Reviewerly/exHarmony huggingface dataset:
Securing Text File Using Combination of Vigenere and One – Time Pad Cipher Algorithm

Technological developments continue to increase, causing many crimes to occur on social media. There are various types of encryption that can be used. So we need a system to protect and prevent misuse of data by unauthorized parties. One of the techniques used in this study is the encryption method. Encryption is applied to user passwords on web login systems. Simulation results can be tested by measuring resistance to attacks or data theft. In this study, the Vigenere cipher was combined with the One-Time Pad cipher, which then produced encryption in the XOR results. Furthermore, the hash value that can be obtained by implementing the MD5 algorithm on encrypted files is used as a means of verifying encrypted files, further strengthening data security while confirming its integrity. Furthermore, the results show that the proper implementation of the algorithm affects data security positively. The test results were obtained with a value of 71%, which means that the encoding results are good enough for data security.
Markerless Augmented Reality Application for Indonesian Traditional House Education

This paper proposes the development of a markerless augmented reality (AR) application for Indonesian traditional house education. The application aims to provide an immersive and interactive way of visualizing traditional houses from various regions of Indonesia, with the potential to raise public awareness about the importance of preserving traditional culture. The prototyping method was employed as the most appropriate approach to software development, and the ARFoundation platform was used to integrate plane detection into the AR application. The results showed that 80% of tested devices were compatible with ARCore, and the AR plane detection could detect up to 250cm from the device position. In addition, a feedback survey conducted on 17 testers showed that the application design and mechanism brought a good experience to users, with 53% stating that the app is easy to use. This research project contributes to the development of technology-based solutions to promote Indonesian culture and preserve traditional houses, especially for younger generations who may be less familiar with traditional culture.
Augmented Reality of Historical Relics in the British Museum

Museums are one of the tourist attractions that are in great demand by the public. Museums can be used as a place to increase knowledge for many people; many people visit museums during the holiday season or on tourist tours, causing severe congestion when they want to visit the museum during that season. This hinders people from enjoying and learning about historical relics. Therefore, our group decided to help people to learn about the historical relics in the museum by using other media. Our group will create an Augmented Reality (AR) application in the form of historical relics in the British Museum. Augmented Reality (AR) will be created using the SDLC (Software Development Life Cycle) method to be able to produce maximum results so that users can use this Augmented Reality (AR) application more easily. The expected result of making this Augmented Reality (AR) application is to reduce the density in the museum, helping people to recognize and gain information about relics, paintings, sculptures, and others in the British Museum without having to visit the museum, as well as to assist students in learning about British historical heritage with a more fun method in a 3D View.
Deafvoir: Recognizing Sign Language Through Game

People with hearing impairments exist in our society, whether we realize it or not. It is not easy for them to communicate with us, the ones without hearing disabilities, and they often receive a lot of mistreatment. Since we don't learn about sign language and don't know where to start, our knowledge about sign language is also limited. This paper explains the making of Deafvoir—a visual novel game about sign language—as well as why it is developed and how it can help the deaf and us learn sign language. By conducting a survey and asking how familiar sign language is to some people and their response about Deafvoir, we can move forward to realize this sign language learning visual novel video game and make it local-based before going international. With this video game, it is hoped to make Indonesia's younger generation, especially Gen Z, understand and be able to use sign language whenever they encounter the deaf in real life.
Feasibility Study for Implementation Biometrics for Online Transaction

It's a big challenge for businesses to increase security in the age of digital technologies, especially for online shops. In online shopping, hackers can penetrate networks and steal private information about customers. Biometrics are still not being used by online retailers in Indonesia as a form of payment security. In this essay, we'll talk about how biometrics might increase online transaction security and effectiveness.
File Encryption Application using Menezes-Vanstone Elliptic Curve Cryptography Based on Python

Humans have been affected by the advent of information technology in many different ways. Information can be sent freely as a result of the conveniences offered. However, unauthorized parties may take advantage of this to retrieve and utilize the information without the owner's consent. In this paper, the authors perform text, image, audio, and video data security using a modified version of the Menezes-Vanstone elliptic curve cryptography algorithm. The study implements Menezes-Vanstone Elliptic Curve Cryptography in a program for file text, image, audio, and video encryption and decryption, while also providing performance data for both our program and alternative approaches for comparison. The outcome demonstrates that the encrypted file size is smaller than alternative approaches. Nevertheless, our program might benefit from some enhancements in terms of encryption and decryption times.
Implementation of Augmented Reality in Study for Human Anatomy

