacy culture of the village community. However, the library collection is not sufficient for the needs of visitors. Therefore, it is necessary to add library collections to fulfill the requirement. One of the solutions is mapping the library needs of visitors. The mapping can be done by identifying visitor profiles by grouping visitors based on the criteria of age, gender, type of visitor, and category of book library. One of the methods that can be used in the process of grouping visitors based on criteria is to use the K-Means Clustering method. Determining the number of K cluster centers at K-Means Clustering method that are not appropriate will give bad results, it is necessary to test the number of K cluster centers using the Cluster Validity index by measuring the clusters with cluster variance, within-cluster variance, and between-cluster variance. From the grouping process using K-Means Clustering with Cluster Validity index, we get 3 clusters of visitor profiles with a cluster variance value of less than 0.1. This shows that this method was able to identify the visitor profiles with high grouping accuracy values.
Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown. This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.
Virtual Space For Virtual Reality Exhibitions With Oculus Quest Devices

Based on information from the Ministry of Cooperatives and MSMEs, currently the number of MSMEs has reached 64.2 million euros and their share of GDP is 61.07% or 8,573.89 trillion rupiah (Coordinating Ministry for the Economy of the Republic of Indonesia, 2021). The contribution of MSMEs to the Indonesian economy includes the ability to absorb 97 percent of the current total employment and generate 60.4 percent of the total investment. (Ministry of Investment, 2021). VR (Virtual Reality) technology is a technology that allows users to feel in a virtual (virtual) world in visual form and users can interact with a virtual environment simulated by a computer in the form of Android. The focus of this research is a technical way of creating a virtual space or virtual space and 3D objects in displaying products from SMEs to be marketed to consumers with the virtual reality method using the oculus quest device, this research uses the Luther method, a six-stage process for creating multimedia that includes concept, design, material gathering, assembly, testing, and distribution. System testing was carried out using black box testing and usability testing using the SUS Score standardization, with a total of 47 respondents getting an average score of 54%. This average number exceeds 50% of the standard SUS Score Analysis, so the virtual reality exhibition space is categorized as suitable and OK for use by users. And it can also help MSMEs in carrying out virtual reality-based online marketing.
LSTM and Bidirectional GRU Comparison for Text Classification

Although the phrases machine learning and AI are frequently used interchangeably and are frequently discussed together, they do not have the same meanings. While all artificial intelligence (AI) is machine learning, not all AI is machine learning, which is a key distinction. In the beginning, machine learning and natural language processing (NLP) are related since machine learning is frequently employed as a tool for NLP tasks. The advantage of NLP is that it can perform analysis, and examine a lot of data, including comments on social media accounts and hundreds of online customer evaluations. Text classification is essentially what needs to be done. This study compares Bidirectional GRU and LSTM as text classification algorithms using 20,000 newsgroup documents from 20 newsgroups from The UCI KDD Archive.&#x0D; After using the suggested model, we compare it to the long short-term memory and bidirectional GRU models for accuracy and validation. The results of the two comparisons show that the bidirectional GRU model performs better than the long short-term memory model. And this is a successful classification of text using a deep learning algorithm that uses a bidirectional GRU.
Optimizing Gender Classification Accuracy in Facial Images Using Data Augmentation and Inception V-3

In the digital era, facial recognition technology plays a crucial role in various applications, including gender classification. However, challenges such as variations in expressions and face positions, as well as differences in features between men and women, make this task formidable. This study aims to enhance the accuracy of gender classification using the Inception V-3 method and the Convolutional Neural Network (CNN), along with data augmentation techniques. The Inception V-3 method was chosen for its superiority in accuracy and speed. In contrast, the CNN model was selected in this study as a comparison and due to its algorithmic advantages in learning and extracting high-level features from images, including facial images, which are crucial for tasks such as gender classification. The data augmentation techniques in this study include rescaling, rotation, width and height shifts, shear range, zoom, horizontal flip, and fill method for model accuracy in gender classification with a small dataset. The study results indicate that the Inception V-3 model provides better accuracy (99.31%) in gender classification compared to the CNN model (81.31%). This conclusion underscores that the use of the Inception V-3 method with data augmentation techniques can improve the accuracy of gender classification in facial images.
Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization

In private universities in Indonesia, student graduation is something that is worth paying attention to, because it will be an aspect of the success of the university. Universities certainly have data on students who graduated, where student graduation data is very important to be taken into consideration by private universities, however with a lot of data it will make it difficult for private universities to find information from this data. Other researchers have previously carried out student graduation data with the same data by examining student graduation data using other methods. So we need a data mining algorithm that has never been tested on student graduation data. The method used is the neural network method with an optimization method, namely the particle swarm optimization method, to test the data, which will later produce information that is very useful for universities. After testing the student graduation data and getting accuracy results using the neural network method of 84.55% and after being optimized using the particle swarm optimization method, the accuracy results were optimal with a value of 86.94%. These results can be used by private universities to predict that students will graduate on time before they take their final semester so that the student graduation rate will be high.
Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.
Sales Conversion Optimization Analysis Using the Random Forest Method

Sales conversion is a challenging field of work in sales and business. Companies are competing to be winners by improving their services and hoping that their product sales can increase in various ways, including by using optimization theory. However, the lack of data analysis is a problem that is often encountered in optimizing sales conversions. Various machine learning-based methods have also been used to help analyze sales conversion optimization. This research uses the Random Forest method which is one of the more robust machine learning methods compared to other methods, namely Adaptive Booster (AdaBoost) and K-Nearest Neighbor (KNN) in analyzing sales conversion optimization. The results showed that the Random Forest method had the best performance in classifying data, by using the 10 cross validation technique the results were obtained with a Mean Squared Error (MSE) value of 0.928 and a Root Mean Square Error (RMSE) of 0.963, better than the Adaptive Booster method. and K-Nearest Neighbor which has lower performance. Sales conversion optimization processing using Random Forest is proven to have the best performance as evidenced by the small Mean Squared Error and Root Mean Square Error which means it has an accurate level of performance compared to other methods.