Biology learning is often challenging for students due to it needs a lot of memory ability and visual ability to memorize all the methods. This research aims to develop and evaluate an Augmented Reality that is based of biology human anatomy such as human body (human body, human muscle, and human skeleton), lung, eye, ear, digestive system. AR-based technology has been recommended for use in education due to its excellent potential in helping to improve the understanding of the concept of a material to be better. Therefore, this research will develop and evaluate the use of augmented reality in biology learning about human anatomy. This research applies the ADDIE model by involving the stages of analysis, design, development, implementation, and evaluation. The study results show that the developed AR application can help students understand human anatomy and learn the description of human anatomy. Student feedback obtained through questionnaires indicated that they rated this application positively regarding ease of use, information conveyed properly, and effectiveness in supporting the learning process. The AR application developed in this study improves the quality and effectiveness of biology learning through innovative and interactive technology. Further research could focus on developing additional content and features and exploring the impact of this application on a broader group of students and in different areas of education.
The Effectiveness of Augmented Reality Using Flash Card in Education to Learn Simple English Words as a Secondary Language

Considering the potential and benefits of augmented reality and the development of education efficiency is essential, it is necessary to utilize the benefits and potential suitable to be implemented in the education field. One of the benefits is the fun learning experience compared to the traditional method. Furthermore, this study aims to analyze the effectiveness of using augmented reality in learning simple English words. The technology method is marker-based tracking, where the marker is flashcards that will be uploaded into Vuforia SDK Kit. Also, this research method uses ADDIE because it is an instructional design that makes the researcher easily to keep track of the progress and result. Then, the method is tested and analyzed using paired t-tests to measure its effectiveness and the User Experience Questionnaire to measure the user experience. Based on the analysis, the result shows that the result is effective, and the questionnaire has a good overall score. In conclusion, the result corresponds to the contribution of this research that enables the education learning experience to be effective, fun, and interactive.
Implementation of Handwriting Recognition and Answer Evaluation with Recurrent Neural Network

Many teachers “still utilize exams to gauge their pupils' knowledge. However, as the teacher must comprehend each answer provided by each student individually, analyzing the exam response can take a long time. Additionally, different handwriting styles may lead to errors in scoring the response, resulting in an erroneous mark. This study suggests a web-based program that can read students' handwriting to assist teachers in assessing students’ responses. The objective of this study was to develop a web-based application that can accept student responses as input and output correctness.
Initial Study of Batik Generation using Variational Autoencoder

One of the most promising architectures for generative models is the variational autoencoder (VAE). To reconstruct Batik patterns for this work, we used a deep convolutional VAE architecture. Reconstruction outcomes from various batik motifs are mapped and contrasted using some criteria. As another crucial component of the convolutional network, batch normalization's impact on the model's performance was also examined. The dataset is used to study some learned latent space features. Through these findings, we laid the framework for next research on Batik generation utilizing VAE.
Recommendation System For Selecting Residential Complexes Using The SAW Method In Decision Support

Housing is one of the main needs of society. Homes are selected based on the buyer's constraints, needs, and desires. The characteristics of housing for each family's needs and desires are different, so a selection process must be carried out. Constraints in collecting information and data for each house complex manually result in the process being less effective and efficient because it takes time and money. Several factors influence the choice of housing, namely the price of the residential unit, the area of the residence, the legality of the residence, the residential rooms, and the need for a clean water source. The SAW method is a data management method that can help buyers make decisions in choosing housing that suits their needs and desires. The SAW method will calculate the final preference value for each alternative house complex based on 20 alternative data samples for house complexes and 5 attributes. Recommendations for choosing a home complex are several options that can be adjusted or eliminated according to the buyer's constraints, needs, and desires. The results of the SAW method calculations can help and make it easier for buyers to make decisions in choosing housing from several recommended housing complexes. The SAW method can be applied in the housing selection process based on the buyer's constraints, needs, and desires. Using a decision support system can save time and minimize errors.
The Development of a Coliform Detection Game As A Part of Android – Based Virtual Food Safety Laboratory to Support Online Learning

For educators in particular, the implementation of online learning during the epidemic both potential and challenges. In order to correctly attain learning outcomes, creativity is required to ensure that online learning is effective and efficient. In order to get through those problems, having the necessary learning resources is undoubtedly highly beneficial. However, there are still few learning resources accessible in several competency areas, such as practical work in food safety. Food safety is a topic that students studying food packaging technology, culinary arts, catering, public health, and food technology must master. The goal of this research is to create a virtual food safety laboratory application that includes a coliform detection game. This game app is meant to aid in the process of learning about This research focuses on food security and uses the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model approach to implement the steps of the game development life cycle. The coliform detection game app was validated by two material specialists and two design and media experts. Application tests were conducted in groups of five individuals, involving 25 students in the third semester of the culinary arts program at Creative Media State Polytechnic. The average pre-test score obtained from these trials was 2.88, while the average post-test score at the same time was 9.28. The data clearly indicates that the coliform detection game application contributes to online learning as it significantly enhances student knowledge.
A MinMax Item-based Method for Multi-Criteria Recommendation Systems

One of the common challenges in multi-criteria recommendation systems is dealing with data normalization. The challenge occurs since criteria can have diverse rating ranges and user rating behaviors are dissimilar. Previous studies on data normalization showed the supremacy of the Decoupling technique in the user-based multi-criteria recommendation system and the MinMax technique in the multi-criteria decision-making system as well as in data mining. A study also showed that the performance of Decoupling is improved in the item-based method than in the user-based. However, no study has been conducted to investigate the performance of MinMax compared to Decoupling in the item-based multi-criteria recommendation system. This study aims to combine the MinMax normalization technique and the item-based modeling approach in a multi-criteria recommendation system. The proposed method is named the MinMax Item-based method (MIB). We conducted a series of experiments using the Yelp Hotel multi-criteria rating dataset to perform a sensitivity analysis of MIB. The best settings are then used to benchmark MIB MIB towards DCMItem, i.e., a method that combines the Decoupling normalization technique and item-based multi-criteria modeling approach. The comparison results show the outperformance of MIB towards DCMItem by 2.30% in Precision and 2.00% in NDCG. Therefore, we can conclude that MixMax is able to improve the performance of the item-based multi-criteria recommendation system better than Decoupling.
Spider Plus: A Text Classifier for Research Article Components

Uploading research articles to a database can be a complex process with multiple HTML fields. The complexity of this process makes users less efficient and productive. This study explored the use of the Multilayer Perceptron (MLP) algorithm using language-independent features (count and Boolean features) to create a multi-class text classifier that can classify 10 research article components (e.g., title, authors, abstract, etc.). The text classifier was developed to pave the way for a streamlined manual data entry process for uploading research articles to a database by implementing a single textarea in favor of using multiple HTML fields. The text samples were obtained from multiple sources using web scraping technology, consolidated, cleaned, and standardized. The 12 language-independent features were generated based on the textual formats of sample texts (e.g., count of capitalized letters, digits, punctuation, checking the existence of a URL pattern, etc.). Recursive feature elimination with cross validation (RFECV) was used to determine the optimal number of input features. The hyperparameter values of the model were determined through the grid search technique. A trial-and-error process was conducted to determine the number of hidden layers. The model achieved 95%, 94%, and 95% scores for micro, macro, and weighted average f1-scores, respectively. The model performed well in classifying research article components. However, it is sensitive to textual format (e.g., lower or upper case, punctuation used, etc.). For subsequent research in this area, this study recommends investigating the use of both language-dependent and language-independent features to address the limitations of the current model.
Path Planning Using the Bee Colony Algorithm to Find Routes for a Coffee Plantation Using Drones

The selling value of coffee depends on the level of maturity of the beans. The coffee plantation in Alam Indah village, Bandung district covers an area of 84 hectares or approximately 840,000 square meters, making it difficult for farmers to monitor the condition of each coffee plant individually. The objective of this paper is to provide a path planning using the bee colony algorithm to find the best routes for a coffee plantation. There are several monitoring techniques for coffee, one of which is using a drone equipped with image processing. In this research, path planning was carried out to determine the fastest route that a drone can take using the bee colony algorithm. The path planning was divided into two areas that have been adjusted to the operational capabilities of the DJI Phantom 4 drone type. The determination of the best route was carried out based on the neighborhood principle using the calculation of the highest fitness value. Each iteration entered the employed bee phase, onlooker bee phase, and scout bee phase. The result shows that there are 24 blocks that need to be passed through. Area 1 can be covered in 18.4 minutes and area 2 in 13.4 minutes at a speed of 30 km/h and a drone altitude of 10 meters. The fastest route in area 1 passes through blocks A-B-C-D-E-F-M-N-O-P-Q-L-K-G, while in area 2, the fastest route passes through blocks I-J-R-T-U-X-W-V-S-H.
Artificial Intelligence Based Automatic Live Stream Chat Machine Translator

In the world of live streaming, language barrier in the live chat remains an issue as people from any language can gather in one live stream, making it harder to understand what each other has said in the chat. The issue becomes more relevant as many major live streaming platforms still do not have automatic live stream chat translation as their feature. To tackle this issue, the author has built the deep learning models that can perform the automatic translation on the live stream chat's incoming English messages to Indonesian and outcoming Indonesian messages to English. These models follow the architecture of Seq2Seq model, an encoder for encoding the input and a decoder for predicting the translation. On both of the encoder and decoder, LSTM variant of the RNN is used to learn the important features of a sentence sequentially. Bidirectional language model allows the sentence to be processed in both directions. Global attention network helps the decoder in finding contextual information from the encoder for the prediction.
Topic Segmentation using Transformer Model for Indonesian Text

The increasing number of articles available in the digital library will require quite a long time and accuracy in sorting articles according to needs. The use of artificial neural networks in finding the right articles as needed through topic segmentation applications will helps this process in terms of speed and accuracy. Several neural network models used in segmentation topic applications include Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The Transformer model since its introduction in 2017 for Natural Language Processing (NLP) has a better level of accuracy compared to the RNN, CNN and LSTM models. Transformer model research for segmentation topic applications, especially articles in Indonesian language, is still very limited. This paper will discuss the use of the Transformer model in segmentation topic applications for Indonesian-language articles. The experimental results found that the accuracy produced by the Transformer model was higher than previous LSTM model with the WindowDiff value generated by the model proposed using Transformer is 0.249 and the LSTM baseline model is 0.363, while the while the Pk value generated by the proposed model is 0.279 and the LSTM baseline model is 0.394.
The Application of Augmented Reality to Generate Realistic Interaction in the Property Sector

Augmented Reality is a technology that overlays computer-generated information and images in a natural environment to enhance or augment the contextual perception of the user's environment. The utilization of AR is currently one of the most important in developing several industrial sectors, such as architecture, engineering, and construction development. Nowadays, the property sector industry has always been an issue of sale for users that do not have direct access to the property. The pandemic and the economic problem in different countries have also reduced consumer interest in purchasing houses. Due to these issues, this paper aims to provide a platform for addressing these pain areas. The software can apply Augmented Reality to real estate in real-time using a mobile device. Not only can they see the outside of the property, but they can also see the interior design, furniture design, and property mapping in 3D. Our test results show that an application is needed to provide the property sector with high demands. Over 80% of our respondents confirm that our application's presence will help ease the sales problem. Because it can save the customer time, viewing property frequency, and cost saving. These increased demands, we are developing an AR-based application for the market to use and gain more traction in the property sector.
Agile Software Development Effort Estimation based on Product Backlog Items

In all methods of Agile Software Development methods, an estimation process which determines the size of all Product Backlog Items (PBI) is essentially important. One of those methods is Planning Poker. This method is familiar among developers in the globe and one of the famous analogy-based estimation techniques known. In Planning Poker, the goal is to have a consensus vote on how big a product backlog should be. This consensus is reached via open discussion. As discussion is involved, many aspects of human errors could cause problems such as: anchoring effect and overconfidence which often leads to underestimation. To comprehend this, we offer an AI method that can be applied alongside the discussion to minimize this human error factors. We will be using NLP and ANN to classify a Product Backlog Item based on previous projects. The aim is individual estimate given by the AI that is free from influence from human to give better estimation. The result is expected at above 90% accuracy to give the best performance.